Deep Research for OpenClaw

Install and wire a structured OpenClaw deep-research sub-agent with hybrid search, artifact-based runs, claim verification, report linting, and validated finalization.

What this skill is

This is an integration skill for installing and wiring the deep-researcher OpenClaw sub-agent from the public repository:

• deep-research-openclaw-agent

The repository contains:

• the workspace-researcher prompt pack;
• the local research helper scripts;
• the Main -> Deep Research orchestration contract;
• the report lint, validation, and finalization pipeline.

This skill is intended for OpenClaw users who want a reproducible deep-research workflow without assembling the runtime and contracts from scratch.

What it can do

• structured deep research through plan -> scout -> harvest -> verify -> synthesize;
• hybrid discovery with web_search, Tavily, and web_fetch;
• explicit source registry, claim ledger, and coverage tracking;
• report linting, validation, and final M2M JSON finalization;
• honest SUCCESS | PARTIAL | FAILURE delivery with explicit gaps and conflicts.

Requirements

• OpenClaw 2026.3.x or later
• Python available on the host
• a configured deep-researcher agent in OpenClaw
• Tavily API access if you want the Tavily-backed path

Install

1. Clone the repository:
   • git clone https://github.com/MilleniumGenAI/deep-research-openclaw-agent.git
2. Copy openclaw/workspace-researcher/ into your OpenClaw base directory, or point your agent config at that path directly.
3. Align the main-agent handoff with:
   • openclaw/main-deep-research-skill.md
4. Register or update the deep-researcher agent in openclaw.json.
5. If you want Tavily-backed scouting, ensure TAVILY_API_KEY is available in env or .env.

Validate

Run these checks before using the agent in real work:

python -m py_compile openclaw/workspace-researcher/scripts/*.py
python openclaw/workspace-researcher/scripts/init_research_run.py --workspace openclaw/workspace-researcher --topic "Smoke test" --language en --task-date 2026-03-10

Then run a first smoke task through OpenClaw once the agent is wired:

openclaw agent --agent deep-researcher --json --message "Perform deep research using your local SOUL.md contract. GOAL: confirm the runtime can initialize a fresh run and return PARTIAL if no external research is performed. SCOPE: in scope is only local init and artifact creation; out of scope is web research. SUCCESS CRITERIA: create fresh tmp artifacts and explain blocked evidence collection honestly. TASK DATE: 2026-03-10. DELIVERABLES: finalized M2M JSON. LANGUAGE: en. CONSTRAINTS: do not fabricate sources; return PARTIAL if evidence is insufficient."

Core references

• Root README: README.md
• Sub-agent contract: openclaw/workspace-researcher/SOUL.md
• Main handoff contract: openclaw/main-deep-research-skill.md
• Runtime scripts: openclaw/workspace-researcher/scripts/
• Agent config template: openclaw/agent-config.template.json
• Known limits: docs/known-limits.md

Notes

• This is an OpenClaw-only v1 package.
• ClawHub publishes skills under platform-wide MIT-0 terms.
• The runtime source of truth is openclaw/workspace-researcher/SOUL.md.
• Findings should be built only from traceable external sources, not from local artifacts.