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Litreview — Academic Literature Orientation

Research litreview Source

Install: claude /plugin install research-skills

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 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 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 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 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)
  5. 4a. What the Research Shows (2-3 sentence synthesis with inline citations)
  6. 4b. Key Papers (3-5 hyperlinked papers with citation count, year, 1-sentence importance)
  7. 4c. Key Search Terms (6-10 keywords, synonyms, MeSH, historical terms)
  8. 4d. Boolean Search Strings (2-3 ready-to-paste strings)
  9. Key Research Groups — top 3-5 authors/groups with affiliations, sub-area coverage, representative paper link (from cross-search aggregator)
  10. Open Questions & Gaps — three categories: methodological / population-context / conceptual-theoretical. Each gap explains why it matters.
  11. Bibliography — alphabetical by first author. Every entry has clickable "View on Consensus" link. Every inline citation matches a bibliography entry.
  12. 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_<topic-slug>_<YYYY-MM-DD>.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/<session>.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

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 Build pattern: Path B (direct conversion). Sibling of pulse (research-pack shape).