Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/chianw/c31/gsd-map-codebasenpx skills add ChianW/C31 --skill gsd-map-codebasegit clone --depth 1 https://github.com/ChianW/C31What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00065 | $0.01172 |
| Opus 5 | $0.00032 | $0.00586 |
| Sonnet 5 | $0.00013 | $0.00234 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
gsd-map-codebase scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multilingual Triggers
| Language | Trigger phrases |
|---|---|
| EN | gsd-map-codebase |
| ZH | 分析代码库, 代码库分析 |
| JA | コードベース分析 |
Output language: Respond automatically in the user's conversation language.
Map an Existing Codebase
Produce a durable reference of an existing codebase so future work doesn't
start from zero context. The output is a set of Markdown files in
memory/.planning/codebase/.
When to Use
- User says "map this codebase", "analyze the repo", "what's in this project?"
- Before planning a refactor, migration, or major feature on a legacy codebase
- After inheriting a project with no documentation
- When onboarding a new contributor who needs orientation
Execution Flow
Phase 1: Scan
- Directory structure — Run
find . -maxdepth 3 -type f(or deeper as needed). Ignorenode_modules/,.git/,dist/,build/,__pycache__/,.venv/. - Tech stack markers — Look for:
package.json,requirements.txt,go.mod,Cargo.toml,Gemfile, etc.Dockerfile,docker-compose.yml,k8s/,helm/- Config files (
vite.config.*,webpack.config.*,tsconfig.json, etc.)
- Architecture signals — Count of services/modules, entry points, API definitions, database schema files, message queue configs, event bus usage.
Phase 2: Detect
Tech Stack (for STACK.md)
- Primary language(s) and versions
- Framework(s) and runtime
- Build tools and package managers
- Database(s), caches, message queues
- Deployment / CI platform(s)
- Testing frameworks
Architecture (for ARCHITECTURE.md)
- Pattern: monolith, modular monolith, microservices, serverless, etc.
- Communication style: REST, GraphQL, gRPC, events, message bus
- Data flow: where is state, where is compute, what is the boundary
- Key entry points and hot paths
Conventions (for CONVENTIONS.md)
- Naming: files, classes, functions, variables, branches
- Folder structure: by feature, by layer, by domain
- Import / module organization
- Code style (formatter, linter config if detectable)
- Commit message style if visible in git log
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 133 lines · 65 tokens per session scan A 65258ae13e0a
gsd-map-codebase is a skill published in the GitHub repository ChianW/C31 (1 stars, last pushed 6d ago), licensed MIT. It adds 65 tokens to every session and 1,172 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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