stockbee-setup-fluency-trainer
Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-setup-fluency-trainer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Stockbee Setup Fluency Trainer Build and maintain a model book for Stockbee-style Momentum Burst setups. This skill turns daily screener candidates into structured study records, updates them after the 3-day and 5-day windows mature, and summarizes which setup features are working or failing. ## When to Use - User wants to study Stockbee Momentum Burst setups systematically - User asks to build a model book from `stockbee-momentum-burst-screener` output - User wants to review failed candidates, missed trades, or A/B setup quality - User wants 3-day / 5-day forward returns, MFE, MAE, and stop-hit outcomes - User wants to improve setup recognition before increasing position size - User asks which Stockbee tags should be promoted, downgraded, or filtered ## Prerequisites - Python 3.10+ - A `stockbee-momentum-burst-screener` JSON report, or compatible candidate JSON - Optional: FMP API key for outcome updates when offline OHLCV JSON is not supplied - Recommended local state path: `state/stockbee/model_book.jsonl` ## Workflow ### Step 1: Ingest Momentum Burst Candidates Run after the Stockbee Momentum Burst screener has produced a JSON report. ```bash python3 skills/stockbee-setup-flu
- When to Use
- Prerequisites
- Workflow
- Step 1: Ingest Momentum Burst Candidates
- Step 2: Update 3-Day and 5-Day Outcomes
- Step 3: Summarize Cohorts
- Step 4: Convert Evidence Into Practice
- Model Book Fields
- Interpretation Rules
- Output
- Resources
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py ingest \ python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \ python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py summarize \
What does the stockbee-setup-fluency-trainer skill do?
Build a Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-setup-fluency-trainer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.