Skill: dongsheng123132/researchclaw (version 1.0.0)

--- name: researchclaw version: 1.0.0 description: | Autonomous research pipeline skill for Claude Code. Given a research topic, orchestrates 23 stages end-to-end: literature review, hypothesis generation, experiment design, code generation & sandbox execution, statistical analysis, paper writing, multi-agent peer review, and conference-ready LaTeX export. No babysitting required. tags: ["research", "paper-writing", "autonomous", "pipeline", "academic", "latex", "experiment"] --- # ResearchClaw — Autonomous Research Pipeline Skill ## Description Run ResearchClaw's 23-stage autonomous research pipeline. Given a research topic, this skill orchestrates the entire research workflow: literature review → hypothesis generation → experiment design → code generation & execution → result analysis → paper writing → peer review → final export. ## Trigger Conditions Activate this skill when the user: - Asks to "research [topic]", "write a paper about [topic]", or "investigate [topic]" - Wants to run an autonomous research pipeline - Asks to generate a research paper from scratch - Mentions "ResearchClaw" by name ## Instructions ### Prerequisites Check 1. Verify config file exists: ```bash ls config.yaml || ls config.researchclaw.example.yaml ``` 2. If no `config.yaml`, create one from the example: ```bash cp config.researchclaw.example.yaml config.yaml ``` 3. Ensure the user's LLM API key is configured in `config.yaml` under `llm.api_key` or via `llm.api_key_env` environment variable. ### Running the Pipeline **Option A: CLI (recommended)** ```bash researchclaw run --topic "Your research topic here" --auto-approve ``` Options: - `--topic` / `-t`: Override the research topic from config - `--config` / `-c`: Config file path (default: `config.yaml`) - `--output` / `-o`: Output directory (default: [REDACTED_SECRET]) - `--from-stage`: Resume from a specific stage (e.g., `PAPER_OUTLINE`) - `--auto-approve`: Auto-approve gate stages (5, 9, 20) without human input **Option B: Python API** ```python from researchclaw.pipeline.runner import execute_pipeline from researchclaw.config import RCConfig from researchclaw.adapters import AdapterBundle from pathlib import Path config = RCConfig.load("config.yaml", check_paths=False) results = execute_pipeline( run_dir=Path("artifacts/my-run"), run_id="research-001", config=config, adapters=AdapterBundle(), auto_approve_gates=True, ) # Check results for r in results: print(f"Stage {r.stage.name}: {r.status.value}") ``` **Option C: Iterative Pipeline (multi-round improvement)** ```python from researchclaw.pipeline.runner import execute_iterative_pipeline results = execute_iterative_pipeline( run_dir=Path("artifacts/my-run"), run_id="research-001", config=config, adapters=AdapterBundle(), max_iterations=3, convergence_rounds=2, ) ``` ### Output Structure After a successful run, the output directory contains: ``` artifacts/<run-id>/ ├── stage-1/ # TOPIC_INIT outputs ├── stage-2/ # PROBLEM_DECOMPOSE outputs ├── ... ├── stage-10/ │ └── experiment.py # Generated experiment code ├── stage-12/ │ └── runs/run-1.json # Experiment execution results ├── stage-14/ │ ├── experiment_summary.json # Aggregated metrics │ └── results_table.tex # LaTeX results table ├── stage-17/ │ └── paper_draft.md # Full paper draft ├── stage-22/ │ └── charts/ # Generated visualizations │ ├── metric_trajectory.png │ └── experiment_comparison.png └── pipeline_summary.json # Overall pipeline status ``` ### Experiment Modes | Mode | Description | Config | |------|-------------|--------| | `simulated` | LLM generates synthetic results (no code execution) | `experiment.mode: simulated` | | `sandbox` | Execute generated code locally via subprocess | `experiment.mode: sandbox` | | `ssh_remote` | Execute on remote GPU server via SSH | `experiment.mode: ssh_remote` | ### Troubleshooting - **Config validation error**: Run `researchclaw validate --config config.yaml` - **LLM connection failure**: Check `llm.base_url` and API key - **Sandbox execution failure**: Verify `experiment.sandbox.python_path` exists and has numpy installed - **Gate rejection**: Use `--auto-approve` or manually approve at stages 5, 9, 20 ## Tools Required - File read/write (for config and artifacts) - Bash (for CLI execution) - No external MCP servers required for basic operation


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)