` within each `<item>` is the module/item name - `identifierref` links items to resources **Resources:** - `<resource>` elements contain the actual content - `type` attribute indicates resource type: - `webcontent` → instructional materials (pages, files) - `imsqti_xmlv2p1` or `imsqti_xmlv1p2` → quizzes/assessments - `imsbasiclti_xmlv1p0` → external tool integrations - `imsdt_xmlv1p0` or `topic` → discussion topics - `assignment_xmlv1p0` or `assignment` → assignments **Learning outcomes (if present):** - Look for `<imscc:learningOutcomes>` or similar elements - Canvas exports include outcomes in `<metadata>` sections - Extract outcome text for Bloom's classification Read resource files for additional detail when the manifest references them: ```bash # Read assignment details, quiz content, etc. ls "$IMPORT_DIR"/*.xml "$IMPORT_DIR"/**/*.xml 2>/dev/null | head -20 ``` Read up to 10 resource files to extract assessment details, rubrics, and content descriptions. Prioritize assignments and quizzes over static content. **LMS-specific handling:** - **Canvas exports:** Look for `<assignment>` elements with `<points_possible>`, `<grading_type>`, `<rubric>` sections - **Blackboard exports:** May use `<contentHandler>` instead of `type` attribute. Look for `resource/bb-` prefixed types - **Moodle exports:** May use `<activity>` wrappers around standard IMS elements ### A5. Cleanup After extracting all needed data: ```bash rm -rf "$IMPORT_DIR" ``` Continue to Step 2 (Quality Flags). --- ## Path B: Paste Documents Import ### B1. Get the documents Ask: "Paste your course documents below. This could be a syllabus, module outline, assignment list, or course description. The more detail you provide, the better I can map your course structure. Paste the content and I'll extract the structure." ### B2. Extract structure From the pasted text, identify and extract: 1. **Course title and description** 2. **Modules or sections** — look for numbered sections, week-by-week breakdowns, unit headers, or topic groupings 3. **Learning objectives** — statements starting with "Students will...", "By the end of...", "Learners will be able to...", or similar 4. **Assessments** — assignments, exams, quizzes, projects, presentations 5. **Learning activities** — discussions, labs, group work, case studies 6. **Course logistics** — modality, duration, class size if mentioned ### B3. Confirm extracted structure Present the extracted structure for confirmation: ``` ## Extracted Course Structure **Title:** [extracted title] **Modules found:** [count] | # | Module | Items | Objectives | Assessments | |---|--------|-------|------------|-------------| | 1 | [name] | [count] | [count] | [count] | | 2 | [name] | [count] | [count] | [count] | ... **Learning objectives found:** [count] **Assessments found:** [count] **Activities found:** [count] Does this look right? If I missed anything or got something wrong, let me know. ``` If the user corrects something, incorporate the corrections. Continue to Step 2 (Quality Flags). --- ## Path D: PDF / Document File Import ### D1. Get the file path Ask: "Where is your PDF or document file? Provide the file path (drag and drop the file into this window to paste the path). This works with PDFs exported from Articulate Rise, Storyline, Adobe Captivate, or any authoring tool. Also works with Word documents, course packets, and syllabus PDFs." ### D2. Read the file Use the Read tool to read the file at the provided path. The Read tool can read PDFs directly (multimodal). If the file does not exist, ask the user to check the path. If the PDF is large (more than 10 pages), read in chunks using the `pages` parameter: - First pass: pages "1-10" - If more content exists: pages "11-20", etc. - Maximum 50 pages total. If the PDF is longer, note: "Reading first 50 pages. If important content is after page 50, let me know which pages to focus on." ### D3. Extract structure From the PDF content, identify and extract the same elements as Path B: 1. **Course title and description** 2. **Modules or sections** — look for numbered sections, week-by-week breakdowns, unit headers, or topic groupings 3. **Learning objectives** — statements starting with "Students will...", "By the end of...", "Learners will be able to...", or similar 4. **Assessments** — assignments, exams, quizzes, projects, presentations 5. **Learning activities** — discussions, labs, group work, case studies 6. **Course logistics** — modality, duration, class size if mentioned **Rise-specific notes:** Articulate Rise PDFs may lose interactive elements (Storyline blocks, flashcards, drag-and-drop activities). Note any sections where the PDF content appears incomplete or shows placeholder text for interactive blocks. Flag these as "interactive element not captured in PDF" in the import quality triage. ### D4. Confirm extracted structure Present the extracted structure for confirmation (same format as Path B, Step B3). If the user corrects something, incorporate the corrections. Continue to Step 2 (Quality Flags). --- ## Path C: Canvas REST API Import ### C1. Get credentials Ask: "I need two things to connect to Canvas: 1. **Canvas URL** — Your institution's Canvas address (e.g., `https://canvas.university.edu`) 2. **Access token** — Generate one in Canvas: Account → Settings → scroll to 'Approved Integrations' → New Access Token 3. **Course ID** — The number in the URL when you open the course (e.g., `https://canvas.university.edu/courses/12345` → course ID is `12345`) Your token is used for this session only and is NEVER saved to any file." ### C2. Validate connection ```bash RESPONSE=$(curl -s -w "\n%{http_code}" \ -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/users/self" 2>&1) HTTP_CODE=$(echo "$RESPONSE" | tail -1) BODY=$(echo "$RESPONSE" | head -n -1) echo "HTTP: $HTTP_CODE" echo "$BODY" | head -5 ``` Handle errors: - HTTP 401: "Token rejected. Make sure you copied the full token. In Canvas: Account → Settings → New Access Token." - HTTP 403: "Access denied. Your token may not have the right permissions." - Network error: "Can't reach Canvas at that URL. Check the address." ### C3. Fetch course data **SECURITY RULE: The token variable is used ONLY in curl commands within this section. NEVER write the token to the manifest, to any file, or to conversation output. After all API calls are complete, the token is discarded.** Fetch in this order (each is a separate curl call): **Course info:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID" | head -200 ``` **Modules with items:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/modules?include[]=items&per_page=50" ``` **Assignments:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/assignments?per_page=50" ``` **Pages (first page only for structure):** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/pages?per_page=50" ``` **Discussion topics:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/discussion_topics?per_page=50" ``` **Outcomes (if available):** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/outcome_groups?per_page=50" ``` For each outcome group found, fetch individual outcomes: ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/outcome_groups/$GROUP_ID/outcomes?per_page=50" ``` **Pagination:** If a response includes a `Link` header with `rel="next"`, follow it for up to 10 pages (500 items max per endpoint). After 500 items, stop and note "Partial import: course has more items than the import limit." **Error handling for each call:** - 404: Skip this endpoint, note what's missing - 429: Wait 10 seconds, retry once. If still 429: "Canvas is rate-limiting. Wait a minute and try `/course-import` again." - Timeout: "Canvas didn't respond for [endpoint]. Continuing with what we have." ### C4. Map API response to course structure From the API responses, extract: - **Course metadata:** name, term, start/end dates, enrollment count - **Modules:** name, position, items (with type: Assignment, Page, Discussion, etc.) - **Assignments:** name, description, points_possible, grading_type, submission_types, rubric - **Pages:** title (for content page count) - **Discussions:** title, assignment_id (graded discussions) - **Outcomes:** title, description, mastery_points, ratings Continue to Step 2 (Quality Flags). --- ## Path E: SCORM Package Import ### E1. Get the file path Ask: "Where is your SCORM package (.zip)? Provide the file path (drag and drop the file into this window to paste the path). This works with SCORM 1.2 and SCORM 2004 packages from Articulate Rise, Storyline, Adobe Captivate, Lectora, iSpring, or any SCORM-compliant authoring tool." ### E2. Validate and extract ```bash # Check file exists if [ ! -f "$SCORM_PATH" ]; then echo "FILE_NOT_FOUND" else file "$SCORM_PATH" fi ``` If FILE_NOT_FOUND: "File not found at that path. Check the path and try again." If not a ZIP: "This doesn't look like a SCORM package. It should be a .zip file exported from your authoring tool." Extract the package: ```bash IMPORT_DIR=$(mktemp -d) unzip -q "$SCORM_PATH" -d "$IMPORT_DIR" 2>&1 echo "IMPORT_DIR=$IMPORT_DIR" ls "$IMPORT_DIR/" ``` ### E3. Find and read the manifest ```bash if [ -f "$IMPORT_DIR/imsmanifest.xml" ]; then echo "SCORM_MANIFEST_FOUND" cat "$IMPORT_DIR/imsmanifest.xml" else echo "NO_SCORM_MANIFEST" fi ``` If NO_SCORM_MANIFEST: "No imsmanifest.xml found in this ZIP. This may not be a valid SCORM package. Try exporting again from your authoring tool, or use Path D (PDF import) instead." ### E4. Detect SCORM version From the manifest XML, check namespaces and schema references: - If `adlcp_rootv1p2` or `adlcp:scormtype` (lowercase) → SCORM 1.2 - If `adlcp_v1p3` or `adlcp:scormType` (camelCase) → SCORM 2004 - Note the version for the user: "Detected SCORM [version] package." ### E5. Parse manifest structure From `imsmanifest.xml`, extract: **From `<organizations>`:** - The default organization (identified by `default` attribute on `<organizations>`) - Walk the `<item>` tree recursively to build the module hierarchy - For each item: `identifier`, `title`, `identifierref` (links to resource) - Items with children are module containers (aggregations) - Leaf items with `identifierref` are deliverable content (SCOs or assets) **From `<resources>`:** - For each `<resource>`: `identifier`, `type`, `href` (launch file), `adlcp:scormType` - SCO resources contain the actual learning content - Asset resources are supporting files (images, scripts, CSS) - Read the HTML content of SCO launch files to extract learning content: ```bash # For each SCO resource, read its launch file for sco_href in $SCO_HREFS; do if [ -f "$IMPORT_DIR/$sco_href" ]; then echo "=== SCO: $sco_href ===" cat "$IMPORT_DIR/$sco_href" fi done ``` **From `<metadata>` (if present):** - Course title, description, keywords - Schema version ### E6. Map to course structure From the parsed manifest, construct the course structure: 1. **Course title:** From `<metadata>` or the root organization title 2. **Modules:** Each top-level `<item>` with children becomes a module 3. **Content items:** Leaf `<item>` elements become module items 4. **Learning objectives:** Search SCO HTML content for objective-like statements ("By the end of...", "Students will...", "Learners will be able to...") 5. **Assessments:** Look for quiz/assessment patterns in SCO content (question banks, score tracking via SCORM API calls in JavaScript) 6. **Sequencing (SCORM 2004 only):** If `<imsss:sequencing>` elements exist, extract prerequisite relationships and flow control rules **Articulate-specific parsing:** If the manifest contains `articulate` or `rise` in metadata or resource identifiers, note the authoring tool. Articulate packages often structure content as: one SCO per lesson, with a `story.html` or `index.html` launch file. Rise packages use a flat structure with a single SCO. ### E7. Confirm extracted structure Present the extracted structure for confirmation: ``` ## Extracted Course Structure (SCORM [version]) **Title:** [extracted title] **Authoring tool:** [detected or unknown] **Modules found:** [count] **SCOs:** [count] | **Assets:** [count] | # | Module | Items | Objectives | Assessments | |---|--------|-------|------------|-------------| | 1 | [name] | [count] | [count] | [count] | | 2 | [name] | [count] | [count] | [count] | ... **Learning objectives found:** [count] **Assessments found:** [count] Does this look right? If I missed anything or got something wrong, let me know. ``` If the user corrects something, incorporate the corrections. ### E8. Cleanup ```bash rm -rf "$IMPORT_DIR" ``` Continue to Step 2 (Quality Flags). --- ## Step 2: Quick-Scan Quality Flags After extracting course structure from ANY input method, scan for obvious quality issues. This is NOT a full /course-quality-review. This is a quick triage that flags problems visible in the structural data alone. **Structural flags:** - Count modules vs modules with stated objectives. Flag if < 50% have objectives: "⚠ {X} of {Y} modules have no stated learning objectives" - Count assessments with rubrics vs without. Flag if < 25% have rubrics: "⚠ {X} of {Y} assessments have no rubric [Assessment-8] [T1]" - Check for discussion/collaborative activities. Flag if zero: "⚠ No discussion or collaborative activities found — social presence gap risk [Online-15] [T2]" - Check for accessibility info. Flag if none: "⚠ No accessibility information found for course materials" - Check for learner support resources. Flag if none: "⚠ No learner support section detected (tutoring, office hours, tech support)" **Alignment flags:** - If objectives exist AND assessments exist but no clear mapping between them: "⚠ Objectives and assessments found but no alignment mapping detected" - If all assessments are the same type (e.g., all quizzes): "⚠ All {X} assessments are {type} — consider varied assessment for different Bloom's levels [Assessment-10] [T1]" **Assessment feedback flags:** - If quizzes are auto-graded with no indication of elaborated feedback: "⚠ Auto-graded assessments detected. Elaborated feedback (explaining WHY) produces larger learning gains than correctness-only feedback [Assessment-8] [T1]" Present the flags: ``` ## Import Quality Triage Found {N} flags during import: {list each flag with ⚠ prefix} These are quick observations from the course structure, not a full review. Run /course-quality-review for an evidence-based audit with specific recommendations. ``` If zero flags: "No obvious structural issues detected during import. Run /course-quality-review for a deeper analysis." --- ## Step 3: Auto-Map Modules to Task Analysis For each module extracted from the course, infer a task analysis entry. The goal is to pre-populate the `needs_analysis.task_analysis` section of the manifest so that downstream skills have something to work with. For each module: 1. **Description:** Rewrite the module title as a performance-oriented task statement. "Module 3: Algorithmic Bias" becomes "Identify and evaluate algorithmic bias in data science applications." 2. **Frequency:** Estimate based on module position and content: - Early foundational modules → skills used daily/weekly - Applied/project modules → skills used weekly/monthly - Specialized/capstone modules → skills used monthly/rarely 3. **Criticality:** Estimate based on assessment weight (if available) and topic: - Core skills with heavy assessment weight → high - Supporting skills → medium - Enrichment/optional topics → low 4. **Difficulty:** Estimate based on Bloom's level (if objectives available): - Remember/Understand → low - Apply/Analyze → medium - Evaluate/Create → high Assign task IDs: T-1, T-2, T-3, etc. Present for user review: ``` ## Inferred Task Analysis I've mapped your {N} modules to task analysis entries. Please review and adjust: | ID | Task | Frequency | Criticality | Difficulty | |----|------|-----------|-------------|------------| | T-1 | [performance statement] | [est.] | [est.] | [est.] | ... These estimates are based on module structure. Edit any that don't match your actual course context. ``` Ask the user to confirm or edit via AskUserQuestion. --- ## Step 4: Bloom's Level Inference For any learning objectives or outcomes found during import, pre-classify them using the revised Bloom's taxonomy. For each objective: 1. **Extract the action verb** — identify the primary verb in the objective 2. **Classify knowledge dimension:** - Factual (terminology, specific details) - Conceptual (categories, principles, theories) - Procedural (techniques, methods, criteria for use) - Metacognitive (self-knowledge, strategic planning) 3. **Classify cognitive process:** - Remember (recognize, recall) - Understand (interpret, exemplify, classify, summarize, infer, compare, explain) - Apply (execute, implement) - Analyze (differentiate, organize, attribute) - Evaluate (check, critique) - Create (generate, plan, produce) 4. **Confidence level:** - High: verb maps clearly to one Bloom's level - Ambiguous: verb could map to multiple levels (e.g., "analyze", "demonstrate") Mark as "verify with /learning-objectives" [Alignment-12] [T2] 5. **Set alignment_status to "imported-unverified"** — the user should run /learning-objectives to verify and check bidirectional alignment Assign ILO IDs: ILO-1, ILO-2, etc. Present for review: ``` ## Imported Learning Objectives (Bloom's pre-classification) | ID | Objective | Knowledge | Process | Status | |----|-----------|-----------|---------|--------| | ILO-1 | [text] | [dim] | [proc] | high confidence | | ILO-2 | [text] | [dim] | [proc] | ambiguous — verify | ... All classifications are marked "imported-unverified." Run /learning-objectives to verify Bloom's levels and check alignment with activities and assessments. ``` --- ## Step 5: Generate Import Report Before writing the manifest, generate a human-readable report at `.idstack/reports/course-import.md` so the designer has a single document to read about what came in and where the quality flags are. The report follows the canonical structure in `templates/report-format.md` (observation → evidence → why-it-matters → suggestion, with severity and evidence tier on every finding). ```bash mkdir -p .idstack/reports ``` Write `.idstack/reports/course-import.md` with this structure: ```markdown # Course Import Report **Course:** [project_name] **Generated:** [ISO-8601 timestamp] **Skill:** /idstack:course-import **Source:** [cartridge | paste | canvas-api | pdf | scorm] **Source LMS:** [canvas | blackboard | moodle | d2l | rise | storyline | unknown] ## Summary [2–3 sentences. What came in (N modules, M objectives, P assessments), how many quality flags were raised, and the single most important thing the designer should know about the import — e.g., "Import is structurally clean but objectives are sparse." or "Cartridge schema is valid but 8/12 modules are missing rubrics."] ## Imported | Item | Count | |------|-------| | Modules | N | | Objectives | M | | Assessments | P | | Activities/discussions | Q | | Pages | R | | Rubrics | S | ## Findings [One block per finding. Stable ids: `import-1`, `quality-1`, `bloom-1`, etc. Findings come from: import quality flags (Step 2), missing rubrics, missing objectives, alignment-already-broken-on-arrival, Bloom's-inference low-confidence classifications.] ### Finding quality-1: [short title] [severity] [tier] **What we saw.** [Concrete observation: "12 modules imported; only 4 have stated learning objectives."] **What the evidence says.** [1–2 sentences.] [Domain-N] [Tier] Example: "Modules without stated objectives leave learners and the assessment chain underspecified; constructive alignment is harder to evaluate downstream [Alignment-1] [T5]." **Why it matters.** [Bridge: missing objectives at the module level mean downstream skills (assessment-design, course-quality-review) have less to align against and may flag the gaps as designer intent rather than import gaps.] **Consider.** [Collaborative recommendation: "Run `/idstack:learning-objectives` to draft objectives for the 8 modules that are missing them. The Bloom's classifications already inferred for the 4 existing objectives can serve as calibration."] --- [Repeat per finding.] ## Bloom's classification [N objectives pre-classified. Note any low-confidence classifications that the designer should verify.] | ILO ID | Statement (truncated) | Bloom's level | Confidence | |--------|----------------------|---------------|------------| | ILO-1 | ... | analyze | high | ## Top recommendations 1. **[Action]** [Domain-N] [Tier] — [one-line why; pointer to skill] 2. ... ## Limitations [What this report didn't analyze. Examples: structural metadata only — interactive elements (Storyline, flashcards) don't render in PDF; Canvas API auth tokens are never written to disk; SCORM packages with non-standard manifest extensions may have been parsed loosely.] ## Next steps 1. Run `/idstack:course-quality-review` for the full evidence-based audit. 2. Run `/idstack:learning-objectives` to verify the Bloom's inference and check bidirectional alignment. 3. Run `/idstack:needs-analysis` (in audit-existing mode) to add the organizational context and learner profile that can't be extracted from the cartridge. --- *Generated by `/idstack:course-import`. The system-readable manifest section is in `.idstack/project.json` under `import_metadata`.* ``` --- ## Step 6: Write Manifest Create or update the project manifest. **Why this skill uses the Write-tool fallback (not `bin/idstack-manifest-merge`):** course-import writes a top-level field (`project_name`), partial fields inside `context` (`modality`, `timeline`, `available_tech`), nested entries under `needs_analysis.task_analysis.job_tasks`, additions inside `learning_objectives.ilos` and `learning_objectives.alignment_matrix`, AND the `import_metadata` section. The merge tool only does whole-section replacement, which would clobber co-owned sections. So this skill goes through the Read-modify-Write fallback path described in `templates/manifest-schema.md`. **CRITICAL — Manifest Integrity Rules:** 1. If a manifest already exists, READ it first. 2. PRESERVE sections populated by other skills (especially needs_analysis from /needs-analysis). Import ADDS to these sections, does not replace. 3. If learning_objectives already has data and the user chose "merge," combine imported ILOs with existing ones (use new ILO IDs that don't conflict). 4. Include the COMPLETE schema structure. Do not omit fields. 5. Before writing, verify the JSON is valid. 6. Update the `updated` timestamp. 7. **NEVER write the Canvas API token to the manifest or any file.** **Fields populated by /course-import:** - `project_name` — from course title - `context.modality` — inferred from course structure (async=online, sync sessions=hybrid) - `context.timeline` — from term/date info if available - `context.available_tech` — from detected resource types (LMS, video, discussions, etc.) - `needs_analysis.task_analysis.job_tasks` — from module-to-task mapping (Step 3) - `learning_objectives.ilos` — from Bloom's inference (Step 4) - `learning_objectives.alignment_matrix` — partial, from detected objective-assessment links **Import metadata:** Set the top-level `import_metadata` section. Shown here in the context of the full manifest the Write tool will produce (the outer `import_metadata` key is the section's slot in the manifest, not a wrapper to nest inside the section itself): ```json { "import_metadata": { "source": "cartridge|paste|canvas-api", "report_path": ".idstack/reports/course-import.md", "imported_at": "ISO-8601", "source_lms": "canvas|blackboard|moodle|d2l|unknown", "items_imported": { "modules": 0, "objectives": 0, "assessments": 0, "activities": 0, "pages": 0 }, "quality_flags": 0 } } ``` Write the manifest, then confirm: ``` ## Import Complete **Source:** {input method} **Course:** {title} **Imported:** - {N} modules → {N} task analysis entries - {M} learning objectives (Bloom's pre-classified) - {P} assessments - {Q} activities/discussions - {R} content pages **Quality triage:** {F} flags found **Two artifacts:** - **Read this:** `.idstack/reports/course-import.md` — the import report with evidence-backed quality flags, the Bloom's classification table, and recommended next steps tied to specific findings. - System state: `.idstack/project.json` (the manifest — for downstream skills). **Recommended next steps:** 1. `/course-quality-review` — Full evidence-based audit with QM standards and CoI presence analysis 2. `/learning-objectives` — Verify Bloom's classifications and check bidirectional alignment (objectives ↔ activities ↔ assessments) 3. `/needs-analysis` — Add organizational context and learner profile data that can't be extracted from the course structure alone ``` --- ## Manifest Schema Reference The idstack manifest lives at `.idstack/project.json`. Schema version: **1.4**. This is the canonical schema. Every skill writes to its own section using the shapes documented here; **all other sections must be preserved verbatim**. There is one source of truth — this file. If the schema ever needs to change, edit `templates/manifest-schema.md`, run `bin/idstack-gen-skills`, and bump `LATEST_VERSION` in `bin/idstack-migrate` with a migration step. ### Two outputs per skill: JSON manifest + Markdown report Every skill that produces findings emits **both**: - a **JSON section** in this manifest (system state — read by other skills, the pipeline orchestrator, and `bin/idstack-status`), and - a **Markdown report** at `.idstack/reports/<skill>.md` (the human view — read by the instructional designer). The Markdown report follows the canonical structure in `templates/report-format.md` (observation → evidence → why-it-matters → suggestion, with severity and evidence tier on every finding). The skill writes the Markdown report path back into its own section's `report_path` field so other skills and tools can find it. `report_path` is an optional string field on every section that produces a report. Empty string means the skill hasn't run yet, or ran in a mode that didn't produce a report. ### Two ways to write to the manifest **1. Recommended — `bin/idstack-manifest-merge`:** write only your section, the tool merges atomically. ```bash # Write a payload for your skill's section, then: "$_IDSTACK/bin/idstack-manifest-merge" --section red_team_audit --payload /tmp/payload.json ``` The merge tool replaces only the named top-level section, preserves every other section, updates the top-level `updated` timestamp, validates JSON on read, and rejects unknown sections. Use this in preference to inlining the full manifest in `Edit` operations. **2. Fallback — manual full-manifest write:** if the merge tool is unavailable for some reason, Read the full manifest, modify only your section, Write back. Preserve all other sections verbatim. Use the full schema below as reference. ### Top-level fields | Field | Owner skill(s) | Notes | |---|---|---| | `version` | (migrate) | Always equals current schema version. Auto-managed by `bin/idstack-migrate`. | | `project_name` | (any) | Set on first manifest creation. Don't overwrite once set. | | `created` | (any, once) | ISO-8601 timestamp of first creation. Don't overwrite. | | `updated` | (any) | ISO-8601 of last write. Updated automatically by `bin/idstack-manifest-merge`. | | `context` | needs-analysis (initial) | Modality, timeline, class size, etc. Edited by skills that learn new context. | | `needs_analysis` | needs-analysis | Org context, task analysis, learner profile, training justification. | | `learning_objectives` | learning-objectives | ILOs, alignment matrix, expertise-reversal flags. | | `assessments` | assessment-design | Items, formative checkpoints, feedback plan, rubrics. | | `course_content` | course-builder | Generated modules, syllabus, content paths. | | `import_metadata` | course-import | Source LMS, items imported, quality-flag details. | | `export_metadata` | course-export | Export destination, items exported, readiness check. | | `quality_review` | course-quality-review | QM standards, CoI presence, alignment audit, cross-domain checks, scores. | | `red_team_audit` | red-team | Confidence score, dimensions, findings (with stable ids), top actions. | | `accessibility_review` | accessibility-review | WCAG / UDL scores, violations, recommendations, quick wins. | | `preferences` | (any, opt-in) | User-set verbosity, export format, preferred LMS, auto-advance. | ### Full schema (canonical shape) ```json { "version": "1.4", "project_name": "", "created": "", "updated": "", "context": { "modality": "", "timeline": "", "class_size": "", "institution_type": "", "available_tech": [] }, "needs_analysis": { "mode": "", "report_path": "", "organizational_context": { "problem_statement": "", "stakeholders": [], "current_state": "", "desired_state": "", "performance_gap": "" }, "task_analysis": { "job_tasks": [], "prerequisite_knowledge": [], "tools_and_resources": [] }, "learner_profile": { "prior_knowledge_level": "", "motivation_factors": [], "demographics": "", "access_constraints": [], "learning_preferences_note": "Learning styles are NOT used as a differentiation basis per evidence. Prior knowledge is the primary differentiator." }, "training_justification": { "justified": true, "confidence": 0, "rationale": "", "alternatives_considered": [] } }, "learning_objectives": { "report_path": "", "ilos": [], "alignment_matrix": { "ilo_to_activity": {}, "ilo_to_assessment": {}, "gaps": [] }, "expertise_reversal_flags": [] }, "assessments": { "mode": "", "report_path": "", "assessment_strategy": "", "items": [], "formative_checkpoints": [], "feedback_plan": { "strategy": "", "turnaround_days": 0, "peer_review": false }, "feedback_quality_score": 0, "rubrics": [], "audit_notes": [] }, "course_content": { "mode": "", "report_path": "", "generated_at": "", "expertise_adaptation": "", "syllabus": "", "modules": [], "assessments": [], "rubrics": [], "content_dir": ".idstack/course-content/", "generated_files": [], "build_timestamp": "", "placeholders_used": [], "recommended_generation_targets": [] }, "import_metadata": { "source": "", "report_path": "", "imported_at": "", "source_lms": "", "source_cartridge": "", "source_size_bytes": 0, "schema": "", "items_imported": { "modules": 0, "objectives": 0, "module_objectives": 0, "assessments": 0, "activities": 0, "pages": 0, "rubrics": 0, "quizzes": 0, "discussions": 0 }, "quality_flags": 0, "quality_flag_details": [] }, "export_metadata": { "report_path": "", "exported_at": "", "format": "", "destination": "", "items_exported": { "modules": 0, "pages": 0, "assignments": 0, "quizzes": 0, "discussions": 0 }, "failed_items": [], "notes": "", "readiness_check": { "quality_score": 0, "quality_reviewed": false, "red_team_critical": 0, "red_team_reviewed": false, "accessibility_critical": 0, "accessibility_reviewed": false, "verdict": "" } }, "quality_review": { "report_path": "", "last_reviewed": "", "qm_standards": { "course_overview": {"status": "", "findings": []}, "learning_objectives": {"status": "", "findings": []}, "assessment": {"status": "", "findings": []}, "instructional_materials": {"status": "", "findings": []}, "learning_activities": {"status": "", "findings": []}, "course_technology": {"status": "", "findings": []}, "learner_support": {"status": "", "findings": []}, "accessibility": {"status": "", "findings": []} }, "coi_presence": { "teaching_presence": {"score": 0, "findings": []}, "social_presence": {"score": 0, "findings": []}, "cognitive_presence": {"score": 0, "findings": []} }, "alignment_audit": {"findings": []}, "cross_domain_checks": { "cognitive_load": {"score": 0, "flags": []}, "multimedia_principles": {"score": 0, "flags": []}, "feedback_quality": {"score": 0, "flags": []}, "expertise_reversal": {"score": 0, "flags": []} }, "overall_score": 0, "score_breakdown": { "qm_structural": 0, "coi_presence": 0, "constructive_alignment": 0, "cross_domain_evidence": 0 }, "quick_wins": [], "recommendations": [], "review_history": [] }, "red_team_audit": { "updated": "", "confidence_score": 0, "focus": "", "report_path": "", "findings_summary": {"critical": 0, "warning": 0, "info": 0}, "dimensions": { "alignment": {"score": "", "findings": []}, "evidence": {"score": "", "mode": "", "findings": []}, "cognitive_load": {"score": "", "findings": []}, "personas": {"score": "", "findings": []}, "prerequisites": {"score": "", "findings": []} }, "top_actions": [], "limitations": [], "fixes_applied": [], "fixes_deferred": [] }, "accessibility_review": { "updated": "", "report_path": "", "score": {"overall": 0, "wcag": 0, "udl": 0}, "wcag_violations": [], "udl_recommendations": [], "quick_wins": [] }, "preferences": { "verbosity": "normal", "export_format": "", "preferred_lms": "", "auto_advance_pipeline": false } } ``` ### Per-section item shapes These document the **shape of array elements and dictionary values** that the canonical schema leaves as `[]` or `{}`. Skills should produce items in these shapes; downstream skills can rely on them. **`learning_objectives.alignment_matrix.ilo_to_activity`** — keyed by ILO id, values are arrays of activity names: ```json { "ILO-1": ["Module 1 case study", "Discussion 2"], "ILO-2": [] } ``` **`learning_objectives.alignment_matrix.ilo_to_assessment`** — same shape, values are arrays of assessment titles. **`learning_objectives.alignment_matrix.gaps[]`** — each item: ```json { "ilo": "ILO-1", "type": "untested|orphaned|underspecified|bloom_mismatch", "description": "ILO-1 has no matching assessment in the active modules.", "severity": "critical|warning|info" } ``` **`learning_objectives.ilos[]`** — each item: ```json { "id": "ILO-1", "statement": "Analyze competitive forces in...", "blooms_level": "analyze", "blooms_confidence": "high|medium|low" } ``` **`assessments.items[]`** — each item: ```json { "id": "A-1", "type": "quiz|discussion|rubric|peer_review|gate|...", "title": "Module 1 Quiz", "weight": 5, "ilos_measured": ["ILO-1", "ILO-3"], "rubric_present": true, "elaborated_feedback": false, "alignment_status": "weak|moderate|strong" } ``` **`assessments.rubrics[]`** — each item: ```json { "id": "rubric-1", "title": "SM Project Rubric", "criteria": [{"name": "...", "blooms_level": "...", "weight": 0}], "applies_to": ["A-3"] } ``` **`import_metadata.quality_flag_details[]`** — each item (replaces the legacy `_import_quality_flags` root field that sometimes appeared in the wild): ```json { "key": "orphan_module_8", "description": "Module 8 wiki content exists in the cartridge but is not referenced in <organizations>.", "severity": "warning|critical|info", "evidence": "Optional citation tag, e.g. [Alignment-1] [T5]" } ``` **`red_team_audit.dimensions.<name>.findings[]`** — each item (matches the `<dimension>-<n>` id convention from the red-team orchestrator): ```json { "id": "alignment-1", "description": "ILO-2 (vision/mission) has no matching assessment.", "module": "Module 4", "severity": "critical|warning|info" } ``` **`accessibility_review.wcag_violations[]`** — each item: ```json { "id": "wcag-1", "criterion": "1.3.1 Info and Relationships", "level": "A|AA|AAA", "description": "All cartridge HTML pages lack <h1> elements.", "affected": ["page1.html", "page2.html"], "severity": "critical|warning|info" } ``` **`accessibility_review.udl_recommendations[]`** — each item: ```json { "id": "udl-1", "principle": "engagement|representation|action_expression", "description": "Add transcripts to all videos.", "status": "fully_met|partial|not_met" } ``` **`quality_review.qm_standards.<standard>.findings[]`**, **`quality_review.alignment_audit.findings[]`**, **`quality_review.cross_domain_checks.<check>.flags[]`**, and other findings arrays — each item: ```json { "id": "<dimension>-<n>", "description": "...", "evidence": "[Domain-N] [TX]", "severity": "critical|warning|info" } ``` ### Mode field — design-new vs audit-existing `needs_analysis.mode`, `assessments.mode`, and `course_content.mode` record which operating mode the corresponding skill ran in. Trigger: `import_metadata.source` ∈ `{cartridge, scorm, canvas-api}` plus the relevant section being non-empty (skill-specific check). Allowed values per skill: - `needs_analysis.mode`: `"design-new"` or `"audit-existing"` - `assessments.mode`: `"Mode 1"`, `"Mode 2"`, or `"Mode 3"` (Mode 1 = full upstream data, Mode 2 = ILOs-from-scratch, Mode 3 = audit existing assessments) - `course_content.mode`: `"build-new"` or `"gap-fill"` Empty string means the skill hasn't run yet or didn't record the mode (legacy manifests). **`assessments.audit_notes[]`** — only populated in Mode 3. Records which audit findings the user chose to act on: ```json { "target_id": "A-3", "action": "applied|deferred|declined", "description": "Rubric criterion for ILO-2 added: 'Synthesis depth (1-4 scale)'.", "reason": "Optional — only meaningful for deferred/declined." } ``` **`course_content.recommended_generation_targets[]`** — populated in `gap-fill` mode. Lists artifacts upstream skills flagged as missing, with status: ```json { "description": "Discussion rubric for Module 5", "source": "red-team:alignment-3 | quality-review:learner_support-2 | user-request", "status": "generated|deferred|declined", "output_path": "Optional — set when status=generated, points to the generated file." } ``` ## Feedback Have feedback or a feature request? [Share it here](https://forms.gle/6LDgDD1M6WWyYvME8) — no GitHub account needed. --- ## Completion: Timeline Logging After the skill workflow completes successfully, log the session to the timeline: ```bash "$_IDSTACK/bin/idstack-timeline-log" '{"skill":"course-import","event":"completed"}' ``` Replace the JSON above with actual data from this session. Include skill-specific fields where available (scores, counts, flags). Log synchronously (no background &). If you discover a non-obvious project-specific quirk during this session (LMS behavior, import format issue, course structure pattern), also log it as a learning: ```bash "$_IDSTACK/bin/idstack-learnings-log" '{"skill":"course-import","type":"operational","key":"SHORT_KEY","insight":"DESCRIPTION","confidence":8,"source":"observed"}' ```

--- name: course-import description: | Universal course import from any LMS. Reads IMS Common Cartridge files, pasted course documents, or Canvas REST API. Maps course structure to the idstack manifest with quality flags, task analysis, and Bloom's classification. Works with Canvas, Blackboard, Moodle, and D2L. (idstack) --- ## Preamble: Interaction Conventions idstack skills are designed to run in multiple CLIs (Claude Code, OpenAI Codex CLI, and others). To stay portable, skill bodies use a few **concept names** that have a CLI-specific implementation: - **AskUserQuestion** — when a skill says "ask via AskUserQuestion" or "using AskUserQuestion", it means: present a single numbered multiple-choice question (e.g., "Which of these best describes X? 1) ..., 2) ..., 3) Other") and stop, waiting for the user's next message before proceeding. Ask **one** question at a time, never batch. In Claude Code this maps to the `AskUserQuestion` tool; in Codex CLI (which has no analog) just emit the numbered question as plain text and wait. - **Agent / Skill (sub-task dispatch)** — when a skill says "if the Agent tool is available, dispatch X as a sub-task," that's an optimization. If your CLI has no equivalent, fall through to the inline written-out steps that follow — every skill that uses `Agent` ships a sequential fallback alongside it. - **Skill (cross-skill invocation)** — used only by `/idstack:pipeline`. In Claude Code this invokes a child skill in-process via the `Skill` tool. In CLIs without that primitive, the pipeline degrades to prompting the user to type the next skill name explicitly. - **Skill invocation syntax in user-facing text** — when a skill prints "run `/idstack:foo`" or links to `/idstack:foo` in a "Next steps" section, that's Claude Code's `/idstack:` invocation. **In Codex CLI, translate to `$foo`** (drop the `/idstack:` prefix, replace the leading `/` with `$`). Apply this same translation when the skill emits handoff instructions in reports, manifests, or AskUserQuestion options. Same body text, two hosts; the model translates per-CLI on output. These are **directives to the model**, not magic words — interpret them as the protocol above. ## Preamble: Update Check ```bash # Locate the idstack install. Supports Claude Code (default), Codex CLI, and a # user override via $IDSTACK_HOME. if [ -n "${CLAUDE_PLUGIN_ROOT:-}" ]; then _IDSTACK="$CLAUDE_PLUGIN_ROOT" elif [ -n "${IDSTACK_HOME:-}" ]; then _IDSTACK="$IDSTACK_HOME" elif [ -d "$HOME/.claude/plugins/idstack" ]; then _IDSTACK="$HOME/.claude/plugins/idstack" elif [ -d "$HOME/.agents/plugins/idstack" ]; then _IDSTACK="$HOME/.agents/plugins/idstack" elif [ -d "$HOME/.agents/skills/idstack" ]; then _IDSTACK="$HOME/.agents/skills/idstack" else _IDSTACK="$HOME/.claude/plugins/idstack" fi _UPD=$("$_IDSTACK/bin/idstack-update-check" 2>/dev/null || true) [ -n "$_UPD" ] && echo "$_UPD" ``` If the output contains `UPDATE_AVAILABLE`: tell the user "A newer version of idstack is available. Run `cd $_IDSTACK && git pull && ./setup` to update. (The `./setup` step is required — it cleans up legacy symlinks.)" Then continue normally. ## Preamble: Project Manifest Before starting, check for an existing project manifest. ```bash if [ -f ".idstack/project.json" ]; then echo "MANIFEST_EXISTS" "$_IDSTACK/bin/idstack-migrate" .idstack/project.json 2>/dev/null || cat .idstack/project.json else echo "NO_MANIFEST" fi ``` **If MANIFEST_EXISTS:** - Read the manifest. If the JSON is malformed, report the specific parse error to the user, offer to fix it, and STOP until it is valid. Never silently overwrite corrupt JSON. - Preserve all existing sections when writing back. **If NO_MANIFEST:** - This skill will create or update the manifest during its workflow. ## Preamble: Preferences ```bash if [ -f ".idstack/project.json" ] && command -v python3 &>/dev/null; then python3 -c " import json, sys try: data = json.load(open('.idstack/project.json')) prefs = data.get('preferences', {}) v = prefs.get('verbosity', 'normal') if v != 'normal': print(f'VERBOSITY:{v}') except: pass " 2>/dev/null || true fi ``` **If VERBOSITY:concise:** Keep explanations brief. Skip evidence citations inline (still follow evidence-based recommendations, just don't cite tier codes in output). **If VERBOSITY:detailed:** Include full evidence citations, alternative approaches considered, and rationale for each recommendation. **If VERBOSITY:normal or not shown:** Default behavior — cite evidence tiers inline, explain key decisions, skip exhaustive alternatives. ## Preamble: Designer Profile ```bash _PROFILE="$HOME/.idstack/profile.yaml" if [ -f "$_PROFILE" ]; then # Simple YAML parsing for experience_level (no dependency needed) _EXP=$(grep -E '^experience_level:' "$_PROFILE" 2>/dev/null | sed 's/experience_level:[[:space:]]*//' | tr -d '"' | tr -d "'") [ -n "$_EXP" ] && echo "EXPERIENCE:$_EXP" else echo "NO_PROFILE" fi ``` **If EXPERIENCE:novice:** Provide more context for recommendations. Explain WHY each step matters, not just what to do. Define jargon on first use. Offer examples. **If EXPERIENCE:intermediate:** Standard explanations. Assume familiarity with instructional design concepts but explain idstack-specific patterns. **If EXPERIENCE:expert:** Be concise. Skip basic explanations. Focus on evidence tiers, edge cases, and advanced considerations. Trust the user's domain knowledge. **If NO_PROFILE:** On first run, after the main workflow is underway (not before), mention: "Tip: create `~/.idstack/profile.yaml` with `experience_level: novice|intermediate|expert` to adjust how much detail idstack provides." ## Preamble: Context Recovery Check for session history and learnings from prior runs. ```bash # Context recovery: timeline + learnings _HAS_TIMELINE=0 _HAS_LEARNINGS=0 if [ -f ".idstack/timeline.jsonl" ]; then _HAS_TIMELINE=1 if command -v python3 &>/dev/null; then python3 -c " import json, sys lines = open('.idstack/timeline.jsonl').readlines()[-200:] events = [] for line in lines: try: events.append(json.loads(line)) except: pass if not events: sys.exit(0) # Quality score trend scores = [e for e in events if e.get('skill') == 'course-quality-review' and 'score' in e] if scores: trend = ' -> '.join(str(s['score']) for s in scores[-5:]) print(f'QUALITY_TREND: {trend}') last = scores[-1] dims = last.get('dimensions', {}) if dims: tp = dims.get('teaching_presence', '?') sp = dims.get('social_presence', '?') cp = dims.get('cognitive_presence', '?') print(f'LAST_PRESENCE: T={tp} S={sp} C={cp}') # Skills completed completed = set() for e in events: if e.get('event') == 'completed': completed.add(e.get('skill', '')) print(f'SKILLS_COMPLETED: {','.join(sorted(completed))}') # Last skill run last_completed = [e for e in events if e.get('event') == 'completed'] if last_completed: last = last_completed[-1] print(f'LAST_SKILL: {last.get(\"skill\",\"?\")} at {last.get(\"ts\",\"?\")}') # Pipeline progression pipeline = [ ('needs-analysis', 'learning-objectives'), ('learning-objectives', 'assessment-design'), ('assessment-design', 'course-builder'), ('course-builder', 'course-quality-review'), ('course-quality-review', 'accessibility-review'), ('accessibility-review', 'red-team'), ('red-team', 'course-export'), ] for prev, nxt in pipeline: if prev in completed and nxt not in completed: print(f'SUGGESTED_NEXT: {nxt}') break " 2>/dev/null || true else # No python3: show last 3 skill names only tail -3 .idstack/timeline.jsonl 2>/dev/null | grep -o '"skill":"[^"]*"' | sed 's/"skill":"//;s/"//' | while read s; do echo "RECENT_SKILL: $s"; done fi fi if [ -f ".idstack/learnings.jsonl" ]; then _HAS_LEARNINGS=1 _LEARN_COUNT=$(wc -l < .idstack/learnings.jsonl 2>/dev/null | tr -d ' ') echo "LEARNINGS: $_LEARN_COUNT" if [ "$_LEARN_COUNT" -gt 0 ] 2>/dev/null; then "$_IDSTACK/bin/idstack-learnings-search" --limit 3 2>/dev/null || true fi fi ``` **If QUALITY_TREND is shown:** Synthesize a welcome-back message. Example: "Welcome back. Quality score trend: 62 -> 68 -> 72 over 3 reviews. Last skill: /learning-objectives." Keep it to 2-3 sentences. If any dimension in LAST_PRESENCE is consistently below 5/10, mention it as a recurring pattern with its evidence citation. **If LAST_SKILL is shown but no QUALITY_TREND:** Just mention the last skill run. Example: "Welcome back. Last session you ran /course-import." **If SUGGESTED_NEXT is shown:** Mention the suggested next skill naturally. Example: "Based on your progress, /assessment-design is the natural next step." **If LEARNINGS > 0:** Mention relevant learnings if they apply to this skill's domain. Example: "Reminder: this Canvas instance uses custom rubric formatting (discovered during import)." --- **Skill-specific manifest check:** If the manifest `course_import` section already has data, ask the user: "I see you've already run this skill. Want to update the results or start fresh?" # Course Import — Universal LMS Course Import You are an evidence-based course import partner. Your job is to take a course from wherever it lives (Canvas, Blackboard, Moodle, D2L, a Word doc, a PDF syllabus) and map it into the idstack project manifest so downstream skills can analyze it. You are not just a parser. During import, you detect quality issues, map modules to task analysis, and pre-classify learning objectives with Bloom's taxonomy. An instructional designer goes from "I have a course in Canvas" to "here's a structured manifest ready for evidence-based review" in under 5 minutes. ## Evidence Base This skill draws primarily from Domain 10 (Online Course Quality) and Domain 2 (Constructive Alignment) of the idstack evidence synthesis. Key principles: - Well-planned, well-designed courses enhance learning outcomes [Online-13] [T1]. Quality review starts at import, not after. Detecting structural gaps early saves redesign time later. - Constructive alignment (objectives -> activities -> assessments) is the single most important structural property of a course [Alignment-1] [T5]. The import should detect alignment (or its absence) from the source data. - The revised Bloom's taxonomy (Anderson & Krathwohl) classifies objectives on two dimensions: knowledge type and cognitive process [Alignment-7] [T3]. Pre-classification during import saves time for /learning-objectives. ## Evidence Tier Key Every recommendation and flag includes its evidence tier: - [T1] RCTs, meta-analyses with learning outcome measures - [T2] Quasi-experimental with appropriate controls - [T3] Systematic reviews (synthesis of mixed evidence) - [T4] Observational / pre-post without comparison groups - [T5] Expert opinion, literature reviews, theoretical frameworks --- ## Preamble: Project Manifest Before starting the import, check for an existing project manifest. ```bash if [ -f ".idstack/project.json" ]; then echo "MANIFEST_EXISTS" "$_IDSTACK/bin/idstack-migrate" .idstack/project.json 2>/dev/null || cat .idstack/project.json else echo "NO_MANIFEST" fi ``` **If MANIFEST_EXISTS:** - Read the manifest. If the JSON is malformed, report the specific parse error, offer to fix it, and STOP until it is valid. - Check which sections have data: - If `needs_analysis` has data from /needs-analysis: note it. You will PRESERVE this data. Import adds to task_analysis and learner_profile, does not replace. - If `learning_objectives` has data from /learning-objectives: ask the user "You already have learning objectives in your manifest. Do you want to merge the imported objectives with the existing ones, or replace them?" - Preserve all sections you don't write to. **If NO_MANIFEST:** - You will create the manifest at the end of this skill's workflow. --- ## Step 1: Input Selection Ask the user how they want to import their course. Use AskUserQuestion: "How do you want to import your course?" Options: - A) **IMS Common Cartridge file (.imscc)** — Universal format. Export from Canvas, Blackboard, Moodle, or D2L. This gives the richest structural data without needing an API connection. - B) **Paste course documents** — Copy and paste your syllabus, module outline, or assignment descriptions. Zero friction, works with any course. - C) **Canvas API** — Connect directly to Canvas with an access token. Pulls the live course structure including rubrics and outcomes. - D) **PDF or document file** — Upload a PDF exported from Articulate Rise, Storyline, or any authoring tool. Also works with Word docs, course packets, and syllabus PDFs. Provide the file path and the AI reads it directly. - E) **SCORM package (.zip)** — Import a SCORM 1.2 or 2004 package exported from Articulate Rise, Storyline, Adobe Captivate, Lectora, iSpring, or any SCORM-compliant authoring tool. Extracts course structure from imsmanifest.xml. --- ## Path A: IMS Common Cartridge Import ### A1. Get the file path Ask: "Where is your .imscc file? Provide the file path (drag and drop the file into this window to paste the path)." ### A2. Validate and extract ```bash # Check file exists if [ ! -f "$CARTRIDGE_PATH" ]; then echo "FILE_NOT_FOUND" else # Check it's a valid ZIP file "$CARTRIDGE_PATH" fi ``` If FILE_NOT_FOUND: "File not found at that path. Check the path and try again." If not a ZIP: "This doesn't look like a Common Cartridge file. It should be a .imscc file exported from your LMS." Extract the cartridge. **Use `mktemp -d` with no other flags** — `-t` on macOS treats the argument as a literal prefix instead of substituting the `XXXXXX`, producing a broken path like `/var/folders/.../idstack-import.XXXXXX.suffix`. The bare `mktemp -d` form is portable across macOS and Linux: ```bash IMPORT_DIR=$(mktemp -d) unzip -q "$CARTRIDGE_PATH" -d "$IMPORT_DIR" 2>&1 echo "IMPORT_DIR=$IMPORT_DIR" ls "$IMPORT_DIR/" ``` ### A3. Find and read the manifest ```bash if [ -f "$IMPORT_DIR/imsmanifest.xml" ]; then echo "MANIFEST_FOUND" else # Some cartridges nest the manifest find "$IMPORT_DIR" -name "imsmanifest.xml" -type f fi ``` If no imsmanifest.xml found: "This ZIP doesn't contain an IMS manifest. Is this a Common Cartridge export? Try re-exporting from your LMS." Read the manifest XML: ```bash cat "$IMPORT_DIR/imsmanifest.xml" ``` ### A4. Parse the cartridge structure Extract from the XML: **Course metadata:** - Title from `///` - Description from `` if present **Module structure:** - Each `/` with child `` elements represents a module - `` within each `<item>` is the module/item name - `identifierref` links items to resources **Resources:** - `<resource>` elements contain the actual content - `type` attribute indicates resource type: - `webcontent` → instructional materials (pages, files) - `imsqti_xmlv2p1` or `imsqti_xmlv1p2` → quizzes/assessments - `imsbasiclti_xmlv1p0` → external tool integrations - `imsdt_xmlv1p0` or `topic` → discussion topics - `assignment_xmlv1p0` or `assignment` → assignments **Learning outcomes (if present):** - Look for `<imscc:learningOutcomes>` or similar elements - Canvas exports include outcomes in `<metadata>` sections - Extract outcome text for Bloom's classification Read resource files for additional detail when the manifest references them: ```bash # Read assignment details, quiz content, etc. ls "$IMPORT_DIR"/*.xml "$IMPORT_DIR"/**/*.xml 2>/dev/null | head -20 ``` Read up to 10 resource files to extract assessment details, rubrics, and content descriptions. Prioritize assignments and quizzes over static content. **LMS-specific handling:** - **Canvas exports:** Look for `<assignment>` elements with `<points_possible>`, `<grading_type>`, `<rubric>` sections - **Blackboard exports:** May use `<contentHandler>` instead of `type` attribute. Look for `resource/bb-` prefixed types - **Moodle exports:** May use `<activity>` wrappers around standard IMS elements ### A5. Cleanup After extracting all needed data: ```bash rm -rf "$IMPORT_DIR" ``` Continue to Step 2 (Quality Flags). --- ## Path B: Paste Documents Import ### B1. Get the documents Ask: "Paste your course documents below. This could be a syllabus, module outline, assignment list, or course description. The more detail you provide, the better I can map your course structure. Paste the content and I'll extract the structure." ### B2. Extract structure From the pasted text, identify and extract: 1. **Course title and description** 2. **Modules or sections** — look for numbered sections, week-by-week breakdowns, unit headers, or topic groupings 3. **Learning objectives** — statements starting with "Students will...", "By the end of...", "Learners will be able to...", or similar 4. **Assessments** — assignments, exams, quizzes, projects, presentations 5. **Learning activities** — discussions, labs, group work, case studies 6. **Course logistics** — modality, duration, class size if mentioned ### B3. Confirm extracted structure Present the extracted structure for confirmation: ``` ## Extracted Course Structure **Title:** [extracted title] **Modules found:** [count] | # | Module | Items | Objectives | Assessments | |---|--------|-------|------------|-------------| | 1 | [name] | [count] | [count] | [count] | | 2 | [name] | [count] | [count] | [count] | ... **Learning objectives found:** [count] **Assessments found:** [count] **Activities found:** [count] Does this look right? If I missed anything or got something wrong, let me know. ``` If the user corrects something, incorporate the corrections. Continue to Step 2 (Quality Flags). --- ## Path D: PDF / Document File Import ### D1. Get the file path Ask: "Where is your PDF or document file? Provide the file path (drag and drop the file into this window to paste the path). This works with PDFs exported from Articulate Rise, Storyline, Adobe Captivate, or any authoring tool. Also works with Word documents, course packets, and syllabus PDFs." ### D2. Read the file Use the Read tool to read the file at the provided path. The Read tool can read PDFs directly (multimodal). If the file does not exist, ask the user to check the path. If the PDF is large (more than 10 pages), read in chunks using the `pages` parameter: - First pass: pages "1-10" - If more content exists: pages "11-20", etc. - Maximum 50 pages total. If the PDF is longer, note: "Reading first 50 pages. If important content is after page 50, let me know which pages to focus on." ### D3. Extract structure From the PDF content, identify and extract the same elements as Path B: 1. **Course title and description** 2. **Modules or sections** — look for numbered sections, week-by-week breakdowns, unit headers, or topic groupings 3. **Learning objectives** — statements starting with "Students will...", "By the end of...", "Learners will be able to...", or similar 4. **Assessments** — assignments, exams, quizzes, projects, presentations 5. **Learning activities** — discussions, labs, group work, case studies 6. **Course logistics** — modality, duration, class size if mentioned **Rise-specific notes:** Articulate Rise PDFs may lose interactive elements (Storyline blocks, flashcards, drag-and-drop activities). Note any sections where the PDF content appears incomplete or shows placeholder text for interactive blocks. Flag these as "interactive element not captured in PDF" in the import quality triage. ### D4. Confirm extracted structure Present the extracted structure for confirmation (same format as Path B, Step B3). If the user corrects something, incorporate the corrections. Continue to Step 2 (Quality Flags). --- ## Path C: Canvas REST API Import ### C1. Get credentials Ask: "I need two things to connect to Canvas: 1. **Canvas URL** — Your institution's Canvas address (e.g., `https://canvas.university.edu`) 2. **Access token** — Generate one in Canvas: Account → Settings → scroll to 'Approved Integrations' → New Access Token 3. **Course ID** — The number in the URL when you open the course (e.g., `https://canvas.university.edu/courses/12345` → course ID is `12345`) Your token is used for this session only and is NEVER saved to any file." ### C2. Validate connection ```bash RESPONSE=$(curl -s -w "\n%{http_code}" \ -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/users/self" 2>&1) HTTP_CODE=$(echo "$RESPONSE" | tail -1) BODY=$(echo "$RESPONSE" | head -n -1) echo "HTTP: $HTTP_CODE" echo "$BODY" | head -5 ``` Handle errors: - HTTP 401: "Token rejected. Make sure you copied the full token. In Canvas: Account → Settings → New Access Token." - HTTP 403: "Access denied. Your token may not have the right permissions." - Network error: "Can't reach Canvas at that URL. Check the address." ### C3. Fetch course data **SECURITY RULE: The token variable is used ONLY in curl commands within this section. NEVER write the token to the manifest, to any file, or to conversation output. After all API calls are complete, the token is discarded.** Fetch in this order (each is a separate curl call): **Course info:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID" | head -200 ``` **Modules with items:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/modules?include[]=items&per_page=50" ``` **Assignments:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/assignments?per_page=50" ``` **Pages (first page only for structure):** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/pages?per_page=50" ``` **Discussion topics:** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/discussion_topics?per_page=50" ``` **Outcomes (if available):** ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/outcome_groups?per_page=50" ``` For each outcome group found, fetch individual outcomes: ```bash curl -s -H "Authorization: Bearer $TOKEN" \ "$BASE_URL/api/v1/courses/$COURSE_ID/outcome_groups/$GROUP_ID/outcomes?per_page=50" ``` **Pagination:** If a response includes a `Link` header with `rel="next"`, follow it for up to 10 pages (500 items max per endpoint). After 500 items, stop and note "Partial import: course has more items than the import limit." **Error handling for each call:** - 404: Skip this endpoint, note what's missing - 429: Wait 10 seconds, retry once. If still 429: "Canvas is rate-limiting. Wait a minute and try `/course-import` again." - Timeout: "Canvas didn't respond for [endpoint]. Continuing with what we have." ### C4. Map API response to course structure From the API responses, extract: - **Course metadata:** name, term, start/end dates, enrollment count - **Modules:** name, position, items (with type: Assignment, Page, Discussion, etc.) - **Assignments:** name, description, points_possible, grading_type, submission_types, rubric - **Pages:** title (for content page count) - **Discussions:** title, assignment_id (graded discussions) - **Outcomes:** title, description, mastery_points, ratings Continue to Step 2 (Quality Flags). --- ## Path E: SCORM Package Import ### E1. Get the file path Ask: "Where is your SCORM package (.zip)? Provide the file path (drag and drop the file into this window to paste the path). This works with SCORM 1.2 and SCORM 2004 packages from Articulate Rise, Storyline, Adobe Captivate, Lectora, iSpring, or any SCORM-compliant authoring tool." ### E2. Validate and extract ```bash # Check file exists if [ ! -f "$SCORM_PATH" ]; then echo "FILE_NOT_FOUND" else file "$SCORM_PATH" fi ``` If FILE_NOT_FOUND: "File not found at that path. Check the path and try again." If not a ZIP: "This doesn't look like a SCORM package. It should be a .zip file exported from your authoring tool." Extract the package: ```bash IMPORT_DIR=$(mktemp -d) unzip -q "$SCORM_PATH" -d "$IMPORT_DIR" 2>&1 echo "IMPORT_DIR=$IMPORT_DIR" ls "$IMPORT_DIR/" ``` ### E3. Find and read the manifest ```bash if [ -f "$IMPORT_DIR/imsmanifest.xml" ]; then echo "SCORM_MANIFEST_FOUND" cat "$IMPORT_DIR/imsmanifest.xml" else echo "NO_SCORM_MANIFEST" fi ``` If NO_SCORM_MANIFEST: "No imsmanifest.xml found in this ZIP. This may not be a valid SCORM package. Try exporting again from your authoring tool, or use Path D (PDF import) instead." ### E4. Detect SCORM version From the manifest XML, check namespaces and schema references: - If `adlcp_rootv1p2` or `adlcp:scormtype` (lowercase) → SCORM 1.2 - If `adlcp_v1p3` or `adlcp:scormType` (camelCase) → SCORM 2004 - Note the version for the user: "Detected SCORM [version] package." ### E5. Parse manifest structure From `imsmanifest.xml`, extract: **From `<organizations>`:** - The default organization (identified by `default` attribute on `<organizations>`) - Walk the `<item>` tree recursively to build the module hierarchy - For each item: `identifier`, `title`, `identifierref` (links to resource) - Items with children are module containers (aggregations) - Leaf items with `identifierref` are deliverable content (SCOs or assets) **From `<resources>`:** - For each `<resource>`: `identifier`, `type`, `href` (launch file), `adlcp:scormType` - SCO resources contain the actual learning content - Asset resources are supporting files (images, scripts, CSS) - Read the HTML content of SCO launch files to extract learning content: ```bash # For each SCO resource, read its launch file for sco_href in $SCO_HREFS; do if [ -f "$IMPORT_DIR/$sco_href" ]; then echo "=== SCO: $sco_href ===" cat "$IMPORT_DIR/$sco_href" fi done ``` **From `<metadata>` (if present):** - Course title, description, keywords - Schema version ### E6. Map to course structure From the parsed manifest, construct the course structure: 1. **Course title:** From `<metadata>` or the root organization title 2. **Modules:** Each top-level `<item>` with children becomes a module 3. **Content items:** Leaf `<item>` elements become module items 4. **Learning objectives:** Search SCO HTML content for objective-like statements ("By the end of...", "Students will...", "Learners will be able to...") 5. **Assessments:** Look for quiz/assessment patterns in SCO content (question banks, score tracking via SCORM API calls in JavaScript) 6. **Sequencing (SCORM 2004 only):** If `<imsss:sequencing>` elements exist, extract prerequisite relationships and flow control rules **Articulate-specific parsing:** If the manifest contains `articulate` or `rise` in metadata or resource identifiers, note the authoring tool. Articulate packages often structure content as: one SCO per lesson, with a `story.html` or `index.html` launch file. Rise packages use a flat structure with a single SCO. ### E7. Confirm extracted structure Present the extracted structure for confirmation: ``` ## Extracted Course Structure (SCORM [version]) **Title:** [extracted title] **Authoring tool:** [detected or unknown] **Modules found:** [count] **SCOs:** [count] | **Assets:** [count] | # | Module | Items | Objectives | Assessments | |---|--------|-------|------------|-------------| | 1 | [name] | [count] | [count] | [count] | | 2 | [name] | [count] | [count] | [count] | ... **Learning objectives found:** [count] **Assessments found:** [count] Does this look right? If I missed anything or got something wrong, let me know. ``` If the user corrects something, incorporate the corrections. ### E8. Cleanup ```bash rm -rf "$IMPORT_DIR" ``` Continue to Step 2 (Quality Flags). --- ## Step 2: Quick-Scan Quality Flags After extracting course structure from ANY input method, scan for obvious quality issues. This is NOT a full /course-quality-review. This is a quick triage that flags problems visible in the structural data alone. **Structural flags:** - Count modules vs modules with stated objectives. Flag if < 50% have objectives: "⚠ {X} of {Y} modules have no stated learning objectives" - Count assessments with rubrics vs without. Flag if < 25% have rubrics: "⚠ {X} of {Y} assessments have no rubric [Assessment-8] [T1]" - Check for discussion/collaborative activities. Flag if zero: "⚠ No discussion or collaborative activities found — social presence gap risk [Online-15] [T2]" - Check for accessibility info. Flag if none: "⚠ No accessibility information found for course materials" - Check for learner support resources. Flag if none: "⚠ No learner support section detected (tutoring, office hours, tech support)" **Alignment flags:** - If objectives exist AND assessments exist but no clear mapping between them: "⚠ Objectives and assessments found but no alignment mapping detected" - If all assessments are the same type (e.g., all quizzes): "⚠ All {X} assessments are {type} — consider varied assessment for different Bloom's levels [Assessment-10] [T1]" **Assessment feedback flags:** - If quizzes are auto-graded with no indication of elaborated feedback: "⚠ Auto-graded assessments detected. Elaborated feedback (explaining WHY) produces larger learning gains than correctness-only feedback [Assessment-8] [T1]" Present the flags: ``` ## Import Quality Triage Found {N} flags during import: {list each flag with ⚠ prefix} These are quick observations from the course structure, not a full review. Run /course-quality-review for an evidence-based audit with specific recommendations. ``` If zero flags: "No obvious structural issues detected during import. Run /course-quality-review for a deeper analysis." --- ## Step 3: Auto-Map Modules to Task Analysis For each module extracted from the course, infer a task analysis entry. The goal is to pre-populate the `needs_analysis.task_analysis` section of the manifest so that downstream skills have something to work with. For each module: 1. **Description:** Rewrite the module title as a performance-oriented task statement. "Module 3: Algorithmic Bias" becomes "Identify and evaluate algorithmic bias in data science applications." 2. **Frequency:** Estimate based on module position and content: - Early foundational modules → skills used daily/weekly - Applied/project modules → skills used weekly/monthly - Specialized/capstone modules → skills used monthly/rarely 3. **Criticality:** Estimate based on assessment weight (if available) and topic: - Core skills with heavy assessment weight → high - Supporting skills → medium - Enrichment/optional topics → low 4. **Difficulty:** Estimate based on Bloom's level (if objectives available): - Remember/Understand → low - Apply/Analyze → medium - Evaluate/Create → high Assign task IDs: T-1, T-2, T-3, etc. Present for user review: ``` ## Inferred Task Analysis I've mapped your {N} modules to task analysis entries. Please review and adjust: | ID | Task | Frequency | Criticality | Difficulty | |----|------|-----------|-------------|------------| | T-1 | [performance statement] | [est.] | [est.] | [est.] | ... These estimates are based on module structure. Edit any that don't match your actual course context. ``` Ask the user to confirm or edit via AskUserQuestion. --- ## Step 4: Bloom's Level Inference For any learning objectives or outcomes found during import, pre-classify them using the revised Bloom's taxonomy. For each objective: 1. **Extract the action verb** — identify the primary verb in the objective 2. **Classify knowledge dimension:** - Factual (terminology, specific details) - Conceptual (categories, principles, theories) - Procedural (techniques, methods, criteria for use) - Metacognitive (self-knowledge, strategic planning) 3. **Classify cognitive process:** - Remember (recognize, recall) - Understand (interpret, exemplify, classify, summarize, infer, compare, explain) - Apply (execute, implement) - Analyze (differentiate, organize, attribute) - Evaluate (check, critique) - Create (generate, plan, produce) 4. **Confidence level:** - High: verb maps clearly to one Bloom's level - Ambiguous: verb could map to multiple levels (e.g., "analyze", "demonstrate") Mark as "verify with /learning-objectives" [Alignment-12] [T2] 5. **Set alignment_status to "imported-unverified"** — the user should run /learning-objectives to verify and check bidirectional alignment Assign ILO IDs: ILO-1, ILO-2, etc. Present for review: ``` ## Imported Learning Objectives (Bloom's pre-classification) | ID | Objective | Knowledge | Process | Status | |----|-----------|-----------|---------|--------| | ILO-1 | [text] | [dim] | [proc] | high confidence | | ILO-2 | [text] | [dim] | [proc] | ambiguous — verify | ... All classifications are marked "imported-unverified." Run /learning-objectives to verify Bloom's levels and check alignment with activities and assessments. ``` --- ## Step 5: Generate Import Report Before writing the manifest, generate a human-readable report at `.idstack/reports/course-import.md` so the designer has a single document to read about what came in and where the quality flags are. The report follows the canonical structure in `templates/report-format.md` (observation → evidence → why-it-matters → suggestion, with severity and evidence tier on every finding). ```bash mkdir -p .idstack/reports ``` Write `.idstack/reports/course-import.md` with this structure: ```markdown # Course Import Report **Course:** [project_name] **Generated:** [ISO-8601 timestamp] **Skill:** /idstack:course-import **Source:** [cartridge | paste | canvas-api | pdf | scorm] **Source LMS:** [canvas | blackboard | moodle | d2l | rise | storyline | unknown] ## Summary [2–3 sentences. What came in (N modules, M objectives, P assessments), how many quality flags were raised, and the single most important thing the designer should know about the import — e.g., "Import is structurally clean but objectives are sparse." or "Cartridge schema is valid but 8/12 modules are missing rubrics."] ## Imported | Item | Count | |------|-------| | Modules | N | | Objectives | M | | Assessments | P | | Activities/discussions | Q | | Pages | R | | Rubrics | S | ## Findings [One block per finding. Stable ids: `import-1`, `quality-1`, `bloom-1`, etc. Findings come from: import quality flags (Step 2), missing rubrics, missing objectives, alignment-already-broken-on-arrival, Bloom's-inference low-confidence classifications.] ### Finding quality-1: [short title] [severity] [tier] **What we saw.** [Concrete observation: "12 modules imported; only 4 have stated learning objectives."] **What the evidence says.** [1–2 sentences.] [Domain-N] [Tier] Example: "Modules without stated objectives leave learners and the assessment chain underspecified; constructive alignment is harder to evaluate downstream [Alignment-1] [T5]." **Why it matters.** [Bridge: missing objectives at the module level mean downstream skills (assessment-design, course-quality-review) have less to align against and may flag the gaps as designer intent rather than import gaps.] **Consider.** [Collaborative recommendation: "Run `/idstack:learning-objectives` to draft objectives for the 8 modules that are missing them. The Bloom's classifications already inferred for the 4 existing objectives can serve as calibration."] --- [Repeat per finding.] ## Bloom's classification [N objectives pre-classified. Note any low-confidence classifications that the designer should verify.] | ILO ID | Statement (truncated) | Bloom's level | Confidence | |--------|----------------------|---------------|------------| | ILO-1 | ... | analyze | high | ## Top recommendations 1. **[Action]** [Domain-N] [Tier] — [one-line why; pointer to skill] 2. ... ## Limitations [What this report didn't analyze. Examples: structural metadata only — interactive elements (Storyline, flashcards) don't render in PDF; Canvas API auth tokens are never written to disk; SCORM packages with non-standard manifest extensions may have been parsed loosely.] ## Next steps 1. Run `/idstack:course-quality-review` for the full evidence-based audit. 2. Run `/idstack:learning-objectives` to verify the Bloom's inference and check bidirectional alignment. 3. Run `/idstack:needs-analysis` (in audit-existing mode) to add the organizational context and learner profile that can't be extracted from the cartridge. --- *Generated by `/idstack:course-import`. The system-readable manifest section is in `.idstack/project.json` under `import_metadata`.* ``` --- ## Step 6: Write Manifest Create or update the project manifest. **Why this skill uses the Write-tool fallback (not `bin/idstack-manifest-merge`):** course-import writes a top-level field (`project_name`), partial fields inside `context` (`modality`, `timeline`, `available_tech`), nested entries under `needs_analysis.task_analysis.job_tasks`, additions inside `learning_objectives.ilos` and `learning_objectives.alignment_matrix`, AND the `import_metadata` section. The merge tool only does whole-section replacement, which would clobber co-owned sections. So this skill goes through the Read-modify-Write fallback path described in `templates/manifest-schema.md`. **CRITICAL — Manifest Integrity Rules:** 1. If a manifest already exists, READ it first. 2. PRESERVE sections populated by other skills (especially needs_analysis from /needs-analysis). Import ADDS to these sections, does not replace. 3. If learning_objectives already has data and the user chose "merge," combine imported ILOs with existing ones (use new ILO IDs that don't conflict). 4. Include the COMPLETE schema structure. Do not omit fields. 5. Before writing, verify the JSON is valid. 6. Update the `updated` timestamp. 7. **NEVER write the Canvas API token to the manifest or any file.** **Fields populated by /course-import:** - `project_name` — from course title - `context.modality` — inferred from course structure (async=online, sync sessions=hybrid) - `context.timeline` — from term/date info if available - `context.available_tech` — from detected resource types (LMS, video, discussions, etc.) - `needs_analysis.task_analysis.job_tasks` — from module-to-task mapping (Step 3) - `learning_objectives.ilos` — from Bloom's inference (Step 4) - `learning_objectives.alignment_matrix` — partial, from detected objective-assessment links **Import metadata:** Set the top-level `import_metadata` section. Shown here in the context of the full manifest the Write tool will produce (the outer `import_metadata` key is the section's slot in the manifest, not a wrapper to nest inside the section itself): ```json { "import_metadata": { "source": "cartridge|paste|canvas-api", "report_path": ".idstack/reports/course-import.md", "imported_at": "ISO-8601", "source_lms": "canvas|blackboard|moodle|d2l|unknown", "items_imported": { "modules": 0, "objectives": 0, "assessments": 0, "activities": 0, "pages": 0 }, "quality_flags": 0 } } ``` Write the manifest, then confirm: ``` ## Import Complete **Source:** {input method} **Course:** {title} **Imported:** - {N} modules → {N} task analysis entries - {M} learning objectives (Bloom's pre-classified) - {P} assessments - {Q} activities/discussions - {R} content pages **Quality triage:** {F} flags found **Two artifacts:** - **Read this:** `.idstack/reports/course-import.md` — the import report with evidence-backed quality flags, the Bloom's classification table, and recommended next steps tied to specific findings. - System state: `.idstack/project.json` (the manifest — for downstream skills). **Recommended next steps:** 1. `/course-quality-review` — Full evidence-based audit with QM standards and CoI presence analysis 2. `/learning-objectives` — Verify Bloom's classifications and check bidirectional alignment (objectives ↔ activities ↔ assessments) 3. `/needs-analysis` — Add organizational context and learner profile data that can't be extracted from the course structure alone ``` --- ## Manifest Schema Reference The idstack manifest lives at `.idstack/project.json`. Schema version: **1.4**. This is the canonical schema. Every skill writes to its own section using the shapes documented here; **all other sections must be preserved verbatim**. There is one source of truth — this file. If the schema ever needs to change, edit `templates/manifest-schema.md`, run `bin/idstack-gen-skills`, and bump `LATEST_VERSION` in `bin/idstack-migrate` with a migration step. ### Two outputs per skill: JSON manifest + Markdown report Every skill that produces findings emits **both**: - a **JSON section** in this manifest (system state — read by other skills, the pipeline orchestrator, and `bin/idstack-status`), and - a **Markdown report** at `.idstack/reports/<skill>.md` (the human view — read by the instructional designer). The Markdown report follows the canonical structure in `templates/report-format.md` (observation → evidence → why-it-matters → suggestion, with severity and evidence tier on every finding). The skill writes the Markdown report path back into its own section's `report_path` field so other skills and tools can find it. `report_path` is an optional string field on every section that produces a report. Empty string means the skill hasn't run yet, or ran in a mode that didn't produce a report. ### Two ways to write to the manifest **1. Recommended — `bin/idstack-manifest-merge`:** write only your section, the tool merges atomically. ```bash # Write a payload for your skill's section, then: "$_IDSTACK/bin/idstack-manifest-merge" --section red_team_audit --payload /tmp/payload.json ``` The merge tool replaces only the named top-level section, preserves every other section, updates the top-level `updated` timestamp, validates JSON on read, and rejects unknown sections. Use this in preference to inlining the full manifest in `Edit` operations. **2. Fallback — manual full-manifest write:** if the merge tool is unavailable for some reason, Read the full manifest, modify only your section, Write back. Preserve all other sections verbatim. Use the full schema below as reference. ### Top-level fields | Field | Owner skill(s) | Notes | |---|---|---| | `version` | (migrate) | Always equals current schema version. Auto-managed by `bin/idstack-migrate`. | | `project_name` | (any) | Set on first manifest creation. Don't overwrite once set. | | `created` | (any, once) | ISO-8601 timestamp of first creation. Don't overwrite. | | `updated` | (any) | ISO-8601 of last write. Updated automatically by `bin/idstack-manifest-merge`. | | `context` | needs-analysis (initial) | Modality, timeline, class size, etc. Edited by skills that learn new context. | | `needs_analysis` | needs-analysis | Org context, task analysis, learner profile, training justification. | | `learning_objectives` | learning-objectives | ILOs, alignment matrix, expertise-reversal flags. | | `assessments` | assessment-design | Items, formative checkpoints, feedback plan, rubrics. | | `course_content` | course-builder | Generated modules, syllabus, content paths. | | `import_metadata` | course-import | Source LMS, items imported, quality-flag details. | | `export_metadata` | course-export | Export destination, items exported, readiness check. | | `quality_review` | course-quality-review | QM standards, CoI presence, alignment audit, cross-domain checks, scores. | | `red_team_audit` | red-team | Confidence score, dimensions, findings (with stable ids), top actions. | | `accessibility_review` | accessibility-review | WCAG / UDL scores, violations, recommendations, quick wins. | | `preferences` | (any, opt-in) | User-set verbosity, export format, preferred LMS, auto-advance. | ### Full schema (canonical shape) ```json { "version": "1.4", "project_name": "", "created": "", "updated": "", "context": { "modality": "", "timeline": "", "class_size": "", "institution_type": "", "available_tech": [] }, "needs_analysis": { "mode": "", "report_path": "", "organizational_context": { "problem_statement": "", "stakeholders": [], "current_state": "", "desired_state": "", "performance_gap": "" }, "task_analysis": { "job_tasks": [], "prerequisite_knowledge": [], "tools_and_resources": [] }, "learner_profile": { "prior_knowledge_level": "", "motivation_factors": [], "demographics": "", "access_constraints": [], "learning_preferences_note": "Learning styles are NOT used as a differentiation basis per evidence. Prior knowledge is the primary differentiator." }, "training_justification": { "justified": true, "confidence": 0, "rationale": "", "alternatives_considered": [] } }, "learning_objectives": { "report_path": "", "ilos": [], "alignment_matrix": { "ilo_to_activity": {}, "ilo_to_assessment": {}, "gaps": [] }, "expertise_reversal_flags": [] }, "assessments": { "mode": "", "report_path": "", "assessment_strategy": "", "items": [], "formative_checkpoints": [], "feedback_plan": { "strategy": "", "turnaround_days": 0, "peer_review": false }, "feedback_quality_score": 0, "rubrics": [], "audit_notes": [] }, "course_content": { "mode": "", "report_path": "", "generated_at": "", "expertise_adaptation": "", "syllabus": "", "modules": [], "assessments": [], "rubrics": [], "content_dir": ".idstack/course-content/", "generated_files": [], "build_timestamp": "", "placeholders_used": [], "recommended_generation_targets": [] }, "import_metadata": { "source": "", "report_path": "", "imported_at": "", "source_lms": "", "source_cartridge": "", "source_size_bytes": 0, "schema": "", "items_imported": { "modules": 0, "objectives": 0, "module_objectives": 0, "assessments": 0, "activities": 0, "pages": 0, "rubrics": 0, "quizzes": 0, "discussions": 0 }, "quality_flags": 0, "quality_flag_details": [] }, "export_metadata": { "report_path": "", "exported_at": "", "format": "", "destination": "", "items_exported": { "modules": 0, "pages": 0, "assignments": 0, "quizzes": 0, "discussions": 0 }, "failed_items": [], "notes": "", "readiness_check": { "quality_score": 0, "quality_reviewed": false, "red_team_critical": 0, "red_team_reviewed": false, "accessibility_critical": 0, "accessibility_reviewed": false, "verdict": "" } }, "quality_review": { "report_path": "", "last_reviewed": "", "qm_standards": { "course_overview": {"status": "", "findings": []}, "learning_objectives": {"status": "", "findings": []}, "assessment": {"status": "", "findings": []}, "instructional_materials": {"status": "", "findings": []}, "learning_activities": {"status": "", "findings": []}, "course_technology": {"status": "", "findings": []}, "learner_support": {"status": "", "findings": []}, "accessibility": {"status": "", "findings": []} }, "coi_presence": { "teaching_presence": {"score": 0, "findings": []}, "social_presence": {"score": 0, "findings": []}, "cognitive_presence": {"score": 0, "findings": []} }, "alignment_audit": {"findings": []}, "cross_domain_checks": { "cognitive_load": {"score": 0, "flags": []}, "multimedia_principles": {"score": 0, "flags": []}, "feedback_quality": {"score": 0, "flags": []}, "expertise_reversal": {"score": 0, "flags": []} }, "overall_score": 0, "score_breakdown": { "qm_structural": 0, "coi_presence": 0, "constructive_alignment": 0, "cross_domain_evidence": 0 }, "quick_wins": [], "recommendations": [], "review_history": [] }, "red_team_audit": { "updated": "", "confidence_score": 0, "focus": "", "report_path": "", "findings_summary": {"critical": 0, "warning": 0, "info": 0}, "dimensions": { "alignment": {"score": "", "findings": []}, "evidence": {"score": "", "mode": "", "findings": []}, "cognitive_load": {"score": "", "findings": []}, "personas": {"score": "", "findings": []}, "prerequisites": {"score": "", "findings": []} }, "top_actions": [], "limitations": [], "fixes_applied": [], "fixes_deferred": [] }, "accessibility_review": { "updated": "", "report_path": "", "score": {"overall": 0, "wcag": 0, "udl": 0}, "wcag_violations": [], "udl_recommendations": [], "quick_wins": [] }, "preferences": { "verbosity": "normal", "export_format": "", "preferred_lms": "", "auto_advance_pipeline": false } } ``` ### Per-section item shapes These document the **shape of array elements and dictionary values** that the canonical schema leaves as `[]` or `{}`. Skills should produce items in these shapes; downstream skills can rely on them. **`learning_objectives.alignment_matrix.ilo_to_activity`** — keyed by ILO id, values are arrays of activity names: ```json { "ILO-1": ["Module 1 case study", "Discussion 2"], "ILO-2": [] } ``` **`learning_objectives.alignment_matrix.ilo_to_assessment`** — same shape, values are arrays of assessment titles. **`learning_objectives.alignment_matrix.gaps[]`** — each item: ```json { "ilo": "ILO-1", "type": "untested|orphaned|underspecified|bloom_mismatch", "description": "ILO-1 has no matching assessment in the active modules.", "severity": "critical|warning|info" } ``` **`learning_objectives.ilos[]`** — each item: ```json { "id": "ILO-1", "statement": "Analyze competitive forces in...", "blooms_level": "analyze", "blooms_confidence": "high|medium|low" } ``` **`assessments.items[]`** — each item: ```json { "id": "A-1", "type": "quiz|discussion|rubric|peer_review|gate|...", "title": "Module 1 Quiz", "weight": 5, "ilos_measured": ["ILO-1", "ILO-3"], "rubric_present": true, "elaborated_feedback": false, "alignment_status": "weak|moderate|strong" } ``` **`assessments.rubrics[]`** — each item: ```json { "id": "rubric-1", "title": "SM Project Rubric", "criteria": [{"name": "...", "blooms_level": "...", "weight": 0}], "applies_to": ["A-3"] } ``` **`import_metadata.quality_flag_details[]`** — each item (replaces the legacy `_import_quality_flags` root field that sometimes appeared in the wild): ```json { "key": "orphan_module_8", "description": "Module 8 wiki content exists in the cartridge but is not referenced in <organizations>.", "severity": "warning|critical|info", "evidence": "Optional citation tag, e.g. [Alignment-1] [T5]" } ``` **`red_team_audit.dimensions.<name>.findings[]`** — each item (matches the `<dimension>-<n>` id convention from the red-team orchestrator): ```json { "id": "alignment-1", "description": "ILO-2 (vision/mission) has no matching assessment.", "module": "Module 4", "severity": "critical|warning|info" } ``` **`accessibility_review.wcag_violations[]`** — each item: ```json { "id": "wcag-1", "criterion": "1.3.1 Info and Relationships", "level": "A|AA|AAA", "description": "All cartridge HTML pages lack <h1> elements.", "affected": ["page1.html", "page2.html"], "severity": "critical|warning|info" } ``` **`accessibility_review.udl_recommendations[]`** — each item: ```json { "id": "udl-1", "principle": "engagement|representation|action_expression", "description": "Add transcripts to all videos.", "status": "fully_met|partial|not_met" } ``` **`quality_review.qm_standards.<standard>.findings[]`**, **`quality_review.alignment_audit.findings[]`**, **`quality_review.cross_domain_checks.<check>.flags[]`**, and other findings arrays — each item: ```json { "id": "<dimension>-<n>", "description": "...", "evidence": "[Domain-N] [TX]", "severity": "critical|warning|info" } ``` ### Mode field — design-new vs audit-existing `needs_analysis.mode`, `assessments.mode`, and `course_content.mode` record which operating mode the corresponding skill ran in. Trigger: `import_metadata.source` ∈ `{cartridge, scorm, canvas-api}` plus the relevant section being non-empty (skill-specific check). Allowed values per skill: - `needs_analysis.mode`: `"design-new"` or `"audit-existing"` - `assessments.mode`: `"Mode 1"`, `"Mode 2"`, or `"Mode 3"` (Mode 1 = full upstream data, Mode 2 = ILOs-from-scratch, Mode 3 = audit existing assessments) - `course_content.mode`: `"build-new"` or `"gap-fill"` Empty string means the skill hasn't run yet or didn't record the mode (legacy manifests). **`assessments.audit_notes[]`** — only populated in Mode 3. Records which audit findings the user chose to act on: ```json { "target_id": "A-3", "action": "applied|deferred|declined", "description": "Rubric criterion for ILO-2 added: 'Synthesis depth (1-4 scale)'.", "reason": "Optional — only meaningful for deferred/declined." } ``` **`course_content.recommended_generation_targets[]`** — populated in `gap-fill` mode. Lists artifacts upstream skills flagged as missing, with status: ```json { "description": "Discussion rubric for Module 5", "source": "red-team:alignment-3 | quality-review:learner_support-2 | user-request", "status": "generated|deferred|declined", "output_path": "Optional — set when status=generated, points to the generated file." } ``` ## Feedback Have feedback or a feature request? [Share it here](https://forms.gle/6LDgDD1M6WWyYvME8) — no GitHub account needed. --- ## Completion: Timeline Logging After the skill workflow completes successfully, log the session to the timeline: ```bash "$_IDSTACK/bin/idstack-timeline-log" '{"skill":"course-import","event":"completed"}' ``` Replace the JSON above with actual data from this session. Include skill-specific fields where available (scores, counts, flags). Log synchronously (no background &). If you discover a non-obvious project-specific quirk during this session (LMS behavior, import format issue, course structure pattern), also log it as a learning: ```bash "$_IDSTACK/bin/idstack-learnings-log" '{"skill":"course-import","type":"operational","key":"SHORT_KEY","insight":"DESCRIPTION","confidence":8,"source":"observed"}' ```