Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
Buffett Analysis
v0.1.0巴菲特视角的上市公司基本面深度分析。当用户提到"分析一家公司"、"看看XX值不值得投资"、"XX的基本面怎么样"、"帮我研究一下XX"、个股分析、价值投资分析、公司估值、高管研究、管理层分析、公司战略、新闻采集、PR分析、业务出海、创新药等需求时使用。也支持行业分析——当用户提到"XX行业怎么样"、"帮我梳理一下...
⭐ 1· 629·1 current·1 all-time
by@zacbai
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
The skill's name/description align with the included scripts and templates: both scripts fetch financials/news/management info and the templates describe the expected report. However the SKILL.md mandates writing the final report into a specific frontend file (alpha-factor-lab/fundamental-reports.json) and the code expects another skill's script (us-market) and an mcporter toolset to be available; these integration points are not declared in requires.config/required envs and thus are implicit dependencies that should be declared.
Instruction Scope
Runtime instructions and scripts instruct the agent to run web_search/web_fetch, call mcporter fintool APIs, invoke a separate us-market script, and 'must' append reports to alpha-factor-lab/fundamental-reports.json. That file-write is an explicit modification of repository/frontend state but is not declared in the skill manifest. The scripts also write/read predictable /tmp files. The instructions give broad discretion for web_fetch/search targets and do not document limits or sanitization, increasing the risk of unexpected data collection or modification.
Install Mechanism
No install spec is present (instruction-only plus included Python scripts). That is lower installation risk than arbitrary remote downloads. The presence of executable scripts means code will run if invoked, but nothing is automatically downloaded or installed by the skill.
Credentials
The skill declares no required env vars, which superficially looks minimal. In practice the scripts call mcporter (fintool-*/fintool-plates/etc.) and expect the us-market skill/script to be present; those platform connectors often rely on platform credentials or tokens not declared here. The SKILL.md also instructs reading and appending a frontend JSON file (alpha-factor-lab/...), which gives write access to a project artifact without being declared.
Persistence & Privilege
always is false and there is no explicit persistence privilege. However the SKILL.md mandates appending analysis results to alpha-factor-lab/fundamental-reports.json (a persistent project/frontend file). That is a modification of repository state the skill will perform each run and should be explicitly documented as a required config/file path; currently it is not.
What to consider before installing
What to check before installing or running this skill:
- Integration dependencies: The scripts call an mcporter toolset (fintool-*) and a separate us-market script (relative path ../../us-market/...). Verify those services/scripts exist in your environment and that you understand what credentials they use. The skill does not declare these dependencies or any env vars.
- File writes: The instructions require appending reports to alpha-factor-lab/fundamental-reports.json. Confirm you want the skill to modify that file and that it won't overwrite or leak other data. The path is not declared as a required config in the manifest.
- Code-level injection risk: The Python scripts build shell commands and call subprocess.run(..., shell=True) with user-provided keywords. If untrusted input reaches those functions, shell injection is possible. Review the scripts and ensure inputs are sanitized or the code is changed to avoid shell=True.
- Temporary files and predictable paths: The scripts read/write predictable /tmp files (e.g., /tmp/buffett_analysis_{code}.json). Verify the runtime environment's /tmp policy and consider running in an isolated environment to avoid TOCTOU or sensitive-file mixing.
- Review network/data exfil patterns: The skill performs web_search/web_fetch and calls platform connectors — ensure that collected data is acceptable to transmit and that external endpoints are trusted.
- Operational recommendation: Only run this skill in a controlled environment, review and/or harden the scripts (avoid shell=True, properly escape inputs, declare required config paths and credentials), and confirm you consent to the skill modifying alpha-factor-lab/fundamental-reports.json. If you need higher assurance, request the author add explicit manifest entries for dependencies and config paths and fix subprocess usage.Like a lobster shell, security has layers — review code before you run it.
latestvk977r7cqmn4gr6ekq2xv0zyazh81vmxy
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
