How do I install the Extract API Surface skill?

Run npx skillmds@latest add oimiragieo/extract-api-surface in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Extract API Surface safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Extract API Surface?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Extract API Surface free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Extract API Surface?

oimiragieo (@oimiragieo) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Extract API Surface by oimiragieo — Agent Skill

Name: Extract API Surface
Author: oimiragieo

Extract API Surface

Extracts the public API of a codebase — class names, function signatures, exported types, module docstrings — without loading every file. Use this skill whenever the user asks to understand, explore, navigate, or describe a codebase, library, repository, or package at the overview level. Trigger phrases include "help me understand this codebase", "what does this library do", "what's the API of X", "how is this project structured", "give me an overview of Y", "explain this repo". Calls gotcontext's AST-aware skeleton extraction so you read the shape of the code, not the implementation bodies.

oimiragieo c67a2fc 2 files · 3.4 KB Updated Sep 17, 2026 0 repo stars

File contents

extract-api-surface

When this skill is the preferred path

The user wants to get oriented in a codebase quickly — they need to know what's there, not how each function is implemented. Reading every file just to answer "what does this library do" wastes context and misses the forest for the trees.

How to use it

Identify the target: a local directory (./src), a repo root, or a GitHub URL the user linked to.
Call read_skeleton (free-tier MCP tool) on the target path. You'll get function/class signatures, type definitions, module-level docstrings, and import maps — bodies stripped. For cross-file understanding across a larger tree, compress_codebase (Pro+) returns the same AST-aware digest for every file in the corpus at once.
If the skeleton is still too large (rare, large monorepos), pipe it through ingest_context at fidelity=balanced.
Present a structured overview:
- Top-level exports grouped by module
- Key types / interfaces
- Entry points (CLI, main, server)
- One-sentence purpose per module
- Total signature count (so the user knows the coverage)

When the raw read is actually fine

You're actively editing a specific file — you need the body.
You're debugging a specific function — skeleton hides it.
Configs, data fixtures, generated code — skeleton isn't meaningful; either skip or run ingest_context on them.

Language support

AST-native: Python, JavaScript, TypeScript. Line-based fallback: Java, Go, Rust, C++. Other languages fall back to regex slicing — surface that caveat to the user if you hit a non-native language.

See references/overview-template.md for the reporting format.

---name: extract-api-surfacedescription: Extracts the public API of a codebase — class names, function signatures, exported types, module docstrings — without loading every file. Use this skill whenever the user asks to understand, explore, navigate, or describe a codebase, library, repository, or package at the overview level. Trigger phrases include "help me understand this codebase", "what does this library do", "what's the API of X", "how is this project structured", "give me an overview of Y", "explain this repo". Calls gotcontext's AST-aware skeleton extraction so you read the shape of the code, not the implementation bodies.---# extract-api-surface## When this skill is the preferred pathThe user wants to get oriented in a codebase quickly — they need toknow what's there, not how each function is implemented. Reading everyfile just to answer "what does this library do" wastes context andmisses the forest for the trees.## How to use it1. Identify the target: a local directory (`./src`), a repo root, or a GitHub URL the user linked to.2. Call `read_skeleton` (free-tier MCP tool) on the target path. You'll get function/class signatures, type definitions, module-level docstrings, and import maps — bodies stripped. For cross-file understanding across a larger tree, `compress_codebase` (Pro+) returns the same AST-aware digest for every file in the corpus at once.3. If the skeleton is still too large (rare, large monorepos), pipe it through `ingest_context` at `fidelity=balanced`.4. Present a structured overview: - Top-level exports grouped by module - Key types / interfaces - Entry points (CLI, main, server) - One-sentence purpose per module - Total signature count (so the user knows the coverage)## When the raw read is actually fine- You're actively editing a specific file — you need the body.- You're debugging a specific function — skeleton hides it.- Configs, data fixtures, generated code — skeleton isn't meaningful; either skip or run `ingest_context` on them.## Language supportAST-native: Python, JavaScript, TypeScript. Line-based fallback: Java,Go, Rust, C++. Other languages fall back to regex slicing — surfacethat caveat to the user if you hit a non-native language.See `references/overview-template.md` for the reporting format.

Frequently asked questions