Litreview

Litreview — Academic Literature Orientation

Portability: Works anywhere with outbound HTTPS — the default search lane is free keyless APIs (PubMed E-utilities + OpenAlex, no account, no key, no MCP). The Consensus MCP is an optional enhancement lane used only when connected in this session. Node.js with docx package is required for document generation, and (in CLI) bash_tool. Works in Claude Code CLI natively and in Claude.ai with Code Execution.

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.

Search Lanes

Lane check (one runtime check — replaces all tier detection): if the Consensus MCP tools are not available in this session, use the free lane — do not attempt tier detection, do not parse marketing copy, do not ask the user about their Consensus plan. If Consensus IS available, additionally run its searches and merge results (dedupe by DOI/title).

Free-lane URL templates (exact)

PubMed E-utilities (keyless; etiquette: ≤3 requests/second):

1. Search → PMIDs: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=<urlencoded-query>&retmode=json&retmax=20&sort=relevance — read esearchresult.idlist (PMIDs) and esearchresult.count.
2. PMIDs → metadata: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=<pmid1,pmid2,...>&retmode=json — per result[<pmid>] read title, authors[].name, pubdate, fulljournalname, articleids[] (the idtype: "doi" entry).
3. Era-gating: append &datetype=pdat&mindate=2021&maxdate=3000 (recent) or &maxdate=2015 (historical).
4. Paper URL: https://pubmed.ncbi.nlm.nih.gov/<PMID>/.

OpenAlex (keyless; add &mailto=<email> for the polite pool — faster + more reliable):

1. Search: https://api.openalex.org/works?search=<urlencoded-query>&per-page=20&mailto=<email> — per results[] read display_name (title), publication_year, cited_by_count, doi, id (OpenAlex URL), authorships[].author.display_name, primary_location.source.display_name (venue).
2. Era-gating: &filter=from_publication_date:2021-01-01 or &filter=to_publication_date:2015-12-31.
3. Review articles: &filter=type:review.

OpenAlex's cited_by_count is the citation-count source for the cross-search intelligence layer (PubMed esummary returns no counts).

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 papers returned by THIS session's searches (free lane and/or Consensus). Training knowledge labeled [Not from search — 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 by DOI/title) / papers cited. Every cited paper has a retrievable URL from this session (PubMed, DOI, OpenAlex, or Consensus). Use scripts/citation_tracker.py for deterministic counts.
• Rate-limit etiquette. PubMed E-utilities: ≤3 requests/second keyless. OpenAlex: polite pool via mailto. Consensus (if connected): 1 query/sec, sequential execution mandatory. Default discipline: sequential, 1 query/sec across all lanes.
• Retry policy. On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user, share what was collected.
• Lane check. One runtime check at session start: Consensus MCP tools available or not. No tier detection, ever.

See references/search_budget_allocation.md for the sequential-execution rationale + budget ceilings.

Error Handling

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 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 recon search to map themes, terminology, methodological distinctions.

• Run the lane check (Consensus available or not), then:
  • Free lane: python scripts/free_search.py --query "<broad version of Q1>" --source both --max 20 (or the esearch/works URL templates above)
  • If Consensus is available, additionally run one broad Consensus search and merge
• 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

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

Depth re-confirmation (forcing choice)

Surface the practical constraint: search lane in use (free / free+Consensus) + approximate ceiling at ~20 results per query per source.

• Quick scan (5 searches × ~20 results = ~100 papers max per source)
• Standard review (10 searches × ~20 = ~200 papers per source)
• Deep dive (20 searches × ~20 = ~400 papers per source)

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. Every search runs on the free lane (free_search.py or the URL templates); if Consensus is available, additionally run the same query there and merge. See 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]" (OpenAlex: add &filter=type:review)
• 2 era-gated searches (most important sub-area): historical (PubMed &maxdate=2015 / OpenAlex to_publication_date:2015-12-31) + recent (PubMed &mindate=2021 / OpenAlex from_publication_date:2021-01-01)
• 1 follow-up on highest-cited paper using its key terms + a from-date after its publication year

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 OpenAlex cited_by_count 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 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 a clickable link: PubMed URL or DOI (free lane) / "View on Consensus" (Consensus-sourced). Every inline citation matches a bibliography entry.
8. Audit Log — search summary table (#, query, filters, papers returned, status), counts block, coverage notes including which search lane was used (free / free+Consensus)

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 (zip-integrity check: python3 -c "import zipfile,sys; zipfile.ZipFile(sys.argv[1]).testzip()" output.docx — no output = intact — then confirm the required sections are present)

Reference the docx skill for setup patterns and best practices.

Output

research_guide_<topic-slug>_<YYYY-MM-DD>.docx

Plus:

• Chat summary block: "Saved: . Audit: N searches × M unique papers / K cited. Search lane: <free | free+Consensus>."
• Audit log printed inline if user asks for it

Tooling

References

• references/framework_selection.md — PICO / SPIDER / Decomposition canon (7+ sources)
• references/search_budget_allocation.md — depth tiers + cross-search intelligence + sequential execution rationale (7+ sources)
• references/docx_8_sections.md — research guide DOCX spec + technical requirements (7+ sources)

Anti-Patterns To Reject

• Parallelizing search calls (any lane)
• 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 this session's searches
• Attempting Consensus plan-tier detection (deleted — the only runtime check is "are the Consensus MCP tools available in this session?")
• Treating Consensus as required (it's an optional enhancement; the free lane is the default)
• Skipping era-gated searches in standard/deep budgets
• Skipping cross-search intelligence (repeat-hits, recurring authors)
• Truncating source URLs in hyperlinks

Version: 1.1.0 Source spec: megaprompts/09-litreview-megaprompt.md Build pattern: Path B (direct conversion). Sibling of pulse (research-pack shape). v1.1.0: free keyless APIs (PubMed + OpenAlex) became the default search lane; Consensus demoted to optional enhancement; plan-tier detection deleted per the 2026-06 newgen audit + ClawHub rule #3 (no paid-service dependencies).

Source: alirezarezvani/claude-skills → research/litreview/skills/litreview/SKILL.md