Agent skill

Stock Performance

Retrieve stock price data and performance metrics using Octagon MCP.

MITAuto-check passedBusiness, Finance & HR

Install Stock Performance

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a claude-code

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

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance --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/OctagonAI/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/stock-performance .claude/skills/stock-performance && 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/

Paste into Claude Code

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .claude/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/OctagonAI/skills/tree/main/skills/stock-performance

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/stock-performance .agents/skills/stock-performance && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .agents/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/stock-performance .cursor/skills/stock-performance && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .cursor/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/OctagonAI/skills.git --path skills/stock-performance

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/stock-performance .gemini/skills/stock-performance && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .gemini/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/stock-performance .github/skills/stock-performance && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .github/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add OctagonAI/skills --skill stock-performance -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install OctagonAI/skills stock-performance --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/stock-performance .opencode/skills/stock-performance && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "stock-performance" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/stock-performance into .opencode/skills/stock-performance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-performance", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: stock-performance
GitHub stars: 127
Token cost: ~1.6k tokens
SKILL.md length: 539 words
Files: 5 (incl. references)
Skills in repo: 53
Repo updated: Jun 5, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Retrieve stock price data and performance metrics using Octagon MCP.

Works in 4 steps: Identify Analysis Parameters → Execute Query via Octagon MCP → Expected Output → …
Analyzing daily closing prices
SKILL.md covers Prerequisites, Workflow, Example Queries and Key Metrics, plus 6 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Stock Performance is an agent skill from OctagonAI/skills. Retrieve stock price data and performance metrics using Octagon MCP. Use when analyzing daily closing prices, trading volume, price trends, historical performance, and comparing stock movements over specific time periods.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `README.md`, `marketplace.json` and `references/interpreting-results.md`).

It sits in Business, Finance & HR, covering OKRs and executive reporting, Trading and backtesting and MCP servers. It works with Model Context Protocol. The repository describes itself as: A collection of Claude skills for agentic financial research by Octagon. The licence is MIT.

When your agent uses it

Analyzing daily closing prices
Historical performance
Comparing stock movements over specific time periods

Example prompts

“/stock-performance”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Identify Analysis Parameters
Execute Query via Octagon MCP
Expected Output
Interpret Results

What it can do on your machine

Read from SKILL.md and the folder at commit 51e938c. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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

Stock Performance loads about 1.6k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 60 tokens; SKILL.md has 539 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~60
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3.7k

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 OctagonAI/skills at commit 51e938c, republished under its MIT licence (© OctagonAI). 539 words, ~1,574 tokens.

Download SKILL.mdSave it as .claude/skills/stock-performance/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: stock-performance
description: Retrieve stock price data and performance metrics using Octagon MCP. Use when analyzing daily closing prices, trading volume, price trends, historical performance, and comparing stock movements over specific time periods.

Stock Performance

Retrieve daily closing prices, trading volume, and performance metrics for public companies using the Octagon MCP server.

Prerequisites

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.

Workflow

1. Identify Analysis Parameters

Determine the following before querying:

Ticker: Stock symbol (e.g., AAPL, MSFT, GOOGL)
Time Period: Number of days or date range
Metrics (optional): Price, volume, returns

2. Execute Query via Octagon MCP

Use the octagon-agent tool with a natural language prompt:

Retrieve the daily closing prices for <TICKER> over the last <N> days.

MCP Call Format:

json

{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve the daily closing prices for AAPL over the last 30 days."
  }
}

3. Expected Output

The agent returns structured price data including:

Date	Closing Price	Volume
2026-02-02	$270.01	73,677,607
2026-01-30	$259.48	92,443,408
2026-01-29	$258.28	67,253,009
...	...	...

Data Sources: octagon-stock-data-agent, octagon-web-search-agent

4. Interpret Results

See references/interpreting-results.md for guidance on:

Analyzing price trends
Evaluating volume patterns
Calculating returns
Identifying support/resistance levels

Example Queries

Daily Closing Prices:

Retrieve the daily closing prices for AAPL over the last 30 days.

Extended Historical Data:

Get historical stock prices for MSFT for the past 90 days.

Volume Analysis:

Retrieve daily trading volume for TSLA over the last 2 weeks.

Price Range:

What are the high and low prices for NVDA over the past month?

Multi-Stock Comparison:

Compare the stock performance of AAPL, MSFT, and GOOGL over the last 30 days.

52-Week Analysis:

What is the 52-week high and low for AMZN?

Key Metrics

Price Metrics

Metric	Description
Closing Price	End-of-day price
Opening Price	Start-of-day price
High	Intraday high
Low	Intraday low
Adjusted Close	Dividend/split adjusted

Volume Metrics

Metric	Description
Daily Volume	Shares traded per day
Average Volume	Typical daily volume
Relative Volume	Current vs. average
Volume Trend	Direction over time

Return Metrics

Metric	Calculation
Daily Return	(Close - Prior Close) / Prior Close
Period Return	(End - Start) / Start
Cumulative Return	Running return over period
Annualized Return	Period return scaled to 1 year

Price Analysis Framework

Trend Analysis

Pattern	Characteristics
Uptrend	Higher highs, higher lows
Downtrend	Lower highs, lower lows
Sideways	Range-bound movement
Breakout	Move beyond range

Volatility Assessment

Measure	Description
Price Range	High - Low over period
Daily Range	Average daily high-low
Standard Deviation	Price dispersion
Beta	Relative to market

Support/Resistance

Level	Description
Support	Price floor, buying interest
Resistance	Price ceiling, selling pressure
Moving Averages	Dynamic support/resistance
Round Numbers	Psychological levels

Volume Analysis

Volume Patterns

Pattern	Interpretation
High Volume + Price Up	Strong buying conviction
High Volume + Price Down	Strong selling pressure
Low Volume + Price Up	Weak rally, may reverse
Low Volume + Price Down	Lack of selling interest

Show full SKILL.md (207 more words)Show less

Volume Indicators

Indicator	Usage
Volume Spike	Unusual activity, potential catalyst
Volume Dry-up	Consolidation, waiting mode
Volume Trend	Confirms price trend
On-Balance Volume	Cumulative volume direction

Time Period Analysis

Short-Term (1-30 Days)

Focus	Use Case
Recent Performance	Current momentum
Trading Signals	Entry/exit timing
News Impact	Event analysis
Volatility	Risk assessment

Medium-Term (1-6 Months)

Focus	Use Case
Trend Identification	Direction confirmation
Seasonality	Cyclical patterns
Earnings Impact	Quarterly effects
Sector Rotation	Relative performance

Long-Term (1+ Years)

Focus	Use Case
Major Trends	Secular moves
52-Week Range	Valuation context
Recovery/Decline	Major shifts
Dividend Yield	Income analysis

Comparative Analysis

Peer Comparison

Metric	What to Compare
Return	Relative performance
Volatility	Risk comparison
Correlation	Movement similarity
Volume	Liquidity comparison

Benchmark Comparison

Benchmark	Usage
S&P 500	Large cap reference
Sector ETF	Industry context
Nasdaq	Tech comparison
Russell 2000	Small cap reference

Analysis Tips

Consider context: Market conditions affect individual stocks.
Adjust for events: Earnings, dividends, splits affect prices.
Use volume confirmation: Price moves need volume support.
Multiple timeframes: Longer and shorter perspectives.
Compare to peers: Relative performance matters.
Watch key levels: Round numbers, 52-week highs/lows.

Use Cases

Trading analysis: Entry and exit timing
Performance tracking: Portfolio monitoring
Event analysis: Earnings, news impact
Volatility assessment: Risk evaluation
Peer comparison: Relative performance

Files

SKILL.md and 4 other files (references) in skills/stock-performance of OctagonAI/skills.

SKILL.md
README.md
marketplace.json
references/interpreting-results.md
references/mcp-setup.md

Open the folder on GitHubat commit 51e938c

Compare with similar skills

Stock Performance 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.

Stock Performance compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Stock Performance this skillOctagonAI/skills	127	—	~1.6k	Automated safety check: Pass	MIT	4 mo ago
Tradingview MCPatilaahmettaner/tradingview-mcp	5k	—	~1.3k	Automated safety check: Pass	MIT	yesterday
Okx Cex Marketdex-original/okx-agent-trade-kit	110	1 repos	~2.7k	Automated safety check: Pass	MIT	4 mo ago
Polymarket Tennislivetennisapi/livetennisapi-mcp	152	—	~3k	Automated safety check: Pass	MIT	4 days ago
Okx Sentiment Trackerdex-original/okx-agent-trade-kit	110	1 repos	~3.8k	Automated safety check: Pass	MIT	4 mo ago
Massing Bimibuilder/massing	122	—	~1.1k	Automated safety check: Pass	MIT	2 days ago

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Questions about Stock Performance

What does Stock Performance do?

Retrieve stock price data and performance metrics using Octagon MCP. Stock Performance is an agent skill from OctagonAI/skills. Retrieve stock price data and performance metrics using Octagon MCP.

When should I use Stock Performance?

Stock Performance fits situations like: analyzing daily closing prices; historical performance; comparing stock movements over specific time periods.

How do I install Stock Performance in Claude Code?

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

How do I install Stock Performance in Codex?

Run `npx skills add OctagonAI/skills --skill stock-performance -a codex`. Or copy the skill folder (skills/stock-performance in OctagonAI/skills) into .agents/skills/stock-performance in your project. Codex loads it when a task matches its description.

Can I use Stock Performance 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 OctagonAI/skills --skill stock-performance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stock-performance, .gemini/skills/stock-performance, .github/skills/stock-performance and .opencode/skills/stock-performance in your project.

What does Stock Performance need to run?

SKILL.md names no scripts, command-line tools or credentials: Stock Performance is instructions for the agent only.

Does Stock Performance access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Stock Performance 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 Stock Performance use?

Stock Performance is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Stock Performance use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.1k tokens, read only when the agent opens those files.

What are the alternatives to Stock Performance?

Skills that share tags, products or a category with Stock Performance: Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Okx Cex Market (dex-original/okx-agent-trade-kit, 110 stars), Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars) and Okx Sentiment Tracker (dex-original/okx-agent-trade-kit, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stock Performance?

OctagonAI (a GitHub organization) maintains it in OctagonAI/skills, which has 127 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on June 5, 2026.

Source: OctagonAI/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Stock Performance

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Stock Performance

Prerequisites

Workflow

1. Identify Analysis Parameters

2. Execute Query via Octagon MCP

3. Expected Output

4. Interpret Results

Example Queries

Key Metrics

Price Metrics

Volume Metrics

Return Metrics

Price Analysis Framework

Trend Analysis

Volatility Assessment

Support/Resistance

Volume Analysis

Volume Patterns

Volume Indicators

Time Period Analysis

Short-Term (1-30 Days)

Medium-Term (1-6 Months)

Long-Term (1+ Years)

Comparative Analysis

Peer Comparison

Benchmark Comparison

Analysis Tips

Use Cases

Files

Compare with similar skills

Similar skills

More from OctagonAI/skills

Related

Questions about Stock Performance