deep-scout
v0.1.4Multi-stage deep intelligence pipeline (Search → Filter → Fetch → Synthesize). Turns a query into a structured research report with full source citations.
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byJonathan Jing@jonathanjing
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name and description (web search → filter → fetch → synthesize) match the actual behavior: it calls web_search/web_fetch, uses LLMs for filtering/synthesis, and optionally uses a local Firecrawl CLI or the browser tool. Required binaries (bash, python3, timeout/gtimeout) and included scripts are proportional to the described functionality.
Instruction Scope
SKILL.md and scripts explicitly instruct the agent to fetch arbitrary web URLs and feed extracted content to LLMs (expected for a research tool). The run.sh includes query sanitization and output-path restrictions as mitigations. Users should note that fetched page content (including snapshots) will be sent to the LLM — this is intended but a privacy consideration.
Install Mechanism
No install spec (instruction-only) and included shell scripts only; no remote downloads are performed by an installer. The optional Firecrawl integration calls a local CLI if present. This is a low-risk install footprint.
Credentials
The skill requests no environment variables, no credentials, and no config paths. That aligns with its purpose: it leverages agent-provided tools (web_search, web_fetch, browser) rather than external API keys.
Persistence & Privilege
always:false (default) and no code attempts to modify other skills or system-wide agent settings. The skill writes its own state to a skill-local state file (deep-scout-state.json) — expected for resumability.
Assessment
This skill appears to do what it says: it runs a search → filter → fetch → synthesize pipeline using agent web tools and LLM prompts. Before installing, be aware of these practical points: 1) The skill will fetch arbitrary web pages and send their extracted text to the LLM — avoid using it for highly sensitive/private queries or internal URLs you don't want shared with the model. 2) It may run local shell scripts (run.sh, firecrawl wrapper). The package includes sanitization and an output-path check, which is good, but you can review those scripts yourself before enabling. 3) Firecrawl is optional and only invoked if present locally; otherwise the wrapper reports FIRECRAWL_UNAVAILABLE. 4) The agent will be able to invoke the skill normally (autonomous invocation is the platform default); if you prefer manual control, only call it interactively. If you'd like greater assurance, inspect scripts/run.sh and prompts locally, and test with non-sensitive queries first.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
Binsbash, python3
Any bintimeout, gtimeout