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-episodic-pivot-analyzergit 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-episodic-pivot-analyzer)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/stockbee-episodic-pivot-analyzer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-episodic-pivot-analyzer/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-episodic-pivot-analyzer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/stockbee-episodic-pivot-analyzer.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 | $0.00122 | $0.01216 |
| Opus 5 | $0.00061 | $0.00608 |
| Sonnet 5 | $0.00024 | $0.00243 |
| Haiku 4.5 | $0.00012 | $0.00122 |
Grade A, and why
stockbee-episodic-pivot-analyzer 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 13d 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-episodic-pivot-analyzer — 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stockbee Episodic Pivot Analyzer
Classify Day 1 Episodic Pivot (EP) candidates using both catalyst quality and price/volume confirmation. The skill is a candidate-quality analyzer, not an execution engine.
When to Use
- The user asks for Pradeep Bonde / Stockbee style EP candidates
- The user provides earnings, guidance, M&A, FDA, analyst, contract, product, short-squeeze, or theme/news events
- The user wants to separate
ACTIONABLE_DAY1candidates fromDELAYED_EP_WATCHnames - The user wants to hand strong earnings/guidance EPs into
pead-screener - The user wants to combine catalyst analysis with
stockbee-momentum-burst-screenerprice/volume output
Prerequisites
- Python 3.10+
- Optional: FMP API key for OHLCV/profile enrichment
- One of:
- Catalyst/events JSON
earnings-trade-analyzerJSON output- Catalyst JSON plus
stockbee-momentum-burst-screenerJSON enrichment
- This skill does not fetch or discover news by itself. If the catalyst is not supplied, first gather the event/news context using the user's preferred news or research process.
Workflow
Step 1: Prepare Candidate Inputs
Use one or more of these input modes.
Mode A — Catalyst/event JSON:
{
"events": [
{
"symbol": "ABC",
"event_date": "2026-04-25",
"catalyst_type": "guidance_raise",
"headline": "ABC raises FY guidance after record demand",
"summary": "Management raised revenue and EPS guidance."
}
]
}
Mode B — Earnings pipeline:
Use the JSON produced by earnings-trade-analyzer.
Mode C — Price/volume enrichment:
Pass a stockbee-momentum-burst-screener JSON report to reuse day-gain, volume, close-location, and risk-distance fields.
Step 2: Run the Analyzer
# Catalyst JSON + offline OHLCV
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--events-json data/catalysts.json \
--prices-json data/daily_ohlcv.json \
--output-dir reports/
# Earnings pipeline input
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--earnings-json reports/earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json \
--output-dir reports/
# Catalyst JSON + Stockbee momentum enrichment
python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \
--events-json data/catalysts.json \
--momentum-json reports/stockbee_momentum_burst_YYYY-MM-DD_HHMMSS.json \
--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.
- 13d ago First seen · 119 lines · 122 tokens per session scan A b5eac42930c8
stockbee-episodic-pivot-analyzer is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 122 tokens to every session and 1,216 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to stockbee-episodic-pivot-analyzer, differing in 0 lines, and is treated as a copy.
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