market-environment-analysis

market-environment-analysis is a skill for Claude Code from mphinance/alpha-skills. It costs 104 tokens per session (1,185 once invoked), scanned A, original, MIT.

A broad financial-market reporting workflow that reviews global shares, currencies, commodities, government-bond yields, volatility, economic indicators, and sector performance. Risk-on and risk-off describe whether investors generally prefer riskier or safer assets.

In plain words
What is it for?
It helps prepare market briefings, review global conditions, assess risk-on or risk-off sentiment, study sector rotation and cross-market links, and interpret technical indicators.
Why use it?
It gathers different markets into one overview so relationships between them and the overall market mood are easier to examine.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the quant-skills plugin — 60 skills shipped together

Good fit It helps prepare market briefings, review global conditions, assess risk-on or risk-off sentiment, study sector rotation and cross-market links, and interpret technical indicators.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mphinance/alpha-skills/market-environment-analysis
Install

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.

Any agent
npx skills add mphinance/alpha-skills --skill market-environment-analysis
Clone the repo
git clone --depth 1 https://github.com/mphinance/alpha-skills

Made for: Claude Code.

Or install quant-skills, the plugin that ships this one along with the rest of its 60 skills.

Wrote 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.

agentmods badge for market-environment-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/mphinance/alpha-skills/market-environment-analysis/github.svg)](https://agentmods.dev/skills/mphinance/alpha-skills/market-environment-analysis)
Your own site
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/market-environment-analysis"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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.

agentmods 80×15 button for market-environment-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/market-environment-analysis"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/market-environment-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,185 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00104 $0.01185
Opus 5.5 $0.00042 $0.00474
Sonnet 5.5 $0.00021 $0.00237
Haiku 4.5 $0.00010 $0.00119

Measured 1mo ago against content hash 723adbb7d7ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/market_utils.py, scripts/tests/__init__.py, scripts/tests/test_market_utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/quant-skills/skills/market-environment-analysis/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 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.

When to Use

  • When you need a comprehensive overview of global market conditions
  • Before making trading or investment decisions
  • For daily/weekly market briefings
  • When assessing risk-on/risk-off sentiment
  • For understanding inter-market correlations and sector rotation
  • When preparing market reports for clients or personal records

Prerequisites

  • WebSearch access: Required for fetching real-time market data
  • No API keys required: This skill uses web search for data collection
  • Optional: Economic calendar data for event-driven analysis

Core Workflow

1. Initial Data Collection

Collect latest market data using web_search tool:

  1. Major stock indices (S&P 500, NASDAQ, Dow, Nikkei 225, Shanghai Composite, Hang Seng)
  2. Forex rates (USD/JPY, EUR/USD, major currency pairs)
  3. Commodity prices (WTI crude, Gold, Silver)
  4. US Treasury yields (2-year, 10-year, 30-year)
  5. VIX index (Fear gauge)
  6. 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

Read the full file on GitHub · 161 lines

Files

What ships with it

6 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.

Changes

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.

  1. 1mo ago First seen · 161 lines · 104 tokens per session scan A 723adbb7d7ce

Subscribe to this mod's changes

market-environment-analysis is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 104 tokens to every session and 1,185 once invoked, about $0.0004 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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