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 downtrend-duration-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/downtrend-duration-analyzer)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/downtrend-duration-analyzer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/downtrend-duration-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/downtrend-duration-analyzer"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/downtrend-duration-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 | $0.00027 | $0.01405 |
| Opus 5 | $0.00014 | $0.00702 |
| Sonnet 5 | $0.00005 | $0.00281 |
| Haiku 4.5 | $0.00003 | $0.00140 |
Grade A, and why
downtrend-duration-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 downtrend-duration-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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Downtrend Duration Analyzer
Overview
Analyze historical price data to identify downtrend periods (peak-to-trough) and build statistical distributions of correction durations. Generate interactive HTML visualizations with histograms segmented by sector and market cap to help traders understand typical recovery timeframes and set realistic expectations for mean reversion strategies.
When to Use
- Trader asks about typical correction lengths for a sector or market cap tier
- User wants to understand historical drawdown recovery times
- Building mean reversion or pullback strategies that need realistic holding period estimates
- Comparing correction behavior across different market segments
- Setting stop-loss timeouts or position holding period limits
Prerequisites
- Python 3.9+
- FMP API key (set
FMP_API_KEYenvironment variable or use--api-key) - Required packages:
requests,pandas,numpy(standard data analysis stack)
Workflow
Step 1: Fetch Historical Price Data
Run the analysis script to fetch OHLC data for a universe of stocks and identify downtrend periods.
python3 skills/downtrend-duration-analyzer/scripts/analyze_downtrends.py \
--sector "Technology" \
--lookback-years 5 \
--output-dir reports/
Step 2: Analyze Downtrend Durations
The script automatically:
- Identifies local peaks and troughs using rolling window analysis
- Calculates duration (trading days) and depth (% decline) for each downtrend
- Segments results by sector and market cap tier (Mega, Large, Mid, Small)
- Computes summary statistics (median, mean, percentiles)
Step 3: Generate Interactive HTML Visualization
python3 skills/downtrend-duration-analyzer/scripts/generate_histogram_html.py \
--input reports/downtrend_analysis_*.json \
--output-dir reports/
This creates an interactive HTML file with:
- Histogram of downtrend durations
- Filters for sector and market cap
- Hover tooltips with percentile information
- Summary statistics table
What ships with it
7 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.
- references/downtrend_methodology.md 6.0 KB
- scripts/analyze_downtrends.py 17 KB runs code
- scripts/generate_histogram_html.py 13 KB runs code
- scripts/tests/conftest.py 240 B runs code
- scripts/tests/test_analyze_downtrends.py 8.0 KB runs code
- scripts/tests/test_fetch_stock_list.py 1.8 KB runs code
- scripts/tests/test_generate_histogram_html.py 6.0 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.
- 13d ago First seen · 177 lines · 27 tokens per session scan A 9d9e0610ad57
downtrend-duration-analyzer is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 27 tokens to every session and 1,405 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to downtrend-duration-analyzer, differing in 0 lines, and is treated as a copy.
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