stockbee-episodic-pivot-analyzer

stockbee-episodic-pivot-analyzer is a skill for Claude Code, Codex from BaggaT236/AI-Trading-Skills. It costs 122 tokens per session (1,216 once invoked), scanned A, a copy of stockbee-episodic-pivot-analyzer, MIT.

A Stockbee-style analyzer for Day 1 Episodic Pivot candidates, meaning stocks reacting to a major event such as earnings, a takeover, regulatory approval, or a large contract. It evaluates the event together with the stock's price and trading-volume reaction.

In plain words
What is it for?
Use it to review supplied catalyst events, classify candidates as immediate or delayed opportunities, and pass suitable results to earnings or momentum analysis workflows.
Why use it?
It separates stronger event-driven candidates from names that may need more observation, using both the reason for the move and market confirmation. It does not discover news or execute trades by itself.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review supplied catalyst events, classify candidates as immediate or delayed opportunities, and pass suitable results to earnings or momentum analysis workflows.

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Install with agentmods
npx agentmods add skills/baggat236/ai-trading-skills/stockbee-episodic-pivot-analyzer
Install

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.

Any agent
npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-episodic-pivot-analyzer
Clone the repo
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-Skills

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Your own site
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Per session 122 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,216 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 13d ago against content hash b5eac42930c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/analyze_ep.py, scripts/tests/test_analyze_ep.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

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.

skills/stockbee-episodic-pivot-analyzer/SKILL.md · 119 lines

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_DAY1 candidates from DELAYED_EP_WATCH names
  • The user wants to hand strong earnings/guidance EPs into pead-screener
  • The user wants to combine catalyst analysis with stockbee-momentum-burst-screener price/volume output

Prerequisites

  • Python 3.10+
  • Optional: FMP API key for OHLCV/profile enrichment
  • One of:
    • Catalyst/events JSON
    • earnings-trade-analyzer JSON output
    • Catalyst JSON plus stockbee-momentum-burst-screener JSON 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/

Read the full file on GitHub · 119 lines

Files

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.

Changes

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.

  1. 13d ago First seen · 119 lines · 122 tokens per session scan A b5eac42930c8

Subscribe to this mod's changes

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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