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:

   ls config.yaml || ls config.researchclaw.example.yaml
   

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

   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)

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,
)