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Map Codebase

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danielvm-git
map-codebase

Derives the tech-stack doc from scratch by scanning the codebase — analyzes stack, architecture, and gray areas (error handling, API shapes) and persists findings into specs/tech-architecture/tech-stack.md. Run when the tech doc doesn't exist yet; use survey-context to consume it once it does.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namemap-codebase
Stars
206
Forks
18
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Map Codebase AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/map-codebase .claude/skills/map-codebase
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Map Codebase in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Map Codebase on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Map Codebase is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Map Codebase

Perform a deep architectural and structural analysis of the codebase. Unlike survey-context which identifies "where we are", map-codebase identifies "what we are dealing with" and "how things are done".

Use this vs survey-context: map-codebase BUILDS the tech-stack doc by scanning the codebase from scratch. survey-context READS existing specs/tech-architecture docs without re-deriving them. Run map-codebase when specs/tech-architecture/tech-stack.md doesn't exist yet; run survey-context when it does.

HARD GATE — Cold analysis only. Do NOT assume architectural patterns without reading the code. If the codebase structure surprises you, call out the delta.

Process

1. Identify Core Stack & Dependencies

  • Scan package.json, Cargo.toml, requirements.txt, etc.
  • Identify primary framework, runtime, and critical libraries (ORM, Auth, State, UI).
  • Note version constraints and any deprecated or unusual dependencies.

2. Map High-Level Architecture

  • Identify the entry points (CLI, Web, API).
  • Map the primary data flow (e.g., Controller → Service → Repository).
  • Identify where business logic lives vs. where I/O lives.
  • Look for established patterns (e.g., hexagonal, layered, feature-folders).

3. Analyze "Gray Areas" (The "How")

Search for patterns and anti-patterns in these categories:

  • Error Handling: Are exceptions caught early or bubbled? Is there a global error handler? Are error messages structured?
  • API Shapes: Is it REST, GraphQL, or RPC? What is the casing (camelCase, snake_case)? How are responses structured?
  • Type Safety: Is it strictly typed? Are there many any or unsafe blocks? Are interfaces used for DIP?
  • Observability: Is there structured logging? Are there health checks? Where do logs go?
  • Testing: What is the test coverage strategy? Are mocks used? Where do tests live?

4. Identify Planning "Signals"

Look for signals that will influence upcoming plans:

  • Consistency Gaps: "Half the project uses async/await, the other half uses Promises."
  • Debt Hotspots: "The AuthManager is 1500 lines and handles both JWT and session logic."
  • Integration Points: "We need to talk to the Stripe API, but there's no wrapper yet."
  • Conventions: "The team always uses functional components over classes."

5. Persist to specs/tech-architecture/tech-stack.md

Compile all findings into specs/tech-architecture/tech-stack.md. This file serves as the project's "Long-Term Memory".

markdown
# Project Context

## Stack
- [Framework/Language]
- [Key Libraries]

## Architecture
- [Pattern Description]
- [Data Flow]

## Conventions (Observed)
- [Error Handling Pattern]
- [API Design]
- [Type System]

## Signals / Active Considerations
- [Gap 1]
- [Hotspot 2]

When to Use

  • When first joining a project.
  • Before a major refactor or architectural change.
  • When survey-context reveals a lack of domain knowledge.
  • To refresh specs/tech-architecture/tech-stack.md after significant changes.

Frequently asked questions

What does the Map Codebase AI skill do?

Derives the tech-stack doc from scratch by scanning the codebase — analyzes stack, architecture, and gray areas (error handling, API shapes) and persists findings into specs/tech-architecture/tech-stack.md. Run when the tech doc doesn't exist yet; use survey-context to consume it once it does.

Why use Map Codebase on TypingMind?

Because you install it once and use it with any model. Map Codebase is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Map Codebase in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielvm-git/bigpowers/tree/main/skills/map-codebase. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Map Codebase?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Map Codebase?

As many as you like. As long as a model supports skills, you can use Map Codebase with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Map Codebase AI skill free?

Yes. It is published on GitHub by danielvm-git under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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