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Deep Research Agent

v0.1.0

Autonomous deep research agent with multi-step web search, sub-agent delegation, and structured report generation. Triggered by requests for deep research, 深...

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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Purpose & Capability
The name/description (deep research using Tavily and an LLM) matches the code and SKILL.md, but the registry metadata declares no required environment variables while both the SKILL.md and backend/agent.py clearly require a TAVILY_API_KEY and an LLM API key. That mismatch is incoherent and reduces trust in the manifest.
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Instruction Scope
SKILL.md and the embedded prompts instruct the agent to discover URLs via Tavily, fetch full page content (using httpx), and write files such as /research_request.md and /final_report.md. Fetching arbitrary URLs and writing absolute-root paths are broad operations outside a narrow 'search-only' scope and could expose internal resources or write to unexpected locations. The instructions also mandate using sub-agents and persistent write_file() calls, which increases the agent's filesystem footprint.
Install Mechanism
There is no formal install spec in the registry (instruction-only), but the package includes requirements.txt and lists pip dependencies in SKILL.md (deepagents, tavily-python, langchain-anthropic, markdownify). These are reasonable for the stated purpose and come from normal package registries, but the lack of an install spec in the registry + included code/requirements is an inconsistency to be aware of.
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Credentials
Requesting a Tavily API key and an LLM API key is proportionate to a web-research agent. However, the registry's 'Required env vars: none' contradicts the explicit SKILL.md and code requirements (TAVILY_API_KEY, ANTHROPIC_API_KEY/GOOGLE_API_KEY/OPENAI_API_KEY). That discrepancy is concerning and should be resolved before trusting the skill.
Persistence & Privilege
The skill does not set always:true and does not claim to modify other skills. However, the runtime instructions and prompts expect write_file() usage that writes /research_request.md and /final_report.md (absolute paths), which means it will persist files to the environment. Persisted files and autonomous sub-agents increase blast radius; run in a sandboxed environment if you proceed.
What to consider before installing
This skill looks like a genuine deep-research tool but has discrepancies and risky behaviors you should address before installing or running it: - Manifest mismatch: The registry metadata says no environment variables are required, but the SKILL.md and code require TAVILY_API_KEY and an LLM API key. Do not supply secrets until the author/registry metadata is corrected or you review the origin. - File writes: The instructions/code expect to write files (e.g., /research_request.md, /final_report.md). Confirm where files will be written (root vs current directory) and run in an isolated container or VM to avoid accidental overwrite of host files. - Arbitrary URL fetching: The agent fetches full page content using httpx for URLs returned by Tavily. That can potentially access internal network endpoints if a search result points there (SSRF-like risk). Prefer running the agent in a network-restricted environment and inspect fetched URLs if possible. - Dependency & install: The skill ships code and a requirements.txt but no formal install spec in the registry. If you install dependencies, do so in a virtualenv/container and inspect the packages (deepagents, tavily-python, langchain-anthropic, markdownify) yourself. - Trust & provenance: There is no homepage and the owner is an opaque ID. If you need to use this skill, ask the publisher for source provenance, or only run it in a sandbox. If you plan to proceed: run it in an isolated environment, avoid using high-privilege or production API keys (create scoped/test keys), and review the Tavily search results and any files the agent writes before trusting outputs.

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.

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