Agent skill

Fin Stock Correlation

by criptogus in criptogus/agent-evolve-network

Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context.

MITAuto-check passed

Install Fin Stock Correlation

skills CLI
$ npx skills add criptogus/agent-evolve-network --skill fin-stock-correlation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install criptogus/agent-evolve-network fin-stock-correlation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/criptogus/agent-evolve-network.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fin-stock-correlation .claude/skills/fin-stock-correlation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fin-stock-correlation
GitHub stars
288
Token cost
~891 tokens
SKILL.md length
309 words
Files
1
Skills in repo
107
Repo updated
First seen
Licence
MIT

At a glance

Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context.

  • The user asks for stock correlation analysis work
  • SKILL.md covers Instructions, Always, Never and Examples, plus 1 more section
  • Calls npx

What it does

Fin Stock Correlation is an agent skill from criptogus/agent-evolve-network. Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context. Use when the user asks for stock correlation analysis work, or mentions fin, stock, correlation.

Its SKILL.md is about 890 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is MIT.

When your agent uses it

  • The user asks for stock correlation analysis work

Example prompts

  • “/fin-stock-correlation”

Requirements

  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit d19b920. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • superagentskill.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fin Stock Correlation loads about 891 tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 309 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~891

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from criptogus/agent-evolve-network at commit d19b920, republished under its MIT licence (© criptogus). 309 words, ~891 tokens.

Download SKILL.mdSave it as .claude/skills/fin-stock-correlation/SKILL.md (or your agent's skills folder).
name
fin-stock-correlation
description
Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context. Use when the user asks for stock correlation analysis work, or mentions fin, stock, correlation.
version
0.1.0
license
MIT
homepage
https://superagentskill.com/marketplace/fin-stock-correlation
source
Super Agent Skill (SAK)

Stock Correlation Analysis

Use this skill when a user wants to understand how stocks move together: discovering co-moving peers, computing pairwise return correlation, clustering a set of names by correlation/sector, or analyzing realized correlation over time (rolling windows and regime-conditional, e.g. risk-on vs risk-off).

It downloads price history, computes returns and correlation matrices, and presents results with practical applications (diversification, pairs trading, hedging context) where relevant. Output is research/educational only, not financial advice; it does not recommend trades.

Instructions

You are a quantitative correlation analyst. Step 1 - Ensure dependencies are available (e.g. yfinance, numpy, pandas). Step 2 - Route to the correct sub-skill: (A) Co-movement Discovery — build a peer universe and find the most-correlated names; (B) Return Correlation — pairwise correlation of returns over a window; (C) Sector Clustering — build a correlation matrix and cluster; (D) Realized Correlation — rolling correlation and regime-conditional correlation. Apply sensible defaults for window and frequency. Step 3 - Download prices, compute returns (not raw prices) and the relevant correlation statistics. Step 4 - Respond: always include the correlation values/matrix and the window used; always caveat that correlations are unstable, regime-dependent, and backward-looking. Mention practical applications (diversification, pairs trading, hedging) when relevant. Research/educational only, not financial advice; do not recommend trades.

Always

  • Compute correlation from returns over a stated window, fetching live price data.
  • Note that correlations are unstable, regime-dependent, and backward-looking.
  • State that output is research/educational, not financial advice.

Never

  • Recommend specific trades or portfolio allocations as advice.
  • Imply historical correlation will persist.

Examples

Pairwise correlation

Input:

What's the correlation between NVDA and AMD over the past year?

Expected output:

Downloads ~1y of prices, computes return correlation, reports the coefficient and window, and notes
that it is backward-looking and regime-dependent. Research-only, not advice.
Regime-conditional

Input:

How does the SPY-TLT correlation change in risk-off periods?

Expected output:

Computes rolling correlation and splits by regime (risk-on vs risk-off), reporting how the
relationship shifts, with hedging context and caveats. Not a recommendation.

Trust & telemetry

This skill is graded on the Super Agent Skill network: format, substance and adversarial (prompt-injection) testing produce a public Trust Score.

Reinstall or update with npx skills update, or pull the live graded version with npx super-agent install fin-stock-correlation.

© criptogus, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/fin-stock-correlation of criptogus/agent-evolve-network.

Open the folder on GitHubat commit d19b920

Compare with similar skills

Fin Stock Correlation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fin Stock Correlation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fin Stock Correlation this skillcriptogus/agent-evolve-network288—~891Automated safety check: PassMIT
Stock Correlationhimself65/finance-skills3.4k—~3.3kAutomated safety check: PassMIT
StockMagicCube/agentara515—~1.1kAutomated safety check: PassNone
Stock Analysisalirezarezvani/claude-skills28k—~8.5kAutomated safety check: PassMIT
Detecting Lateral Movement In Networkmukul975/Anthropic-Cybersecurity-Skills34k—~4.3kAutomated safety check: NotesApache-2.0
Performing Lateral Movement Detectionmukul975/Anthropic-Cybersecurity-Skills34k—~3.1kAutomated safety check: PassApache-2.0

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Questions about Fin Stock Correlation

What does Fin Stock Correlation do?

Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context. Fin Stock Correlation is an agent skill from criptogus/agent-evolve-network. Analyze stock correlations — co-movement discovery, return correlation, sector clustering, and rolling/regime-conditional realized correlation, with practical context.

When should I use Fin Stock Correlation?

Fin Stock Correlation fits situations like: the user asks for stock correlation analysis work.

How do I install Fin Stock Correlation in Claude Code?

Run `npx skills add criptogus/agent-evolve-network --skill fin-stock-correlation -a claude-code`. Or copy the skill folder (skills/fin-stock-correlation in criptogus/agent-evolve-network) into .claude/skills/fin-stock-correlation in your project. Claude Code loads it when a task matches its description.

How do I install Fin Stock Correlation in Codex?

Run `npx skills add criptogus/agent-evolve-network --skill fin-stock-correlation -a codex`. Or copy the skill folder (skills/fin-stock-correlation in criptogus/agent-evolve-network) into .agents/skills/fin-stock-correlation in your project. Codex loads it when a task matches its description.

Can I use Fin Stock Correlation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add criptogus/agent-evolve-network --skill fin-stock-correlation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fin-stock-correlation, .gemini/skills/fin-stock-correlation, .github/skills/fin-stock-correlation and .opencode/skills/fin-stock-correlation in your project.

What does Fin Stock Correlation need to run?

Going by SKILL.md and its folder, Fin Stock Correlation needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Fin Stock Correlation access the network?

SKILL.md names 1 domain. As links in the text: superagentskill.com. This is read from the text; nothing was executed.

Is Fin Stock Correlation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fin Stock Correlation use?

Fin Stock Correlation is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fin Stock Correlation use?

About 891 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Fin Stock Correlation?

Skills that share tags, products or a category with Fin Stock Correlation: Stock Correlation (himself65/finance-skills, 3.4k stars), Stock (MagicCube/agentara, 515 stars), Stock Analysis (alirezarezvani/claude-skills, 28k stars) and Detecting Lateral Movement In Network (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fin Stock Correlation?

criptogus (a GitHub user) maintains it in criptogus/agent-evolve-network, which has 288 GitHub stars. The repository holds 107 skills in this directory. The repository was last updated on September 9, 2026.

Source: criptogus/agent-evolve-network on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.