internal/embed/claude/skills/literature-review/SKILL.md
Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
npx skillsauth add moralespanitz/research-loop literature-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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PLAN → GATHER (parallel researcher subagents) → SYNTHESIZE → CITE (verifier) → VERIFY (reviewer) → DELIVER
Derive a short slug from the topic (lowercase, hyphens, no filler words, ≤5 words). Use this slug for all files.
Create .research-loop/sessions/<slug>/plan.md with:
Write the plan and continue immediately. Summarize briefly. Do not ask for confirmation.
Dispatch researcher subagents (.claude/agents/researcher.md) for each search angle. For a typical literature review, use 3–4 subagents in parallel:
| Agent | Task | |-------|------| | researcher | Search academic papers: foundational works, recent advances, surveys | | researcher | Search official documentation, benchmarks, and implementation repos | | researcher | Search for field disagreements, unresolved debates, open problems | | researcher (if broad scope) | Search adjacent fields, cross-domain applications, alternative approaches |
Each researcher writes to .research-loop/sessions/<slug>/findings-<angle>.md.
Each researcher must produce an evidence table with:
| # | Source | URL | Key claim | Type | Quality | |---|--------|-----|-----------|------|---------| | 1 | ... | ... | ... | primary / secondary / self-reported | A / B / C |
| Tier | Description | Examples | |------|-------------|---------| | A (Highest) | Peer-reviewed papers, official documentation, verified benchmarks, primary datasets, government filings | arXiv proceedings, IEEE/ACM, official docs | | B (Good) | Reputable secondary sources, expert technical blogs, well-cited surveys, established trade publications | Distill.pub, high-quality blog posts | | C (Accept with caveats) | Undated posts, content aggregators, social media with primary links, vendor claims without independent verification | Listicles, Medium posts without citations | | Reject | No author + no date, AI-generated content without primary backing, anonymous claims | — |
When initial results skew toward low-quality sources, re-search with domain filters targeting authoritative domains (.edu, .gov, arXiv, ACL, NeurIPS, etc.).
includeContent: true for top candidates only; triage by title/snippet first.done, blocked, or needs follow-up.Write the synthesis yourself. Do not delegate to subagents.
Save to .research-loop/sessions/<slug>/draft.md.
Structure:
Synthesis rules:
Dispatch the verifier agent (.claude/agents/verifier.md) to add inline citations and verify every source URL.
The verifier will:
[N] in body maps to Sources, every Sources entry is cited at least onceSave the cited draft to .research-loop/sessions/<slug>/draft-cited.md.
Dispatch the reviewer agent (.claude/agents/reviewer.md) to check the cited draft for:
The reviewer classifies issues as:
Fix FATAL issues before delivery. If FATAL issues were found, run one more verification pass after the fixes.
Save the final literature review to .research-loop/sessions/<slug>/report.md.
Write a provenance sidecar to .research-loop/sessions/<slug>/provenance.md:
# Provenance: Literature Review — [topic]
- **Date:** [date]
- **Search angles:** [list of angles]
- **Sources consulted:** [count and/or list]
- **Sources accepted:** [count]
- **Sources rejected:** [count] — [reasons]
- **Verification:** [PASS / PASS WITH NOTES / BLOCKED]
- **Plan:** .research-loop/sessions/<slug>/plan.md
- **Research files:** [files used]
Before responding, verify on disk that both report.md and provenance.md exist.
| Agent | When dispatched | |-------|-----------------| | researcher | Step 2 — evidence gathering across parallel angles | | verifier | Step 4 — citation verification and URL checking | | reviewer | Step 5 — review pass on cited draft |
All subagent definitions live in .claude/agents/.
testing
Plan and execute a structured replication workflow for a paper, claim, or benchmark with environment selection and integrity checks.
testing
End-to-end paper generation pipeline ported from AutoResearchClaw (Aiming Lab). 14 phases covering topic initiation through export/publish, with human- in-the-loop gates and quality gating at each handoff. Use this when the user wants a full paper pipeline run — topic to submission-ready manuscript. Delegates to researcher/reviewer/writer/verifier subagents for stage execution and to autonomous-iteration for experiment optimization loops.
testing
Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
development
Publication-quality figure generation for research papers. Decision agent selects figure type (code plot vs architecture diagram). Generates Matplotlib/Seaborn code for quantitative figures with iterative improvement loop. Style-matches conference templates (NeurIPS, ICML, ICLR). Use when the paper-pipeline reaches the figure generation phase, or when a user requests figures for an existing draft.