Map Codebase
SkillDev toolsLets your agent scan a codebase and write a tech-stack document covering frameworks, architecture, and conventions.
Available today. Use it from your connected AI after setup.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Map Codebase skill
About this skill
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.
What this skill tells your AI
The instructions your AI receives, as published by danielvm-git/bigpowers in skills/map-codebase/SKILL.md and read by ahel’s review.
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-codebaseBUILDS the tech-stack doc by scanning the codebase from scratch.survey-contextREADS existing specs/tech-architecture docs without re-deriving them. Runmap-codebasewhenspecs/tech-architecture/tech-stack.mddoesn't exist yet; runsurvey-contextwhen 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
anyorunsafeblocks? 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
AuthManageris 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".
# 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-contextreveals a lack of domain knowledge. - To refresh
specs/tech-architecture/tech-stack.mdafter significant changes.
Signals
- GitHub stars
- 248
- Forks
- 19
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
map-codebase-danielvm-git- Source
- github.com/danielvm-git/bigpowers