Smart Money Miner

This skill should be used when the user asks to "mine smart money addresses", "find smart money", "analyze PumpFun tokens", "analyze Four Meme tokens", "anal...

What to consider before installing or running this skill: - Review the code before running. Open scripts/miner.py and grep for subprocess, os.system, eval/exec, open(..., 'r') outside the workspace, or any hidden network destinations. The visible miner.py uses requests and is network-heavy but does not obviously spawn shell commands. - Be cautious about the included virtualenv. The repository bundles a full venv (pip, requests, urllib3, etc.). Do NOT run that venv blindly — instead create a fresh isolated environment (e.g., new Python venv or container) and install only the minimal dependencies (requests) from PyPI. - Run in isolation first. Execute the script in a disposable container or VM, with network monitoring, and start with tiny limits (e.g., --limit 1) so you can observe outbound requests and behaviour. - Check external endpoints and credentials. The code calls OKX 'priapi' endpoints and a Binance narrative endpoint. Confirm whether those endpoints are public and whether they expect API keys; the skill does not request any keys. If you must provide credentials later, only do so with least privilege and after verification. - Inspect SKILL.md for hidden characters. The scan indicated unicode-control-chars — open the file in a hex-capable editor to ensure there are no invisible sections that could affect automated parsers. - Validate outputs. Some values in the bundled smart_money_results.json are implausibly large — this may indicate bugs or lack of input validation. Treat results as investigatory data, not trading advice. - Legal and ethical caution: mining and acting on on-chain trader addresses may have legal or platform policy implications depending on jurisdiction and how you use the results. If you want, I can: - Summarize miner.py and highlight any exact lines that perform network calls, file I/O, or potentially risky operations. - Produce a short checklist of commands to safely run the tool in a containerized environment. Confidence note: Assessment is medium confidence because the core code aligns with the stated purpose, but the included vendored virtualenv and the unicode-control-chars finding are unexplained and increase risk; additional manual code review would raise confidence either way.