功能描述

Integrates AutoResearchClaw to autonomously generate conference-ready academic papers from user research topics with real citations and experimental code.

使用说明 (SKILL.md)

ResearchClaw

Name: AutoResearchClaw Integration
Author: nffdasilva

AutoResearchClaw is a fully autonomous 23-stage research pipeline that transforms a single research idea into a conference-ready academic paper with real literature from OpenAlex, Semantic Scholar, and arXiv.

Quick Start

Basic Usage

User says: "Research [topic]"

Agent workflow:

Check if AutoResearchClaw is installed (which researchclaw)
If not installed: clone, setup venv, install with pip install -e .
Copy config.researchclaw.example.yaml → config.arc.yaml
Ask user for LLM provider choice (OpenAI-compatible or ACP agent)
Configure with API keys or ACP agent selection
Run: researchclaw run --topic "[topic]" --auto-approve
Monitor progress, return results from artifacts/rc-*/deliverables/

Configuration

Ask user for LLM backend preference:

Option 1: OpenAI-compatible API

llm:
  provider: "openai-compatible"
  base_url: "https://api.openai.com/v1"
  api_key_env: "OPENAI_API_KEY"  # or ask for key
  primary_model: "gpt-4o"
  fallback_models: ["gpt-4o-mini"]

Option 2: ACP Agent (Claude Code, Codex, Gemini)

llm:
  provider: "acp"
  acp:
    agent: "claude"  # or "codex", "gemini", etc.
    cwd: "."

Installation

Check Installation

which researchclaw || echo "Not installed"

Install AutoResearchClaw

cd ~
git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Verify Installation

researchclaw --version

Running Research

Basic Command

researchclaw run --topic "Your research idea" --auto-approve

With Specific Config

researchclaw run --config config.arc.yaml --topic "Your research idea" --auto-approve

Output Location

Results in: ~/AutoResearchClaw/artifacts/rc-YYYYMMDD-HHMMSS-\x3Chash>/deliverables/

Deliverables

After completion, the agent should:

Check deliverables/ directory contents
Present key outputs:
- paper.tex - Conference-ready LaTeX
- paper_draft.md - Markdown paper
- references.bib - Real citations
- verification_report.json - Citation integrity check
- runs/ - Experimental code and results
- charts/ - Generated figures
- reviews.md - Multi-agent peer review
Copy/present relevant sections to user

Pipeline Stages (23 Total)

Phase A: Research Scoping

Stage 1: TOPIC_INIT
Stage 2: PROBLEM_DECOMPOSE

Phase B: Literature Discovery

Stage 3: SEARCH_STRATEGY
Stage 4: LITERATURE_COLLECT
Stage 5: LITERATURE_SCREEN [gate]
Stage 6: KNOWLEDGE_EXTRACT

Phase C: Knowledge Synthesis

Stage 7: SYNTHESIS
Stage 8: HYPOTHESIS_GEN

Phase D: Experiment Design

Stage 9: EXPERIMENT_DESIGN [gate]
Stage 10: CODE_GENERATION
Stage 11: RESOURCE_PLANNING

Phase E: Experiment Execution

Stage 12: EXPERIMENT_RUN
Stage 13: ITERATIVE_REFINE
Stage 14: RESULT_ANALYSIS
Stage 15: RESEARCH_DECISION

Phase F: Analysis & Decision

Stage 16: PAPER_OUTLINE
Stage 17: PAPER_DRAFT
Stage 18: PEER_REVIEW
Stage 19: PAPER_REVISION

Phase G: Paper Writing

Stage 20: QUALITY_GATE [gate]
Stage 21: KNOWLEDGE_ARCHIVE
Stage 22: EXPORT_PUBLISH
Stage 23: CITATION_VERIFY

Hardware Awareness

AutoResearchClaw auto-detects:

NVIDIA CUDA (GPU)
Apple MPS (M1/M2/M3)
CPU-only fallback

Adapts code generation, imports, and experiment scale accordingly.

Quality Features

Real Citations: OpenAlex, Semantic Scholar, arXiv - no hallucinated references
4-Layer Verification: arXiv ID → CrossRef DOI → Semantic Scholar → LLM relevance
Multi-Agent Debate: Hypothesis generation, result analysis, peer review
Self-Healing: NaN/Inf detection, automatic code repair
Conference Templates: NeurIPS, ICLR, ICML support

OpenClaw Bridge Integration (Optional)

Enable in config.arc.yaml:

openclaw_bridge:
  use_cron: true          # Scheduled research runs
  use_message: true       # Progress notifications (Discord/Slack/Telegram)
  use_memory: true        # Cross-session knowledge persistence
  use_sessions_spawn: true # Parallel sub-sessions
  use_web_fetch: true     # Live web search during literature review
  use_browser: false      # Browser-based paper collection

MetaClaw Integration (Optional)

For cross-run learning:

metaclaw_bridge:
  enabled: true
  skills_dir: "~/.metaclaw/skills"
  lesson_to_skill:
    enabled: true
    min_severity: "warning"
    max_skills_per_run: 5

Troubleshooting

Installation Issues

# Check Python version
python3 --version  # Requires 3.8+

# Install dependencies
pip install -r requirements.txt

LLM API Errors

Verify OPENAI_API_KEY is set
Check API endpoint is accessible
Fallback models configured correctly

Sandbox Issues

Ensure Python path is correct: .venv/bin/python
Check allowed imports in config
Adjust memory limits if needed

Literature Collection Failures

Check internet connectivity
Semantic Scholar API key optional (higher rate limits)
OpenAlex should work without API key

Advanced Usage

Specify Research Domains

researchclaw run --topic "Your topic" --domains ml,nlp --auto-approve

Target Specific Conference

export:
  target_conference: "neurips_2025"  # neurips_2025 | iclr_2026 | icml_2026

Custom Prompts

prompts:
  custom_file: "custom_prompts.yaml"

Resources

GitHub: https://github.com/aiming-lab/AutoResearchClaw
Integration Guide: See AutoResearchClaw docs/integration-guide.md
Testing Guide: See AutoResearchClaw docs/TESTER_GUIDE.md
Discord: https://discord.gg/u4ksqW5P

Comparison with Superpowers

ResearchClaw: Academic research, literature review, paper writing, experimental validation
Superpowers: Software development, TDD, code review, production code

Use ResearchClaw for research/paper generation. Use Superpowers for production software implementation. They complement each other when researching then implementing findings.

安全使用建议

This skill will clone and install a third‑party GitHub project and then run an autonomous pipeline that may execute arbitrary code, use your LLM API keys, access the web, and create scheduled jobs or persistent memory. Before installing: (1) review the GitHub repo and pin to a known-good commit; (2) run the install inside an isolated environment (container or disposable VM) rather than on your main host; (3) do not hand over high-privilege or broad API keys — use least-privilege/test keys where possible; (4) avoid enabling cron/web-fetch/messaging/use_memory until you have inspected the code and understood what data is sent externally; (5) verify licensing, maintainer identity, and whether the project has reproducible releases and checksums; (6) if you need assurance, ask the skill author for a homepage, maintainer contact, or an audit of the repository. If you are not comfortable reviewing or sandboxing the code, do not install it.

功能分析

Type: OpenClaw Skill Name: autoresearchclaw-integration Version: 1.0.0 The `researchclaw` skill bundle (SKILL.md) instructs the agent to clone and install an external repository (github.com/aiming-lab/AutoResearchClaw) and execute a 23-stage pipeline that includes autonomous code generation and execution. It requests LLM API keys and suggests enabling high-risk features like cron jobs for persistence and sub-session spawning. While these actions are aligned with the stated goal of autonomous academic research, the reliance on external code execution and the request for persistence mechanisms represent significant security risks without further verification of the external repository.

能力评估

✓ Purpose & Capability

Name and description (autonomous research → paper with code/citations) align with the instructions: the SKILL.md explicitly instructs cloning, installing, and running an AutoResearchClaw pipeline that performs literature search, experiments, and paper generation. The capabilities requested (LLM keys, web fetch, messaging) are plausible for the stated purpose.

⚠ Instruction Scope

Runtime instructions direct the agent to git-clone a GitHub repo, create a virtualenv, pip install the project, run a 23-stage autonomous pipeline with --auto-approve, and optionally enable cron, messaging (Discord/Slack/Telegram), cross-session memory and web fetch. That scope allows executing arbitrary downloaded code, performing network traffic, scheduling recurring jobs, and persisting data — all broader than a simple 'paper-writing helper' and could exfiltrate data or run unexpected experiments if the repo is malicious or buggy.

⚠ Install Mechanism

There is no vetted install spec in the registry; SKILL.md instructs cloning https://github.com/aiming-lab/AutoResearchClaw.git and pip installing it in editable mode. Downloading and installing unpinned code from a third-party GitHub repo is a high-risk install pattern (no pinned commit or checksum), because it writes and executes arbitrary code on the user's machine.

⚠ Credentials

Registry metadata declares no required env vars, but the instructions require/ask for LLM API keys (OPENAI_API_KEY or ACP credentials) and optionally Semantic Scholar or messaging credentials. Requesting these keys is plausible for the tool's features, but the mismatch with declared requirements and the potential need for additional messaging/cron credentials (Discord/Slack/Telegram tokens) increases risk and attack surface. The skill also encourages enabling persistent memory and scheduled runs which will retain and reuse credentials or outputs.

⚠ Persistence & Privilege

The skill itself is not marked always:true, but it instructs enabling features that grant persistent presence (cron scheduled runs, cross-session memory, messaging notifications, spawning sub-sessions, writing to ~/.metaclaw/skills). Those behaviors introduce longer-term persistence and automated execution beyond a single interactive run and should be treated cautiously because they expand the blast radius if the installed code is malicious or vulnerable.

版本历史

v1.0.0

Initial release - OpenClaw integration for AutoResearchClaw: autonomous research from idea to paper

元数据

Slug autoresearchclaw-integration

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题

AutoResearchClaw Integration 是什么？

Integrates AutoResearchClaw to autonomously generate conference-ready academic papers from user research topics with real citations and experimental code. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 400 次。

如何安装 AutoResearchClaw Integration？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install autoresearchclaw-integration」即可一键安装，无需额外配置。

AutoResearchClaw Integration 是免费的吗？

是的，AutoResearchClaw Integration 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

AutoResearchClaw Integration 支持哪些平台？

AutoResearchClaw Integration 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 AutoResearchClaw Integration？

由 nffdasilva（@nffdasilva）开发并维护，当前版本 v1.0.0。

AutoResearchClaw Integration