Agent skill

ibd-distribution-day-monitor

Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.

BaggaT236github.com/BaggaT236GitHub ↗
claude-codeships scriptsMIT
Install
npx skills add BaggaT236/AI-Trading-Skills --skill ibd-distribution-day-monitor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 24
SKILL.md size: 5 KB
Bundled scripts: yes
Path: skills/ibd-distribution-day-monitor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 118
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# IBD Distribution Day Monitor ## Purpose Detect IBD-style Distribution Days for major market ETFs (QQQ as Nasdaq proxy, SPY as S&P 500 proxy) and produce a daily market deterioration signal plus a TQQQ/QQQ exposure recommendation. Designed for post-market review. ## When to Use Invoke this skill: - Daily after the US market close. - Before increasing TQQQ exposure or rebalancing leveraged positions. - When evaluating whether an uptrend is becoming vulnerable to a correction. - As an upstream input to FTD (Follow-Through Day) detection or other market-state frameworks. Do NOT use this skill to: - Execute trades or modify orders. - Generate discretionary market predictions outside of the IBD ruleset. ## Inputs - Symbols (default: QQQ, SPY) and lookback (default 80 trading sessions). - Optional `--as-of YYYY-MM-DD` for backtesting against a historical session. - Strategy context: instrument (TQQQ or QQQ), current exposure %, base trailing stop %. - FMP API key via `--api-key`, `config.data.api_key`, or `FMP_API_KEY` env var (in that priority order). ## Core Rules A Distribution Day is detected when: 1. Today's close is at least 0.2% below yesterday's close. 2. Today's volume is great

What's inside
Steps it walks through
  1. Purpose
  2. When to Use
  3. Inputs
  4. Core Rules
  5. Counting Conventions
  6. Risk Classification
  7. TQQQ Exposure Policy
  8. Workflow
  9. Outputs
  10. Operating Principles
  11. CLI
  12. API Requirements
  13. Related Skills
Ships with 23 files
  • config/default.yaml
  • references/ibd_distribution_methodology.md
  • references/tqqq_exposure_policy.md
  • scripts/_fmp_compat.py
  • scripts/data_loader.py
  • scripts/distribution_day_tracker.py
  • scripts/exposure_policy.py
  • scripts/fmp_client.py
  • scripts/history_utils.py
  • scripts/ibd_monitor.py
  • scripts/math_utils.py
  • scripts/models.py
  • scripts/report_generator.py
  • scripts/risk_classifier.py
  • scripts/tests/conftest.py
  • scripts/tests/helpers.py
  • scripts/tests/test_data_loader.py
  • scripts/tests/test_distribution_day_tracker.py
  • scripts/tests/test_exposure_policy.py
  • scripts/tests/test_history_utils.py
  • scripts/tests/test_math_utils.py
  • scripts/tests/test_report_generator.py
  • scripts/tests/test_risk_classifier.py
Commands it runs
python3 skills/ibd-distribution-day-monitor/scripts/ibd_monitor.py \
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About this skill
What does the ibd-distribution-day-monitor skill do?

Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.

How do I install it?

Run `npx skills add BaggaT236/AI-Trading-Skills --skill ibd-distribution-day-monitor --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.

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