Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/research-litWrote 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/raja21068/autoresearch/research-lit)<a href="https://agentmods.dev/skills/raja21068/autoresearch/research-lit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-lit/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/raja21068/autoresearch/research-lit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-lit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00048 | $0.06260 |
| Opus 5 | $0.00024 | $0.03130 |
| Sonnet 5 | $0.00010 | $0.01252 |
| Haiku 4.5 | $0.00005 | $0.00626 |
Grade B, and why
research-lit scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/arxiv/ -name "arxiv_fetch.py" 2>/dev/null | head -1) How it starts
The opening of the file, as written. The whole thing — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Literature Review
Research topic: $ARGUMENTS
Constants
- REVIEWER_BACKEND =
codex— Default: Codex MCP (xhigh). Override with— reviewer: oracle-profor GPT-5.4 Pro via Oracle MCP. Seeshared-references/reviewer-routing.md. - PAPER_LIBRARY — Local directory containing user's paper collection (PDFs). Check these paths in order:
papers/in the current project directoryliterature/in the current project directory- Custom path specified by user in
CLAUDE.mdunder## Paper Library
- MAX_LOCAL_PAPERS = 20 — Maximum number of local PDFs to scan (read first 3 pages each). If more are found, prioritize by filename relevance to the topic.
- ARXIV_DOWNLOAD = false — When
true, download top 3-5 most relevant arXiv PDFs to PAPER_LIBRARY after search. Whenfalse(default), only fetch metadata (title, abstract, authors) via arXiv API — no files are downloaded. - ARXIV_MAX_DOWNLOAD = 5 — Maximum number of PDFs to download when
ARXIV_DOWNLOAD = true.
💡 Overrides:
/research-lit "topic" — paper library: ~/my_papers/— custom local PDF path/research-lit "topic" — sources: zotero, local— only search Zotero + local PDFs/research-lit "topic" — sources: zotero— only search Zotero/research-lit "topic" — sources: web— only search the web (skip all local)/research-lit "topic" — sources: web, semantic-scholar— also search Semantic Scholar for published venue papers (IEEE, ACM, etc.)/research-lit "topic" — sources: deepxiv— only search via DeepXiv progressive retrieval/research-lit "topic" — sources: all, deepxiv— use default sources plus DeepXiv/research-lit "topic" — arxiv download: true— download top relevant arXiv PDFs/research-lit "topic" — arxiv download: true, max download: 10— download up to 10 PDFs
Data Sources
This skill checks multiple sources in priority order. All are optional — if a source is not configured or not requested, skip it silently.
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.
- 8d ago First seen · 427 lines · 48 tokens per session scan B a2af2b3e24f8
research-lit is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 6,260 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…