hikaru122700/cim-cac-maxcut-g22/researchclaw

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 ``

1. If no config.yaml, create one from the example:

``bash cp config.researchclaw.example.yaml config.yaml ``

1. 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)

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: artifacts/rc-YYYYMMDD-HHMMSS-HASH/)
• --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

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)

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

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