SkillHub

deep-research-openclaw-agent

v0.1.1

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

Sourced from ClawHub, Authored by LamdaProject

Installation

Please help me install the skill `deep-research-openclaw-agent` from SkillHub official store. npx skills add MilleniumGenAI/deep-research-openclaw-agent

Deep Research for OpenClaw

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:
  2. git clone https://github.com/MilleniumGenAI/deep-research-openclaw-agent.git
  3. Copy openclaw/workspace-researcher/ into your OpenClaw base directory, or point your agent config at that path directly.
  4. Align the main-agent handoff with:
  5. openclaw/main-deep-research-skill.md
  6. Register or update the deep-researcher agent in openclaw.json.
  7. 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.