Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-setup-fluency-trainergit clone --depth 1 https://github.com/BaggaT236/AI-Trading-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/baggat236/ai-trading-skills/stockbee-setup-fluency-trainer)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/stockbee-setup-fluency-trainer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-setup-fluency-trainer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/stockbee-setup-fluency-trainer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-setup-fluency-trainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00098 | $0.01227 |
| Opus 5 | $0.00049 | $0.00613 |
| Sonnet 5 | $0.00020 | $0.00245 |
| Haiku 4.5 | $0.00010 | $0.00123 |
Grade A, and why
stockbee-setup-fluency-trainer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to stockbee-setup-fluency-trainer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-screeneroutput - 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-screenerJSON 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.
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:
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:
python3 skills/stockbee-setup-fluency-trainer/scripts/build_model_book.py update \
--model-book state/stockbee/model_book.jsonl \
--prices-json data/daily_ohlcv.json \
--horizons 3,5 \
--output-dir reports/
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 123 lines · 98 tokens per session scan A ae45d220f94c
stockbee-setup-fluency-trainer is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 7d ago), licensed MIT. It adds 98 tokens to every session and 1,227 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to stockbee-setup-fluency-trainer, differing in 0 lines, and is treated as a copy.
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