Researchclaw

ResearchClaw Skill — Autonomous Research Pipeline

This skill wraps AutoResearchClaw, a 23-stage pipeline that takes a research topic and produces a conference-grade LaTeX paper with real citations, sandbox-executed experiments, multi-agent peer review, and citation verification.

Honesty policy: This skill does not fabricate capabilities. Every command maps to real upstream functionality. If something fails, the skill reports the actual error and suggests concrete fixes — it never pretends the problem does not exist.

Commands

/researchclaw — Help

When invoked without a subcommand, display this command list and a one-line status summary:

1. Check if researchclaw CLI is installed: which researchclaw
2. Check if config.yaml exists in the current directory
3. Print the command table above
4. Suggest the most logical next step based on what is missing

/researchclaw:setup — Prerequisites Installation

MANDATORY: Ask the user before installing anything. Present what is missing and get explicit approval.

Run the prerequisite check script:

bash "${CLAUDE_SKILL_DIR}/scripts/check-prereqs.sh"

The script checks each dependency and outputs a JSON report. Based on the report:

1. Python 3.11+: Check python3 --version. If missing or too old, suggest pyenv install 3.11 or system package manager.
2. pip / uv: Check pip3 --version or uv --version. Suggest uv if not present (faster).
3. Docker: Check docker info. If Docker daemon is not running, tell the user honestly — this skill cannot start Docker for you on most systems.
4. LaTeX: Check pdflatex --version. If missing, suggest sudo apt-get install texlive-full (Linux) or brew install --cask mactex (macOS). Be honest: this is a large download (2-4 GB).
5. AutoResearchClaw: Check pip3 show researchclaw. If not installed:

   pip3 install researchclaw
   

   Or from source:

   git clone https://github.com/aiming-lab/AutoResearchClaw.git
   cd AutoResearchClaw
   pip3 install -e ".[all]"
   

After installation, re-run the check script to verify everything passes.

What this skill CANNOT do:

• Start the Docker daemon (requires system-level access)
• Install LaTeX without sudo on Linux
• Fix network/firewall issues blocking API access
• Provide LLM API keys — the user must supply their own

/researchclaw:config — Interactive Configuration Wizard

Generate a working config.yaml by asking the user a series of questions. Use AskUserQuestion for each batch.

Batch 1 — Essential settings (MUST ask):

1. Research topic: What do you want to research? (free text)
2. LLM provider: Which LLM API? Options: openai, anthropic, azure, deepseek, local
3. API key: Provide your API key, or the environment variable name that holds it (e.g., OPENAI_API_KEY)
4. Model: Which model? Suggest defaults per provider:
   • openai: gpt-4o
   • anthropic: claude-sonnet-4-20250514
   • deepseek: deepseek-chat

Batch 2 — Experiment settings (ask with smart defaults):

5. Experiment mode: simulated (no code execution, fastest), sandbox (local execution), or ssh_remote (GPU server). Default: simulated
6. Auto-approve gates: Skip human approval at stages 5, 9, 20? Default: true for first run
7. Output directory: Where to save artifacts. Default: artifacts/

Batch 3 — Optional advanced settings (offer but don't require):

8. Paper template: neurips, icml, iclr, or generic. Default: neurips
9. Max iterations: For iterative pipeline mode. Default: 3
10. Literature sources: arxiv, semantic_scholar, or both. Default: both

After collecting answers, generate config.yaml using the template in assets/config-template.yaml. Write it to the current directory and show the user the generated file.

Validation: After generating, run:

researchclaw validate --config config.yaml

If validation fails, explain what went wrong and offer to fix it.

/researchclaw:run — Execute the Pipeline

Pre-flight checks (always run before starting):

1. Run /researchclaw:validate logic silently
2. If any check fails, report it and ask the user whether to proceed or fix first

Start the pipeline:

researchclaw run --topic "$ARGUMENTS" --config config.yaml --auto-approve 2>&1 | tee researchclaw-run.log

If $ARGUMENTS is empty, read the topic from config.yaml.

During execution:

• The pipeline runs 23 stages. Each stage produces output in artifacts/<run-id>/stage-N/
• Monitor progress by checking which stage directories exist
• If the pipeline fails, capture the error output and run /researchclaw:diagnose logic automatically

After completion:

• Report which stages succeeded and which failed
• Show the path to the generated paper (typically artifacts/<run-id>/stage-17/paper_draft.md or the final PDF)
• Show total execution time

/researchclaw:status — Pipeline Status

Check the current state of a pipeline run:

ls -la artifacts/ 2>/dev/null | tail -5

For the most recent run:

1. Find the latest artifacts/rc-* directory
2. Count completed stages: ls -d artifacts/rc-*/stage-* 2>/dev/null | wc -l
3. Check for pipeline_summary.json — if it exists, the run is complete
4. If no summary exists, check which stage was last modified to estimate current progress
5. Report: Stage X/23 complete. Current stage: [stage name]. Status: [running/failed/complete]

Stage name mapping (for human-readable output):

/researchclaw:resume — Resume a Failed Run

Resume from the last successful stage:

1. Find the latest run directory: ls -td artifacts/rc-* | head -1
2. Find the last completed stage: check pipeline_summary.json or find the highest-numbered stage-* directory with output files
3. Determine the next stage name from the stage mapping above
4. Run:

   researchclaw run --config config.yaml --from-stage STAGE_NAME --output <run-dir> --auto-approve 2>&1 | tee researchclaw-resume.log
   

Known issue (upstream): The --from-stage flag may not work correctly in all versions. If resume fails, inform the user honestly and suggest:

• Starting a fresh run
• Manually copying successful stage outputs to a new run directory

/researchclaw:diagnose — Auto-Diagnose Failures

Read the most recent log and error output to identify the problem:

tail -100 researchclaw-run.log 2>/dev/null || tail -100 researchclaw-resume.log 2>/dev/null

Common failure patterns and fixes:

After diagnosis, suggest the specific fix. If the fix is automatable (e.g., installing a package), offer to do it with user approval.

/researchclaw:validate — Pre-Run Validation

Run all checks without starting the pipeline:

bash "${CLAUDE_SKILL_DIR}/scripts/check-prereqs.sh"

Then additionally:

1. Config syntax: python3 -c "import yaml; yaml.safe_load(open('config.yaml'))"
2. Config completeness: Check that llm.api_key or llm.api_key_env is set, research.topic is non-empty
3. API connectivity: Test the LLM endpoint with a minimal request
4. Docker health: docker info (if experiment mode is sandbox)
5. Disk space: df -h . — warn if less than 10 GB free
6. Write permissions: touch artifacts/.write-test && rm artifacts/.write-test

Report results as a checklist with pass/fail for each item.

Additional Resources

• For the full pipeline stage reference, see references/pipeline-stages.md
• For configuration field reference, see references/config-reference.md
• For troubleshooting recipes, see references/troubleshooting.md
• For Chinese documentation, see references/README-CN.md

Principles

1. Never lie. If something is broken, say so. If a feature does not exist upstream, do not pretend it does.
2. Always test. Run validation before every pipeline execution. Check results after every action.
3. Ask before acting. Never install packages, modify configs, or start long-running processes without explicit user approval.
4. Report honestly. Show actual error messages, not sanitized summaries. The user needs real information to debug.
5. Stay current. This skill targets AutoResearchClaw v0.3.x. If the upstream version changes significantly, some commands may need updating.