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/quangphu1912/codebase-analyzer/mapping-architecturenpx skills add quangphu1912/codebase-analyzer --skill mapping-architecturegit clone --depth 1 https://github.com/quangphu1912/codebase-analyzerWhat 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 | $0.00031 | $0.00980 |
| Opus 5 | $0.00015 | $0.00490 |
| Sonnet 5 | $0.00006 | $0.00196 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
mapping-architecture 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 2d ago.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Announce at start: "Using codebase-analyzer to map the architecture."
Overview
Map the codebase's structural architecture: components, layers, boundaries, and communication patterns.
Process
- Identify entry points (main files, index files, routing, public API exports)
- Map directory structure to logical layers (presentation, business, data, infrastructure)
- Trace component boundaries — which dirs/modules form cohesive units
- Identify cross-cutting concerns (auth, logging, config, error handling)
- Map communication between components (imports, function calls, events, message queues)
- Find shared state and coupling points
Quick Reference
Directory names suggest layers, but imports reveal the truth. Use the table as a starting hypothesis, then verify with actual import analysis.
| Signal | Hypothesized Layer | What to verify |
|---|---|---|
| routes/, pages/, views/, controllers/ | Presentation | Do these import only from business/data, or do they bypass directly to infrastructure? |
| services/, domain/, business/, use-cases/ | Business Logic | Do these stay free of framework/DB imports, or are they coupled to infrastructure? |
| models/, repositories/, data/, db/ | Data Access | Do these have business logic mixed in (validations, calculations in models)? |
| config/, middleware/, utils/, lib/ | Infrastructure | Check fan-in: if everything imports from here, it may be a hidden god module |
| tests/, tests/, spec/ | Test | Are tests colocated with code (unit) or separated (integration)? |
Trigger Signals
- HIGH confidence: Generated code mixed with hand-written (e.g., GraphQLcodegen output in src/) ->
trace-codebase-provenance - HIGH confidence: Hidden/private modules not referenced from entry points ->
classify-repo-artifacts - MEDIUM confidence: Unclear layer boundaries, tangled imports ->
tracing-dependencies - LOW confidence: Clean separation, well-defined layers -> no deep dive needed
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 84 lines · 31 tokens per session scan A 7123d04093c5
mapping-architecture is a skill published in the GitHub repository quangphu1912/codebase-analyzer (2 stars, last pushed 4mo ago), licensed MIT. It adds 31 tokens to every session and 980 once invoked, about $0.0002 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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