stockbee-episodic-pivot-analyzer
Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.
npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-episodic-pivot-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to Use
- Prerequisites
- Workflow
- Step 1: Prepare Candidate Inputs
- Step 2: Run the Analyzer
- Step 3: Review the Output
- Step 4: Handoff Rules
- Output
- Resources
Catalyst JSON + offline OHLCV python3 skills/stockbee-episodic-pivot-analyzer/scripts/analyze_ep.py \ Earnings pipeline input Catalyst JSON + Stockbee momentum enrichment export FMP_API_KEY=your_key
What does the stockbee-episodic-pivot-analyzer skill do?
Analyze Stockbee-style Day 1 Episodic Pivot candidates from earnings, guidance raises, M&A, FDA/regulatory approvals, analyst actions, major contracts, product launches, short-squeeze catalysts, or theme/story events. Scores catalyst quality together with gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low. Use when the user asks for EP candidates, episodic pivots, Day 1 catalyst trades, game-changing news reactions, delayed EP watchlists, or handoffs into PEAD monitoring.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill stockbee-episodic-pivot-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.