Restart Cursor to activate autoresearchclaw-autonomous-research. Access via /autoresearchclaw-autonomous-research in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
AutoResearchClaw is a fully autonomous 23-stage research pipeline that takes a natural language topic and produces a complete academic paper: real arXiv/Semantic Scholar citations, sandboxed experiments, statistical analysis, multi-agent peer review, and conference-ready LaTeX (NeurIPS/ICML/ICLR). No hallucinated references. No human babysitting.
Installation
# Clone and installgit clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
python3 -m venv .venv &&source .venv/bin/activate
pip install-e.# Verify CLI is availableresearchclaw --help
project:name:"my-research"research:topic:"Your research topic here"llm:provider:"openai"base_url:"https://api.openai.com/v1"api_key_env:"OPENAI_API_KEY"primary_model:"gpt-4o"fallback_models:["gpt-4o-mini"]experiment:mode:"sandbox"sandbox:python_path:".venv/bin/python"
The agent CLI (e.g. claude) handles its own authentication.
OpenClaw bridge (optional advanced capabilities)
openclaw_bridge:use_cron:true# Scheduled research runsuse_message:true# Progress notificationsuse_memory:true# Cross-session knowledge persistenceuse_sessions_spawn:true# Parallel sub-sessionsuse_web_fetch:true# Live web search in literature reviewuse_browser:false# Browser-based paper collection
Key CLI Commands
# Basic run — fully autonomous, no promptsresearchclaw run --topic"Your research idea" --auto-approve
# Run with explicit config fileresearchclaw run --config config.arc.yaml --topic"Mixture-of-experts routing efficiency" --auto-approve
# Run with topic defined in config (omit --topic flag)researchclaw run --config config.arc.yaml --auto-approve
# Interactive mode — pauses at gate stages for approvalresearchclaw run --config config.arc.yaml --topic"Your topic"# Check pipeline status / resume a runresearchclaw status --run-id rc-20260315-120000-abc123
# List past runsresearchclaw list
Gate stages (5, 9, 20) pause for human approval in interactive mode. Pass --auto-approve to skip all gates.
Python API
from researchclaw.pipeline import Runner
from researchclaw.config import load_config
# Load config and runconfig = load_config("config.arc.yaml")config.research.topic ="Efficient attention mechanisms for long-context LLMs"config.auto_approve =Truerunner = Runner(config)result = runner.run()# Access outputsprint(result.artifact_dir)# artifacts/rc-YYYYMMDD-HHMMSS-<hash>/print(result.deliverables_dir)# .../deliverables/print(result.paper_draft_path)# .../deliverables/paper_draft.mdprint(result.latex_path)# .../deliverables/paper.texprint(result.bibtex_path)# .../deliverables/references.bibprint(result.verification_report)# .../deliverables/verification_report.json
# Run specific stages onlyfrom researchclaw.pipeline import Runner, StageRange
runner = Runner(config)result = runner.run(stages=StageRange(start="LITERATURE_COLLECT", end="KNOWLEDGE_EXTRACT"))
# Access knowledge base after a runfrom researchclaw.knowledge import KnowledgeBase
kb = KnowledgeBase.load(result.artifact_dir)findings = kb.get("findings")literature = kb.get("literature")decisions = kb.get("decisions")
Output Structure
After a run, all outputs land in artifacts/rc-YYYYMMDD-HHMMSS-<hash>/:
exportOPENAI_API_KEY="$OPENAI_API_KEY"researchclaw run \--topic"Self-supervised learning for protein structure prediction"\ --auto-approve
Pattern: Reproducible run with full config
# config.arc.yamlproject:name:"protein-ssl-research"research:topic:"Self-supervised learning for protein structure prediction"llm:provider:"openai"api_key_env:"OPENAI_API_KEY"primary_model:"gpt-4o"fallback_models:["gpt-4o-mini"]experiment:
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
