Stock Liquidity Analysis
SkillAI & modelsAnalyze stock liquidity — full dashboard, bid-ask spread, volume, order-book depth, market-impact estimates, and turnover ratio, with practical execution guidance. Use when the user asks for stock liquidity analysis work, or mentions fin, stock, liquidity.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Stock Liquidity Analysis skill
What this skill tells your AI
The instructions your AI receives, as published by criptogus/agent-evolve-network in skills/fin-stock-liquidity/SKILL.md and read by ahel’s review.
Use this skill when a user wants to assess how liquid a stock is and how costly it is to trade: a full liquidity dashboard, bid-ask spread analysis (including options-spread context), volume analysis, order-book depth, market-impact estimates for a given order size, or turnover ratio.
It fetches quote/volume data, computes the relevant liquidity metrics, and provides practical execution guidance (e.g. slicing large orders, expected slippage) where relevant. Output is research/educational only, not financial advice; it does not recommend trades.
Instructions
You are a market-microstructure / liquidity analyst. Step 1 - Ensure dependencies are available (e.g. yfinance, numpy, pandas). Step 2 - Route to the correct sub-skill: (A) Liquidity Dashboard — compute all key metrics at once; (B) Spread Analysis — current bid-ask spread from the quote plus options-spread context; (C) Volume Analysis — average/median volume, dollar volume, trends; (D) Order Book Depth — from available depth data; (E) Market Impact — estimate impact/slippage for a given order size; (F) Turnover Ratio. Apply sensible defaults for windows. Step 3 - Fetch data and compute the metrics for the chosen sub-skill. Step 4 - Respond: always include the computed metrics and the period/assumptions used; always caveat that liquidity varies intraday and estimates are approximate. Offer practical execution guidance (order slicing, expected slippage) when relevant. Research/educational only, not financial advice; do not recommend trades.
Always
- Fetch quote/volume data and compute liquidity metrics rather than answering from memory.
- State the period/assumptions used and that estimates are approximate.
- State that output is research/educational, not financial advice.
Never
- Recommend specific trades or order routing as financial advice.
- Present market-impact estimates as precise guarantees.
Examples
Liquidity dashboard
Input:
How liquid is SNDK?
Expected output:
Computes the dashboard (average dollar volume, spread, turnover) and summarizes whether the name is
liquid or thin, with caveats on intraday variation. Research-only, not advice.
Market impact
Input:
What's the expected slippage if I buy $5M of this stock?
Expected output:
Estimates market impact for the order size relative to average volume, reports approximate slippage
and suggests order slicing, noting the estimate is approximate. 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.
- Trust Score & evidence: https://superagentskill.com/marketplace/trust/fin-stock-liquidity
- Skill page: https://superagentskill.com/marketplace/fin-stock-liquidity
- Live version (always current) via MCP: https://superagentskill.com/api/mcp
Reinstall or update with npx skills update, or pull the live graded version with
npx super-agent install fin-stock-liquidity.
Signals
- GitHub stars
- 308
- Forks
- 1
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
fin-stock-liquidity- Source
- github.com/criptogus/agent-evolve-network