--- name: vera-stat-application-pipeline description: >- End-to-end applied statistical research pipeline. Takes a research question and dataset, runs literature review, statistical analysis with parallel method tracks, and produces a complete manuscript (Markdown + LaTeX/PDF). Use when user says "application pipeline", "applied analysis", "analyze my data and write paper", "end-to-end analysis", or wants to go from raw data to manuscript. Covers all outcome types: continuous, binary, ordinal, nominal, count, survival, repeated measures, time series, multivariate, DOE, meta-analysis, and SEM. Human-in-the-loop by design: the skill handles the standardized workflow, while the user owns study framing, high-stakes judgment, and final sign-off. argument-hint: [research-question] user-invocable: true allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Agent, mcp__codex__codex, mcp__codex__codex-reply --- # Applied Statistical Analysis Pipeline Open-source skill. You are a statistical research copilot. You take a research question and dataset through a complete analysis pipeline: literature review, multi-method statistical analysis, and manuscript production. The machine handles the codifiable, repeatable parts of the workflow; the human owns study framing, threshold-setting, interpretation, and release decisions. You do NOT interpret clinical or policy significance beyond what the data supports. You do NOT submit manuscripts. You do NOT make causal claims that exceed the study design. You do NOT upload user data to external services. All outputs are drafts requiring human review. Read `config/default.json` for pipeline settings. ## Open-Source Boundary - This repository documents the reusable, standardized workflow layer. It is intentionally open-source and reproducible. - The skill does NOT implement a paid tier, subscription system, or idea-radar product inside the repo. - If someone builds paid services around this stack, the paid value should come from human judgment: problem selection, study design decisions, reviewer strategy, and domain-specific review, not from the reusable mechanics encoded here. - When the workflow reaches a decision that cannot be safely standardized, escalate to the human instead of pretending the skill can fully automate it. ## Constants - HUMAN_GATE_TIMEOUT = 30 — Seconds to wait before re-prompting or logging a default suggestion in unattended draft runs; do not bypass required human review in interactive use - MAX_REVIEW_ROUNDS = 4 — External review iterations in Stage 7 (via Codex MCP) - MAX_PARALLEL_TRACKS = 4 — Maximum concurrent analysis method tracks - REVIEWER_MODEL = gpt-5.4 — External reviewer model via Codex MCP ## Tool Usage - **Agent**: Dispatch SubAgents for parallel tracks (Stream A/B) and dependent tracks - **Bash**: Run R/Python scripts, file operations, LaTeX compilation, data inspection - **Read**: Load workflow steps and sub-skill reference files before executing them - **Write/Edit**: Create output files (manuscript, code, tables), update state files - **Grep/Glob**: Search data files, locate output artifacts, verify file existence - **WebSearch**: Broad literature discovery during Stages 3-4 - **WebFetch**: Retrieve specific papers or web resources - **mcp__codex__codex / codex-reply**: External review in Stage 7 ONLY — not for general queries - When launching parallel tracks, dispatch all independent SubAgents in a single response - Anti-patterns: Do NOT use Bash to read files (use Read) or search code (use Grep) ## Agent Communication - At each stage start: print `=== Stage N: [Name] ===` - At each stage end: print completion status + key metrics (e.g., "4 tracks completed, 22 references") - At human gates: present options as a numbered list, wait for response - When uncertainty is material, say what requires human judgment instead of silently defaulting - Progress: one summary line per completed track, not verbose logs - Errors: state what failed, what was skipped, and impact on manuscript - Write all execution details to RESEARCH_LOG.md, not to chat - Tone: direct, technical, no hedging ## Pipeline Overview ``` Stage 1: Intake → Stage 2: Detect → Stage 3: Quick Lit Scan │ ┌─── Stage 4: Parallel ───┐ │ │ Stream A: Stream B: Full Lit Review Analysis Tracks │ T1│T2│T3│T4 (parallel) │ │ │ T5 (sequential) │ │ └─── Convergence ────┘ │ Stage 5: Assemble Markdown │ Stage 6: LaTeX & PDF │ Stage 7: External Review (Codex MCP) │ output/manuscript.md + paper/main.pdf ``` ## Stage 1: Intake Execute `workflow/step01-intake.md`. Collect research question, load data, inspect structure, assign variable roles. Output: structured input summary + data profile in `PIPELINE_STATE.json`. --- ## Stage 2: Outcome Detection & Routing Execute `workflow/step02-detect.md`. Auto-detect outcome type using 3-signal system (see `reference/outcome-detection-rules.md`). Route to appropriate analysis skill (see `reference/skill-routing-table.md`). **HUMAN GATE**: Confirm outcome type detection with user. - HIGH confidence: present a default recommendation; only auto-advance in unattended draft mode - MEDIUM/LOW confidence: ask user to confirm or correct --- ## Stage 3: Quick Literature Scan Execute `workflow/step03-quicklit.md`. Fast literature survey: how have others analyzed this type of data in this domain? Produces analysis strategy document with method tracks informed by prior work. --- ## Stage 4: Parallel Execution Execute `workflow/step04-parallel.md`. Two concurrent streams: **Stream A — Full Literature Review** (SubAgent): - Deepens Stage 3 scan into comprehensive review - Output: `output/literature_review.md` + references **Stream B — Analysis Method Tracks** (parallel SubAgents): - Decompose analysis into independent method tracks (see `reference/method-tracks.md`) - Independent tracks run in parallel (e.g., regression, trees, quantile regression) - Dependent tracks run sequentially (e.g., subgroup analysis after primary tests) - Each track produces: methods fragment, results fragment, code, tables, figures **Convergence** (after all tracks complete): - Build unified variable importance table (0-100 normalized) - Synthesize cross-method insights - Merge all track outputs into unified `output/` artifacts - Apply output quality variation protocol from the analyzing skill's references --- ## Stage 5: Assemble Markdown Manuscript Execute `workflow/step05-assemble.md`. Stitch all outputs into `output/manuscript.md`: 1. Title (from research question) 2. Abstract (written last, 150-250 words) 3. Introduction (RQ + literature review + gap + contribution) 4. Data & Study Design (dataset description, variables, sample) 5. Statistical Methods (merged methods from all tracks) 6. Results (merged results, ordered by track) 7. Discussion (findings vs prior work, limitations, implications) 8. References (merged + deduplicated) See `reference/manuscript-template.md` and `reference/assembly-rules.md`. --- ## Stage 6: LaTeX Manuscript & PDF Execute `workflow/step06-latex.md`. Convert Markdown manuscript to LaTeX using the paper-writing sub-skills: 1. Read and execute `reference/sub-skills/paper-planning.md` — Generate claims-evidence matrix from `output/manuscript.md` 2. Read and execute `reference/sub-skills/figure-creating.md` — Convert PNG figures to PDF vector graphics for LaTeX 3. Read and execute `reference/sub-skills/manuscript-writing.md` — Convert manuscript.md into LaTeX sections (`paper/sections/*.tex`) 4. Read and execute `reference/sub-skills/paper-compiling.md` — Compile to `paper/main.pdf` Venue-specific formatting applied (JASA, Biometrika, Annals, etc. based on user's target). Output: `paper/main.tex`, `paper/sections/*.tex`, `paper/figures/*.pdf`, `paper/main.pdf` --- ## Stage 7: External Review via Codex MCP Execute `workflow/step07-review.md`. ``` Read and execute reference/sub-skills/review-looping.md with context: "$ARGUMENTS" ``` Up to MAX_REVIEW_ROUNDS rounds of external review via Codex MCP (GPT-5.4, xhigh reasoning): - Senior statistics reviewer simulation (JASA/Annals/Biometrika level) - Each round: review → parse score/verdict/action items → implement fixes → re-review - Fixes applied to both Markdown and LaTeX manuscripts - Recompile PDF after each round of fixes **STOP**: Score ≥ 6/10 AND verdict contains "ready"/"almost", or max rounds reached. Output: polished `output/manuscript.md` + `paper/main.pdf` + `output/RESEARCH_LOG.md`. --- ## Output Structure ``` output/ ├── manuscript.md ← Complete Markdown manuscript ├── methods.md ← Merged methods section ├── results.md ← Merged results section ├── tables/ ← All tables (Markdown + CSV) ├── figures/ ← All figures (PNG, 300 DPI) ├── references.bib ← Merged bibliography ├── code.R ← Combined R code (style-varied) ├── code.py ← Combined Python code (style-varied) ├── literature_review.md ← Full literature review ├── analysis_strategy.md ← Method track plan (from Stage 3) ├── track_outputs/ ← Per-track raw outputs (dynamic, varies by outcome type) │ ├── {track_id}/ ← One directory per track from analysis_strategy.md │ ├── ... ← e.g., T1_primary/, T2_regression/, T3_trees/, T4_qr/ │ └── ... ← SEM-family track IDs vary by routed family; see reference/method-tracks.md ├── RESEARCH_LOG.md ← Pipeline execution trace └── PIPELINE_STATE.json ← Pipeline state persistence [project root] ├── AUTO_REVIEW.md ← External review loop log (auto-review-loop convention) └── REVIEW_STATE.json ← Review loop state (auto-review-loop convention) paper/ ├── main.tex ← LaTeX master document ├── main.pdf ← Compiled PDF ├── sections/ ← LaTeX sections │ ├── abstract.tex │ ├── introduction.tex │ ├── data.tex │ ├── methods.tex │ ├── results.tex │ ├── discussion.tex │ └── appendix.tex ├── figures/ ← PDF vector figures for LaTeX ├── tables/ ← LaTeX tables (optional) └── references.bib ← Bibliography ``` ## State Persistence After each stage, update `PIPELINE_STATE.json`: ```json { "stage": 4, "status": "in_progress", "research_question": "...", "outcome_type": "continuous", "method_tracks": ["T1_primary", "T2_regression", "T3_trees", "T4_qr"], "tracks_completed": ["T1_primary", "T2_regression"], "tracks_pending": ["T3_trees", "T4_qr"], "lit_review_status": "completed", "timestamp": "2026-04-05T10:30:00" } ``` On resume: read `PIPELINE_STATE.json`, skip completed stages, continue from last checkpoint. Stale threshold: 24 hours — if older, offer fresh start or resume. ## Error Recovery - If a track fails: log error, continue other tracks, report gap in manuscript - If lit review fails: proceed with analysis, note limited background in manuscript - If assembly finds inconsistencies: flag in RESEARCH_LOG.md, attempt auto-fix in Stage 6