Deep Research for OpenClaw
Security checks across static analysis, malware telemetry, and agentic risk
Overview
This is a coherent deep-research setup guide, with the main things to notice being external repository installation, local helper-script execution, and optional Tavily/web-provider use.
Before installing, review or pin the GitHub repository, inspect the Python helper scripts and OpenClaw prompt/config files, and use a limited Tavily API key if enabling Tavily-backed scouting. Avoid sending secrets or private data in research prompts unless you are comfortable with the configured sub-agent and external provider flows.
Static analysis
No static analysis findings were reported for this release.
VirusTotal
VirusTotal findings are pending for this skill version.
Risk analysis
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
Installing the skill may bring in prompt packs and helper scripts from a GitHub repository whose contents can change over time.
The skill's runtime is obtained from an external repository rather than included in the submitted artifact set. This is disclosed and central to installation, but the user is relying on that repository's current contents.
Clone the repository: `git clone https://github.com/MilleniumGenAI/deep-research-openclaw-agent.git`
Review the repository before installing, preferably pin to a trusted commit or release, and inspect the referenced prompt files and scripts before use.
Running the validation command executes code from the installed research-agent repository on the user's machine.
The validation workflow explicitly runs a Python helper script from the cloned repository. This is user-directed and purpose-aligned, but it is still local code execution.
python openclaw/workspace-researcher/scripts/init_research_run.py --workspace openclaw/workspace-researcher --topic "Smoke test" --language en --task-date 2026-03-10
Run the commands only after reviewing the script source, avoid elevated privileges, and execute them from an appropriate project workspace.
If configured, the Tavily key may be used to make external search requests, potentially consuming quota or sending research queries to Tavily.
The skill can use an external Tavily API credential for research scouting. This is optional and expected for the described integration, with no evidence of hardcoding or credential leakage.
If you want Tavily-backed scouting, ensure `TAVILY_API_KEY` is available in env or `.env`.
Use a dedicated Tavily key with appropriate limits, keep it out of shared files, and avoid submitting sensitive research prompts unless acceptable for that provider.
Research topics, prompts, and fetched content may be shared with the configured sub-agent and external search/fetch services during normal use.
The skill is designed to hand work to a configured sub-agent and use external discovery/fetch tools. This is aligned with deep research, but it means task content can move between agents and web/provider tools.
the Main -> Deep Research orchestration contract; ... hybrid discovery with `web_search`, Tavily, and `web_fetch`
Do not include secrets or private material in research requests unless those agent and provider data flows are acceptable.