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 market-environment-analysisgit 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/market-environment-analysis)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/market-environment-analysis"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/market-environment-analysis/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/market-environment-analysis"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/market-environment-analysis.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 | $0.00104 | $0.01011 |
| Opus 5 | $0.00052 | $0.00505 |
| Sonnet 5 | $0.00021 | $0.00202 |
| Haiku 4.5 | $0.00010 | $0.00101 |
Grade A, and why
market-environment-analysis 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 12d 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 market-environment-analysis — 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Environment Analysis
Comprehensive analysis tool for understanding market conditions and creating professional market reports anytime.
Core Workflow
1. Initial Data Collection
Collect latest market data using web_search tool:
- Major stock indices (S&P 500, NASDAQ, Dow, Nikkei 225, Shanghai Composite, Hang Seng)
- Forex rates (USD/JPY, EUR/USD, major currency pairs)
- Commodity prices (WTI crude, Gold, Silver)
- US Treasury yields (2-year, 10-year, 30-year)
- VIX index (Fear gauge)
- Market trading status (open/close/current values)
2. Market Environment Assessment
Evaluate the following from collected data:
- Trend Direction: Uptrend/Downtrend/Range-bound
- Risk Sentiment: Risk-on/Risk-off
- Volatility Status: Market anxiety level from VIX
- Sector Rotation: Where capital is flowing
3. Report Structure
Standard Report Format:
1. Executive Summary (3-5 key points)
2. Global Market Overview
- US Markets
- Asian Markets
- European Markets
3. Forex & Commodities Trends
4. Key Events & Economic Indicators
5. Risk Factor Analysis
6. Investment Strategy Implications
Script Usage
market_utils.py
Provides common functions for report creation:
# Generate report header
python scripts/market_utils.py
# Available functions:
- format_market_report_header(): Create header
- get_market_session_times(): Check trading hours
- categorize_volatility(vix): Interpret VIX levels
- format_percentage_change(value): Format price changes
Reference Documentation
Key Indicators Interpretation (references/indicators.md)
Reference when you need:
- Important levels for each index
- Technical analysis key points
- Sector-specific focus areas
Analysis Patterns (references/analysis_patterns.md)
Reference when analyzing:
- Risk-on/Risk-off criteria
- Economic indicator interpretation
- Inter-market correlations
- Seasonality and market anomalies
Output Examples
Quick Summary Version
📊 Market Summary [2025/01/15 14:00]
━━━━━━━━━━━━━━━━━━━━━
【US】S&P 500: 5,123.45 (+0.45%)
【JP】Nikkei 225: 38,456.78 (-0.23%)
【FX】USD/JPY: 149.85 (↑0.15)
【VIX】16.2 (Normal range)
⚡ Key Events
- Japan GDP Flash
- US Employment Report
📈 Environment: Risk-On Continues
What ships with it
3 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.
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.
- 12d ago First seen · 140 lines · 104 tokens per session scan A 76d00c0d2a1d
market-environment-analysis is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 104 tokens to every session and 1,011 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to market-environment-analysis, differing in 0 lines, and is treated as a copy.
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