AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
Borrowing it
Nothing to install: this file belongs to aiming-lab/AutoResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aiming-lab/AutoResearchClaw/main/.claude/skills/literature-search/SKILL.mdgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aiming-lab/autoresearchclaw/literature-search)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/literature-search"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/literature-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/literature-search"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/literature-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 57 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.00625 |
| Opus 5.5 | $0.00011 | $0.00250 |
| Sonnet 5.5 | $0.00006 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
literature-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Search Best Practice
Search Strategy Design
- Define research question using PICO framework (Population, Intervention, Comparison, Outcome)
- Identify 2-4 core concepts from the research question
- List synonyms, abbreviations, and related terms for each concept
- Combine terms with Boolean operators: AND (between concepts), OR (within synonyms)
- Select at least 3 complementary databases relevant to the domain:
- Biomedical: PubMed, Scopus, Web of Science
- Computer science: arXiv, Semantic Scholar, DBLP, ACL Anthology
- Interdisciplinary: Google Scholar, OpenAlex
- Document exact search strings for reproducibility
Inclusion and Exclusion Criteria
- Define date range (e.g., last 5-10 years for rapidly evolving fields)
- Specify language restrictions (typically English)
- Specify publication types (peer-reviewed, preprints, conference papers)
- Define study design requirements (RCTs, observational, computational)
- Set domain-specific filters (species, methodology, sample size)
- Document all criteria BEFORE screening begins
PRISMA Methodology
- Record total hits from each database before deduplication
- Remove duplicates and record count
- Screen titles and abstracts against inclusion criteria (record excluded count)
- Full-text review of remaining papers (record excluded with reasons)
- Report final included studies with PRISMA flow diagram
- For scoping reviews, use PRISMA-ScR extension
Screening and Quality Assessment
- Use two-pass screening: title/abstract first, then full text
- Apply quality assessment tools appropriate to study type:
- RCTs: Cochrane Risk of Bias tool
- Observational: Newcastle-Ottawa Scale
- ML papers: check reproducibility, dataset validity, statistical rigor
- Extract data systematically using a predefined extraction form
Synthesis Approaches
- Narrative synthesis: Organize findings thematically, identify patterns and contradictions
- Meta-analysis: Pool quantitative results when studies are sufficiently homogeneous
- Gap analysis: Explicitly identify what is NOT covered in the literature
- Summarize key findings per theme with supporting citation counts
- Highlight conflicting results and possible explanations
- End with clear statement of research gaps that motivate your study
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 58 lines · 28 tokens per session scan A a49eec3ab666
literature-search is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,822 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 625 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
literature-search compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| literature-searchaiming-lab/AutoResearchClaw | Skill | 15k | 2mo ago | MIT | A | 28 | Claude Code |
| aclawdemyLeoYeAI/openclaw-master-skills | Skill | 2.2k | 5d ago | MIT | A | 30 | Claude Code, Codex |
| adme-property-predictorLeoYeAI/openclaw-master-skills | Skill | 2.2k | 5d ago | MIT | A | 61 | Claude Code |
| arxiv-summarizer-orchestratorInternLM/WildClawBench | Skill | 534 | yesterday | MIT | A | 75 | Codex |
| nerf-to-3dgs-migratorjaccen/Awesome-Gaussian-Skills | Skill | 178 | today | Apache-2.0 | A | 106 | Claude Code |
| baseline-comparison-auditwanshuiyin/Anti-Autoresearch | Skill | 179 | 7d ago | MIT | A | 310 | Claude Code |
| eval-design-forensicswanshuiyin/Anti-Autoresearch | Skill | 179 | 7d ago | MIT | A | 458 | Claude Code |