--- name: litreview description: "Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search." license: MIT metadata: source_spec: "megaprompts/09-litreview-megaprompt.md" build_pattern: "Path B (direct conversion)" research_pack_convention: "Agent Integrity Rules verbatim per PR #657 audit; sibling of pulse" version: 1.0.0 --- # Litreview — Academic Literature Orientation > **Portability:** Requires a Consensus MCP connection, Node.js with `docx` package for document generation, and (in CLI) `bash_tool`. Works in Claude Code CLI natively. In Claude.ai with Consensus MCP + Code Execution, the workflow is supported. Produce a **launching pad** — not a finished literature review, but an orientation document that gives a researcher entering an unfamiliar field everything they need to start reading and searching with confidence. Think: what a generous colleague who knows the field would tell you over coffee. ## Agent Integrity Rules (Research-Pack Convention) Inherited from the research-pack convention; locked verbatim per PR #657's cross-skill consistency audit. - **Source discipline.** Only cite Consensus-returned papers from THIS session. Training knowledge labeled `[Not from Consensus — model knowledge]` and excluded from cited count. Sparse results stated explicitly, never silently filled. - **Counting discipline.** Three numbers tracked: searches executed / unique papers received (deduplicated) / papers cited. Every cited paper has a retrievable Consensus URL from this session. Use `scripts/citation_tracker.py` for deterministic counts. - **Tool constraints.** Consensus per-query cap depends on plan tier. **Detect at first search**, report at checkpoint. Rate limit is **1 query/sec** — sequential execution mandatory. - **Retry policy.** On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user, share what was collected. - **Plan-tier detection.** Parse first-search response for "Showing top 10" / "upgrade" → free tier (10/search). 20 returned → Pro (20/search). Calculate theoretical ceiling and surface at checkpoint so user can recalibrate. See [`references/search_budget_allocation.md`](references/search_budget_allocation.md) for the sequential-execution rationale + plan-tier signals. ## Error Handling | Failure | Behavior | |---|---| | Consensus rate-limit hit | Wait 3s, retry once, log outcome | | Search returns 0 results | Note explicitly; "either niche terminology or genuine gap"; never silently fill | | Plan-tier cap detected | Log tier; report at checkpoint; surface in audit | | 3 consecutive failures | Stop searching, alert user, share what's collected, ask how to proceed | | Sub-area returns thin results (<5 papers) | Flag in audit; suggest manual PubMed/Scholar supplementation | | User wants to adjust sub-areas | Update table, re-confirm before searching | | DOCX validation fails | Unpack XML, fix, repack | ## Phase 0: Grill-Me Intake (3 forcing questions, one at a time) Each question carries explicit "why I'm asking". Stop condition: max 3 before Phase 1. ### Q1 (root) — Research question specificity > **State the research question in 1–2 sentences. Specific is better — "How do LLMs perform on clinical reasoning tasks compared to physicians?" beats "AI in medicine". Vague questions produce vague reviews.** > > *Why I'm asking:* The reconnaissance search hinges on precise terminology. Vague questions produce thin recon results that don't yield a useful framework breakdown. **Refuse mush.** Re-ask once with examples if user is too broad. If still vague, deliver with explicit "broad-scope orientation, not depth review" caveat. ### Q2 (depends on Q1) — Framework hint > **Framework — pick one or say "you pick":** > > 1. **PICO** (Population / Intervention / Comparison / Outcome — most clinical questions) > 2. **SPIDER** (Sample / Phenomenon / Design / Evaluation / Research-type — social/qualitative) > 3. **Decomposition** (Problem / Solution / Evaluation / Limitations — technology-focused) > 4. **Hybrid** (you pick which components from which framework) > 5. **You pick** — analyze Q1 and recommend > > *Why I'm asking:* PICO is the default for ~70% of clinical questions but maps poorly to qualitative work or technology evaluation. Picking upfront saves the recon search from suggesting a misaligned framework. Forcing choice with default ("you pick"). The skill surfaces its own framework recommendation after the recon search so user can override. Use `scripts/framework_recommender.py` for the heuristic. See [`references/framework_selection.md`](references/framework_selection.md) for PICO / SPIDER / Decomposition canon. ### Q3 (depends on Q1) — Tentative depth > **Tentative depth — pick one. Final confirmation comes after the framework breakdown:** > > 1. **Quick scan** (5 searches) > 2. **Standard review** (10 searches) > 3. **Deep dive** (20 searches) > > *Why I'm asking:* I ask this twice — once now to calibrate the recon search emphasis, once after the framework breakdown to confirm. Tentative answer affects which sub-areas to surface first; final answer drives search budget allocation. Forcing choice. **Re-asked** at the post-Phase-2 checkpoint after the user has seen the framework breakdown. **Stop condition:** 3 questions max before Phase 1. The post-Phase-2 checkpoint is its own grill-me moment (framework table + sub-area-adjustment + depth-reconfirmation). ## Phase 1: Initial Reconnaissance **One broad Consensus search** to map themes, terminology, methodological distinctions. - Query: broad version of Q1 (terminology variants are okay; first search casts wide) - Record: `citation_tracker.py --action record_search --session NAME --query "..."` - Record received count: `citation_tracker.py --action record_papers_received --session NAME --count N` - **Detect plan tier** from response: "Showing top 10" / "upgrade" → free; 20 returned → Pro Synthesize for the checkpoint: - Themes that surfaced - Terminology variations (e.g., "LLM" vs "large language model" vs "GPT-style model") - Methodological distinctions (clinical trials vs benchmark eval vs case study) - Coverage gaps (sub-questions absent from recon results) ## Phase 2: Framework Selection + Sub-area Generation Choose framework (from Q2 OR override based on recon): - **PICO** — most clinical questions (~70% default) - **SPIDER** — social / qualitative - **Decomposition** — technology focus (Problem / Solution / Evaluation / Limitations) - **Hybrid** — explicit cross-framework mapping Generate **4-5 sub-area questions** mapped to framework components. Each becomes a targeted Phase 3 search. ## Checkpoint (grill-me forcing-options moment) After Phase 2, halt and present: ### 3-4 sentence recon summary - What themes surfaced - Terminology landscape - Evidence landscape characterization ### Framework breakdown table | Framework Component | How It Maps to This Topic | Proposed Sub-area to Explore | |---|---|---| | (Component 1) | ... | Sub-area 1 | | (Component 2) | ... | Sub-area 2 | | (Component 3) | ... | Sub-area 3 | | (Component 4) | ... | Sub-area 4 | | Cross-cutting theme | ... | Sub-area 5 | ### Depth re-confirmation (forcing choice) Surface the **practical constraint**: detected plan tier + theoretical ceiling. - Quick scan (5 searches × ~10 results each = ~50 papers max) - Standard review (10 searches × ~10 = ~100 papers) - Deep dive (20 searches × ~10 = ~200 papers) ### Sub-area forcing options - "Looks good — proceed with these sub-areas" - "Adjust: add sub-area on [X]" - "Adjust: remove and replace [Y] with [Z]" - "Restart with different framework" ### Why I'm asking (the rationale) > A wrong framework or sub-area set wastes the search budget. This is the **last cheap moment** to correct course. **Wait for user response before Phase 3.** Refuse to start Phase 3 without explicit user choice. ## Phase 3: Targeted Searches Sequential (1 query/sec), budget per depth tier. See [`references/search_budget_allocation.md`](references/search_budget_allocation.md) for full canon. ### Quick scan (5 searches) - 5 sub-area searches (one per sub-area) - Skip era-gated + review-specific ### Standard review (10 searches) - 5 sub-area searches - 2 review article searches (top 2 sub-areas): `"systematic review [topic]"` / `"meta-analysis [topic]"` - 2 era-gated searches (most important sub-area): `year_max: 2015` + `year_min: 2021` - 1 follow-up on highest-cited paper using its key terms + `year_min` after publication ### Deep dive (20 searches) - 5 sub-area searches - 5 review article searches (one per sub-area) - 4 era-gated searches (top 2 sub-areas, old + new each) - 3 follow-ups on top 3 highest-cited papers - 3 spare for emerging threads (surprising findings to chase) Throughout: 1 q/sec rate limit. Sequential. Confirm response before next call. Record each via `citation_tracker.py`. ## Cross-Search Intelligence Three trackers across ALL search results — run `scripts/cross_search_aggregator.py --session NAME` after Phase 3 completes: 1. **Repeat-hit papers** — same paper appearing in 3+ sub-area searches = likely foundational 2. **Recurring authors** — same author in multiple searches = dominant research group; top 3-5 most frequent matter 3. **Citation-per-year heuristic** — a 2023 paper with 150 citations >> 2008 paper with 150 citations. Use for seminal-work identification. These feed the "Start Here" + "Key Research Groups" + "Bibliography" DOCX sections. ## Phase 4: DOCX Research Guide Generate via Node.js + `docx` library. 8 sections (see [`references/docx_8_sections.md`](references/docx_8_sections.md) for full spec): 1. **Topic Overview** — single tight paragraph (4-6 sentences) 2. **Start Here — Priority Reading Order** — 5-7 papers ordered: best recent review → foundational → 2-3 frontier → gap/controversy. Each: hyperlinked title + authors/year + 1-sentence contribution + 1-sentence "what to look for" 3. **How the Field Got Here** — chronological narrative (1-2 paragraphs) + timeline table (5-8 milestones: Year / Milestone / Significance) + terminology evolution note 4. **Sub-area Guides** (one per sub-area, 4 parts each) - 4a. What the Research Shows (2-3 sentence synthesis with inline citations) - 4b. Key Papers (3-5 hyperlinked papers with citation count, year, 1-sentence importance) - 4c. Key Search Terms (6-10 keywords, synonyms, MeSH, historical terms) - 4d. Boolean Search Strings (2-3 ready-to-paste strings) 5. **Key Research Groups** — top 3-5 authors/groups with affiliations, sub-area coverage, representative paper link (from cross-search aggregator) 6. **Open Questions & Gaps** — three categories: methodological / population-context / conceptual-theoretical. Each gap explains *why it matters*. 7. **Bibliography** — alphabetical by first author. Every entry has clickable "View on Consensus" link. Every inline citation matches a bibliography entry. 8. **Audit Log** — search summary table (#, query, filters, papers returned, status), counts block, coverage notes including detected tier and theoretical ceiling ### DOCX Technical Requirements Document the key `docx` library patterns: - Page: US Letter, 1-inch margins - Lists: `LevelFormat.BULLET` (never unicode bullets) - Hyperlinks: `ExternalHyperlink` with `style: "Hyperlink"`, full URL (never truncated) - Tables: dual widths (`columnWidths` + cell `width`), `ShadingType.CLEAR` - Validation step after save (`python scripts/office/validate.py output.docx`) Reference the **docx skill** for setup patterns and best practices. ## Output ``` research_guide__.docx ``` Plus: - Chat summary block: "Saved: . Audit: N searches × M unique papers / K cited. Plan tier: ." - Audit log printed inline if user asks for it ## Tooling | Script | Role | |---|---| | `scripts/citation_tracker.py` | JSON-backed three-count audit at `~/.litreview_sessions/.json` | | `scripts/framework_recommender.py` | Heuristic PICO/SPIDER/Decomposition suggestion from research question | | `scripts/cross_search_aggregator.py` | Repeat-hits + recurring-authors + citation-per-year ranking after Phase 3 | ## References - [`references/framework_selection.md`](references/framework_selection.md) — PICO / SPIDER / Decomposition canon (7+ sources) - [`references/search_budget_allocation.md`](references/search_budget_allocation.md) — depth tiers + cross-search intelligence + sequential execution rationale (7+ sources) - [`references/docx_8_sections.md`](references/docx_8_sections.md) — research guide DOCX spec + technical requirements (7+ sources) ## Anti-Patterns To Reject - Parallelizing Consensus calls - Skipping the interactive checkpoint (running all searches without user confirmation) - Padding thin results with training knowledge - Defaulting to non-PICO framework without justification - Citing papers in chat that didn't come from Consensus this session - Hardcoding plan tier instead of detecting from first response - Skipping era-gated searches in standard/deep budgets - Skipping cross-search intelligence (repeat-hits, recurring authors) - Truncating Consensus URLs in hyperlinks --- **Version:** 1.0.0 **Source spec:** [`megaprompts/09-litreview-megaprompt.md`](../../../../megaprompts/09-litreview-megaprompt.md) **Build pattern:** Path B (direct conversion). Sibling of `pulse` (research-pack shape).