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-20pct-studygit 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-20pct-study)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/stockbee-20pct-study"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-20pct-study/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-20pct-study"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-20pct-study.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 | $0.00087 | $0.01504 |
| Opus 5 | $0.00044 | $0.00752 |
| Sonnet 5 | $0.00017 | $0.00301 |
| Haiku 4.5 | $0.00009 | $0.00150 |
Grade A, and why
stockbee-20pct-study 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 12d 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-20pct-study — 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stockbee 20% Study
Build a daily event study of US equities that moved +20% or -20% over a defined window. Convert large movers into structured study records, classify the catalyst and chart context, update forward outcomes, and summarize recurring patterns for research.
This skill is a research, model-book, and setup-fluency workflow. It does not generate buy/sell signals, place orders, or output broker execution instructions.
When to Use
- User wants to run a Stockbee-style daily 20% mover study
- User asks which stocks moved +20% or -20% today, this week, or over a configurable lookback window
- User wants to backfill historical 20% movers and study what happened next
- User wants to identify continuation, reversal, exhaustion, or theme-cluster patterns
- User wants to build a model book of explosive winners, major failures, and failed low-quality pops
- User wants edge hints for downstream strategy research rather than immediate trade signals
Prerequisites
- Python 3.9+
- FMP API key for live US universe scans, or offline OHLCV JSON via
--prices-json - Optional structured news/catalyst JSON for higher-quality catalyst classification
- Recommended market regime artifact from
market-regime-daily - Recommended local state path:
state/stockbee/20pct_study_events.jsonl
Workflow
Step 1: Scan for 20% Movers
Run after the US market close, or against the latest complete daily bar in an offline OHLCV file.
python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
--fmp-universe \
--max-symbols 300 \
--as-of 2026-06-28 \
--lookback-days 5 \
--min-abs-return-pct 20 \
--min-price 5 \
--min-dollar-volume 20000000 \
--include-down-movers \
--state-file state/stockbee/20pct_study_events.jsonl \
--output-dir reports/
Use offline data instead of FMP:
python3 skills/stockbee-20pct-study/scripts/run_20pct_study.py scan \
--prices-json data/us_daily_ohlcv.json \
--as-of 2026-06-28 \
--lookback-days 5 \
--include-down-movers \
--state-file state/stockbee/20pct_study_events.jsonl \
--output-dir reports/
What ships with it
13 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.
- assets/cohort_summary_template.md 158 B
- assets/daily_report_template.md 196 B
- references/catalyst_taxonomy.md 2.2 KB
- references/cohort_mining_rules.md 2.1 KB
- references/event_schema.md 3.5 KB
- references/methodology.md 3.1 KB
- references/scoring_system.md 2.2 KB
- scripts/run_20pct_study.py 68 KB runs code
- scripts/tests/test_cli_outputs.py 6.4 KB runs code
- scripts/tests/test_cohort_summary.py 3.0 KB runs code
- scripts/tests/test_event_detection.py 4.4 KB runs code
- scripts/tests/test_fmp_client.py 4.2 KB runs code
- scripts/tests/test_forward_outcomes.py 4.1 KB runs code
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.
- 12d ago First seen · 133 lines · 87 tokens per session scan A ea5045f55400
stockbee-20pct-study is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 8d ago), licensed MIT. It adds 87 tokens to every session and 1,504 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to stockbee-20pct-study, differing in 0 lines, and is treated as a copy.
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