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 mphinance/alpha-skills --skill stock-analyzergit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/stock-analyzer)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/stock-analyzer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/stock-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/mphinance/alpha-skills/stock-analyzer"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/stock-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 | $0.00065 | $0.00674 |
| Opus 5.5 | $0.00026 | $0.00270 |
| Sonnet 5.5 | $0.00013 | $0.00135 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
stock-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Analyzer
Random Forest regression over technical indicators to predict a 5-day price range and generate market insights for the Single Ticker Audit view.
Usage
from stock_analyzer import StockAnalyzer, generate_market_analysis
analyzer = StockAnalyzer()
data = analyzer.calculate_technical_indicators(ohlcv_df)
model_info = analyzer.train_prediction_model(data, horizon=5)
prediction = analyzer.predict_price_range(model_info, current_price=150.0)
insights = generate_market_analysis(data, ticker="AAPL")
Requirements
Needs pandas and scikit-learn. Run with the repo-root venv, NOT bare python3:
.venv/bin/python -c "from stock_analyzer import StockAnalyzer, generate_market_analysis"
Verified: ./.venv/bin/python has both. Bare python3 does not, and the resulting
ModuleNotFoundError: No module named 'pandas' is the "skill seems broken" symptom,
not a code fault.
Indicators
| Category | Indicators |
|---|---|
| Moving averages | SMA(20), SMA(50), EMA(12), EMA(26) |
| Momentum | RSI(14), MACD + signal + histogram |
| Volatility | Bollinger Bands(20,2), ATR(14) |
| Volume | Volume SMA(20), Volume Ratio |
| Oscillators | Stochastic %K, %D |
ML features: lag (close/volume/returns 1–5d back), rolling mean/std (5/10/20d), price-vs-SMA20/50, 10/20d return volatility. Minimums: 50 bars to train, 30 after split.
Prediction output
{ 'expected': 152.30, 'low': 148.50, 'high': 156.10,
'expected_change_pct': 1.53, 'confidence': 0.72, 'horizon': 5 }
low/high = ±1 std dev across trees; confidence = inverse of relative
uncertainty (0–1).
Insight categories
Price movement · RSI overbought/oversold · MA alignment · Bollinger extremes ·
MACD direction & crossovers · volume conviction. Returned as an emoji-annotated
list, e.g. 💡 RSI at 28.5 suggests oversold — potential buying opportunity.
Integration
main.py → render_audit_view(): fetch ~6mo daily (yfinance) → indicators →
train (if enough data) → prediction card (Low/Expected/High) → insights panel.
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.
- 1mo ago First seen · 75 lines · 65 tokens per session scan A bf5091bf2717
stock-analyzer is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 65 tokens to every session and 674 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
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