# stockbee-setup-fluency-trainer

Turns Stockbee Momentum Burst screener candidates into a study model book and fills in 3-day and 5-day outcomes.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2600 installs · ★ 3267
- Author: tradermonty
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/stockbee-setup-fluency-trainer/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/stockbee-setup-fluency-trainer

## What it does

The skill reads a JSON report from `stockbee-momentum-burst-screener` and writes each candidate into a durable model book. A record keeps the symbol, setup date, primary trigger, rating, setup tags, entry and stop references, plus your own labels and notes. Rejected candidates are skipped unless you pass `--include-rejects` to build a negative-example set on purpose.

Once the 3-day and 5-day windows mature, the `update` step fills in forward close return, MFE and MAE, whether the stop was touched and on which date. Each record then gets an outcome tag such as `STRONG_WINNER`, `WORKED`, `FAILED_STOP`, `FAILED_FADE`, `CHOPPY_FAILURE` or `NEUTRAL`. Price data comes either from FMP with an API key or from a local OHLCV JSON file when you would rather stay offline.

The `summarize` step groups records by rating, primary trigger and setup tags, and writes Markdown and JSON reports. `--min-sample` sets how many examples a cohort needs. Treat the generated `rule_candidates` as prompts for review, not as automatic rule changes — open the charts of representative names by hand before you touch your trading rules. Python 3.10+ is required.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/stockbee-setup-fluency-trainer/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/stockbee-setup-fluency-trainer/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/stockbee-setup-fluency-trainer/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/stockbee-setup-fluency-trainer/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install tradermonty/claude-trading-skills stockbee-setup-fluency-trainer
```

2. **Manually: put the files in place**
- File: `.github/skills/stockbee-setup-fluency-trainer/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/tradermonty/claude-trading-skills --path skills/stockbee-setup-fluency-trainer
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/stockbee-setup-fluency-trainer/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add tradermonty/claude-trading-skills --skill stockbee-setup-fluency-trainer -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/stockbee-setup-fluency-trainer/SKILL.md

## When to use it
- build a setup model book from a screener report
- track failed candidates and missed entries
- compute 3-day and 5-day forward returns
- measure MFE, MAE and stop-hit status
- compare setup quality across tag cohorts

## Example prompts
- Build the model book from `reports/stockbee_momentum_burst_2024-05-01_093000.json`
- Update 3-day and 5-day outcomes for `state/stockbee/model_book.jsonl`
- Summarize cohorts by rating and setup tags with a minimum sample of 5

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: e10618ebc48a0da0.

BEGIN THIRD-PARTY FILE e10618ebc48a0da0
````text
---
name: stockbee-setup-fluency-trainer
description: 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.
---

# 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-fluency-trainer/scripts/build_model_book.py ingest \
  --screener-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
  --model-book state/stockbee/model_book.jsonl \
  --output-dir reports/
```

Use `--include-rejects` when intentionally building a negative-example set. Otherwise rejected candidates are skipped.

### Step 2: Update 3-Day and 5-Day Outcomes

Use FMP:

```bash
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
  --model-book state/stockbee/model_book.jsonl \
  --horizons 3,5 \
  --output-dir reports/
```

Use offline OHLCV JSON:
````
END THIRD-PARTY FILE e10618ebc48a0da0

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---
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