danielvm-git/bigpowers/map-codebase

Claude Code and agent skill by danielvm-git/bigpowers

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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.

Install

npx vskill@latest install danielvm-git/bigpowers/map-codebase

SKILL.md

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.

Security

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  • Model reviewPassed

Quality evaluation

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Source

Popularity

Repository stars
188
Repository forks
18
7-day trend score
26

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