Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to \"map the codebase\", \"map codebase\", \"architecture report\", \"codebase overview\", \"architecture overview\", \"what am I looking at\", or \"explain this codebase\".
npx skills add https://github.com/tobihagemann/turbo --skill map-codebase
Deep architecture report. Fans out parallel inspections across different aspects of the codebase, synthesizes their findings, and writes .turbo/codebase-map.md and .turbo/codebase-map.html. Analysis-only.
At the start, use TaskCreate to create a task for each phase:
If $ARGUMENTS specifies paths, use those directly (skip the question).
Otherwise, use AskUserQuestion to confirm scope:
Once scope is determined, glob for source files in the selected scope. Exclude generated and vendored directories (node_modules/, dist/, build/, vendor/, __pycache__/, .build/, DerivedData/, target/, .tox/, and others appropriate to the project).
Build a file manifest grouped by top-level source directory. This manifest is shared with all agents as a starting point. Agents may explore beyond it based on what they discover.
Before dispatching, read the project's test configuration and CI workflow to identify any test tier that resets a shared external resource between tests, such as a database, a fixed port, or a cache. Such tiers have no cross-process interlock, so agents running them concurrently wipe each other's state and return failures that look like real defects. Name any such tier to every agent as off-limits.
Use the Agent tool to launch all 7 agents below in a single assistant message so they run concurrently. Run them in the foreground so all their results return in this turn. Each Agent call uses model: "opus" and no name. Each agent receives the scoped file manifest and its exploration brief, and its prompt directs it to treat the shared working tree and its git index as read-only — any empirical check runs in an isolated git worktree created under $TMPDIR and discarded afterward. Give that worktree its own dependency install rather than reaching the shared tree's install by any route: removing a worktree deletes through symlinks, and a redirected suite writes into the shared install. When its own install is not possible, the check is left unrun and reported as such. Afterward the agent verifies that git worktree list no longer shows the worktree, that git status --short is clean, and that the shared tree's dependency directory still resolves (a destroyed install leaves git status clean, since it is gitignored). Damage the agent cannot repair is reported with the exact repair command in place of findings.
Launch one agent per dimension. Each agent's prompt provides the file manifest and the exploration brief below. Agents explore adaptively: go deeper where complexity warrants it, stay high-level where things are straightforward. Findings should be concrete (reference specific files and directories) rather than generic.
| # | Dimension | Exploration Brief |
|---|---|---|
| 1 | Project Structure | Map directory layout, module organization, naming conventions, and file roles. Identify the organizing principle (feature-based, layer-based, hybrid). Note generated or build output directories. |
| 2 | Tech Stack and Build System | Identify languages, frameworks, package managers, build tools, and runtime requirements. Note version constraints and compatibility targets. |
| 3 | Entry Points and Public API | Find how the system is invoked: CLI commands, HTTP endpoints, event handlers, exported modules, main functions. Map the public surface area. |
| 4 | Core Abstractions and Patterns | Identify key types, classes, interfaces, and design patterns. Note architectural patterns (MVC, plugin system, pipeline, etc.) and how they shape the code. |
| 5 | Data Flow and State | Trace how data enters, transforms, persists, and exits the system. Identify state management approaches, storage layers, and data boundaries. |
| 6 | External Dependencies and Integrations | Map third-party services, APIs, databases, and system boundaries. Note how external dependencies are abstracted or coupled. |
| 7 | Testing and Quality Infrastructure | Describe the test strategy: frameworks, coverage approach, test organization, CI/CD pipeline. Note gaps or unusual patterns. |
Each agent writes its findings as structured markdown with sections and subsections.
After all agents complete:
.turbo/codebase-map.md using the report template. Output the executive summary as text before writing the file.# Codebase Map
**Date:** <date>
**Scope:** <what was inspected>
## Executive Summary
<3-5 sentences: what the system is, how it's organized, what architectural choices define it>
## Project Structure
<from dimension 1>
## Tech Stack
<from dimension 2>
## Entry Points and API
<from dimension 3>
## Core Abstractions
<from dimension 4>
## Data Flow
<from dimension 5>
## External Dependencies
<from dimension 6>
## Testing and Quality
<from dimension 7>
## Cross-Cutting Observations
<themes, trade-offs, and connections identified during synthesis>
Convert the markdown report into a styled, interactive HTML page.
/frontend-design skill to load design principles..turbo/codebase-map.md for the full report content..turbo/codebase-map.html (single file, no external dependencies beyond Google Fonts) that presents the architecture report with:@media printGuide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Take tobihagemann/map-codebase from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.