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

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":

PICO (Population / Intervention / Comparison / Outcome — most clinical questions)

SPIDER (Sample / Phenomenon / Design / Evaluation / Research-type — social/qualitative)

Decomposition (Problem / Solution / Evaluation / Limitations — technology-focused)

Hybrid (you pick which components from which framework)

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:

Quick scan (5 searches)

Standard review (10 searches)

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:

Repeat-hit papers — same paper appearing in 3+ sub-area searches = likely foundational
Recurring authors — same author in multiple searches = dominant research group; top 3-5 most frequent matter
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):

Topic Overview — single tight paragraph (4-6 sentences)
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"
How the Field Got Here — chronological narrative (1-2 paragraphs) + timeline table (5-8 milestones: Year / Milestone / Significance) + terminology evolution note
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)
Key Research Groups — top 3-5 authors/groups with affiliations, sub-area coverage, representative paper link (from cross-search aggregator)
Open Questions & Gaps — three categories: methodological / population-context / conceptual-theoretical. Each gap explains why it matters.
Bibliography — alphabetical by first author. Every entry has clickable "View on Consensus" link. Every inline citation matches a bibliography entry.
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: <path>. Audit: N searches × M unique papers / K cited. Plan tier: <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

```
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 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).

litreview

NPX Install

Tags

SKILL.md Content