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literature-review
Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when user mentions autonomous iteration, metric-driven optimization, $research-loop plan, $research-loop debug, $research-loop fix, $research-loop security, $research-loop ship, $research-loop scenario, $research-loop predict, $research-loop learn, $research-loop reason, $research-loop probe, or mentions "research-loop" with a goal/metric. Autonomous Goal-directed Iteration — apply Karpathy's autoresearch principles: modify, verify, keep/discard, repeat. Supports bounded mode via Iterations: N inline config.
Mandatory activation layer — loads on any conversation start. Establishes skill-loading protocol, Red Flags, priority rules, and HARD-GATE enforcement for all research-loop skills.
Run a thorough, multi-phase deep research investigation on a topic with subagent dispatch, provenance tracking, and integrity verification.
Experiment sandbox execution for Research Loop. Supports four modes: local (venv), Docker (isolated containers), SSH remote (GPU compute on servers), and Colab (Google Drive bridge). Provides experiment harness templates, code validation, metric collection, deterministic seeding, and compute budget enforcement. Use before running experiments generated by the paper-pipeline.
Use when user wants to explore a topic, find papers, map a field, or understand the research landscape. Not for explaining concepts — use learn for that.
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.
| name | literature-review |
| description | Run a structured literature review on a topic using parallel search, evidence tables with quality scoring, and primary-source synthesis. |
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/.