Warren Buffet's Brain

Point-in-time Buffett-style company analysis for stocks, Berkshire case studies, and BUY/PASS verdicts. Use when the user wants a Buffett/Graham underwriting...

This package appears to be a coherent Buffett-style research framework, but before installing or running it, do the following simple checks: 1) Inspect oracle.py (and any small scripts) for network calls, hardcoded endpoints, calls to subprocess/shell, or code that reads arbitrary filesystem paths. Search for 'requests', 'urllib', 'socket', 'subprocess', 'open(', and 'os.system'. 2) Open agents/openai.yaml and CLAUDE.md to verify they don't contain API keys or telemetry endpoints. 3) If you will allow the agent to fetch filings, be aware that this implies outbound network access — confirm which endpoints will be used and whether your environment policy allows that. 4) If you intend to let the skill update the repo, run it in a sandbox or give limited filesystem permissions (or require manual review of any saved changes). 5) The docs include absolute local paths (e.g., /Users/pineapple/...), which are benign artifacts but indicate developer-local references; they should not be trusted as runtime instructions to read other hosts. If you are not comfortable inspecting the code yourself, ask the publisher for: (a) a brief code summary of oracle.py's external I/O, (b) whether any telemetry/analytics endpoints are contacted, and (c) an explicit list of network hosts the tool may access. If any of the above reveal unexpected endpoints, credential prompts, or aggressive shell execution, treat the package as suspicious and do not run it on sensitive hosts.

Type: OpenClaw Skill Name: think-like-warren-buffett Version: 1.0.0 The Buffett Oracle skill bundle is a comprehensive financial research framework designed to analyze stocks using Warren Buffett's investment principles. The bundle includes a Python utility (oracle.py) for repository management, site generation, and data validation, alongside structured markdown instructions (SKILL.md, CLAUDE.md) and a large dataset of company analysis cards. The code logic and agent instructions are entirely consistent with the stated purpose; there is no evidence of data exfiltration, malicious execution, or deceptive prompt injection. The Python script's use of local file operations and a local development server is standard for this type of research tool.