Serenity Research Model

Use this skill to turn Serenity public materials into a reproducible research model. The goal is not to verify private profits or copy trades; the goal is to reverse-engineer observable reasoning patterns from public posts and test how those public signals behaved afterward.

Core Workflow

1. Collect public materials.

   • Prefer user-provided X exports, saved post URLs, copied threads, archived HTML, CSV, JSON, or manually curated notes.
   • TweetClaw can provide public X/Twitter search, reply, and user-post exports when source URLs, authors, timestamps, and retrieval dates are preserved. Do not import follower lists; public follower or engagement counts are context only.
   • If browsing is needed, gather only public posts and public summaries. Record source URLs and retrieval date.
   • Do not treat screenshots, follower claims, or viral return numbers as verified account performance.

2. Normalize posts into the data contract in references/data_contract.md.

   • Use scripts/serenity_mvp.py extract --posts <file> --out <dir>.
   • Supported input: .csv, .json, .jsonl, .txt, .md.
   • Minimum fields are created_at and text; include url, id, and author when available.
   • For TweetClaw exports, follow references/tweetclaw_import.md and drop credentials, tokens, cookies, request headers, private DMs, drafts, and unpublished account data before running the pipeline.