Stock Liquidity Analysis

SkillAI & models

Analyze 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.

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.

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