/SKILL.md
Paper-first autonomous research agent. Use when the user wants to (1) research a topic or scientific question, (2) write an academic paper, (3) run experiments to validate a hypothesis, (4) do a literature review or survey, (5) explore whether a phenomenon exists, (6) benchmark or compare approaches, or (7) any task involving "write a paper", "research this", "is this true", "validate this claim", "literature review", "run experiments". Writes the abstract and intro as the specification first, then grounds in literature, runs experiments, and judges results in a loop.
npx skillsauth add thepicklegawd/autoresearch-skill autoresearchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an autonomous research agent. You write the paper first — the abstract and intro are the specification. Experiments validate claims.
You are a long-running agent. Do NOT stop after creating files. Execute the full workflow.
All research state lives in .autoresearch/ in the user's project:
.autoresearch/
├── paper/ # paper directory
│ ├── paper.md # default (or main.tex if LaTeX)
│ └── references.bib # living bibliography
├── state.md # current snapshot (rewritten, not appended)
├── refs/ # downloaded arxiv papers as context (gitignored)
├── reports/ # timestamped phase reports
├── settings.md # project preferences
├── log.jsonl # all activity across phases and agents
└── scratch/ # experimental scratch work (gitignored)
On first run, create this structure. Add to .gitignore:
.autoresearch/refs/
.autoresearch/scratch/
Read .autoresearch/settings.md for project preferences. If it doesn't exist, create it with defaults and ask the user what they want to change.
Default settings.md:
# Research Settings
- Paper: .autoresearch/paper/
- Env: uv, python 3.11
- Phases: ground, specify, experiment, judge
- Notes: (none)
Four settings, that's it:
uv, python 3.11, conda, cuda 12.1, pip, docker)Four phases. Read the detailed protocol from ${CLAUDE_SKILL_DIR}/phases/<phase>.md before executing each one.
| Phase | What happens | Pauses for user? |
|-------|-------------|-----------------|
| ground | Search literature, download key papers to refs/, build references.bib | Yes — user confirms gap and direction |
| specify | Co-write abstract + intro, citing references.bib | Yes — user approves spec |
| experiment | Run experiments (code in repo, outputs in scratch/), log to log.jsonl | No — runs autonomously |
| judge | Evaluate results against paper claims, decide next action | Only if verdict is PIVOT |
Experiment → judge loops until the judge passes.
references.bib in the paper directory is maintained across all phases. Rules:
[@citekey] in markdown or \cite{citekey} in LaTeXnote = {TO VERIFY} to the bib entry{firstauthor}{year}{keyword} (e.g., vaswani2017attention).autoresearch/refs/ for full-text contextAfter completing each phase, write a report to .autoresearch/reports/YYYY-MM-DD/<phase>/report.md. For phases that loop (experiment, judge), number subsequent reports: report_2.md, report_3.md. Additional artifacts (figures, data, tables) go in the same folder.
Reports are grounded in the research intention — always tie back to what the user set out to show:
These are for the user to quickly judge whether the research is on track.
.autoresearch/state.md is the working memory. Rewrite it (don't append) after every phase completion or significant change. It should always reflect current reality:
Read state.md first when resuming. It's faster than parsing the full log.
Append to .autoresearch/log.jsonl after every significant action:
{"time":"ISO-8601","phase":"ground","action":"searched sparse attention papers","result":"found 12 relevant papers","refs_added":["child2019sparse"]}
Read the log before acting to avoid repeating work.
Commit at phase boundaries. Prefix with [autoresearch] so research history is easy to filter (git log --grep="autoresearch").
When to commit:
[autoresearch] setup — <topic>[autoresearch] ground — <gap found, key insight>[autoresearch] specify — <main claim, N targets>[autoresearch] judge — <verdict, why>[autoresearch] experiment — <what changed, result>Don't commit every failed experiment attempt — log.jsonl and state.md track that.
/autoresearch "question"):Step 1: Scan the repo. Before asking anything, silently survey the project:
**/*.tex, **/*.bib, **/paper/, **/*.sty, **/*.cls**/*.py, **/*.ipynb, **/requirements.txt, **/pyproject.toml.autoresearch/README.md, CLAUDE.md, AGENTS.md if they existStep 2: Ask setup questions. Based on what you found, ask the user (all at once, not one by one):
.tex files → "Use this as the working paper?" / Nothing found → "Write in markdown (recommended) or import a LaTeX project (e.g., NeurIPS/COLM zip)?"uv.lock, requirements.txt, pyproject.toml, environment.yml, Dockerfile → confirm. Nothing found → "What tools? (uv recommended, python version, cuda, docker, etc.)"Step 3: Set up. Based on answers:
.autoresearch/ structure (refs/, reports/, scratch/, log.jsonl)paper.md from ${CLAUDE_SKILL_DIR}/templates/paper.md + empty references.bib. If LaTeX: use detected template or tell user to extract their conference zip into the paper directory.settings.md with all detected/confirmed values.autoresearch/refs/ and .autoresearch/scratch/ to .gitignoreCLAUDE.md (create if needed):
## Research
This project uses [autoresearch](https://github.com/ThePickleGawd/autoresearch-skill).
Current status: `.autoresearch/state.md`
Run `/autoresearch resume` to continue.
Step 4: Begin ground phase. Read ${CLAUDE_SKILL_DIR}/phases/ground.md — execute it now.
/autoresearch resume):.autoresearch/state.md — this tells you where things stand.autoresearch/settings.md for project configtesting
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Create, edit, improve, or audit AgentSkills. Use when creating a new skill from scratch or when asked to improve, review, audit, tidy up, or clean up an existing skill or SKILL.md file. Also use when editing or restructuring a skill directory (moving files to references/ or scripts/, removing stale content, validating against the AgentSkills spec). Triggers on phrases like "create a skill", "author a skill", "tidy up a skill", "improve this skill", "review the skill", "clean up the skill", "audit the skill".
testing
Host security hardening and risk-tolerance configuration for OpenClaw deployments. Use when a user asks for security audits, firewall/SSH/update hardening, risk posture, exposure review, OpenClaw cron scheduling for periodic checks, or version status checks on a machine running OpenClaw (laptop, workstation, Pi, VPS).