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/classify-repo-artifactsnpx skills add quangphu1912/codebase-analyzer --skill classify-repo-artifactsgit clone --depth 1 https://github.com/quangphu1912/codebase-analyzerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/quangphu1912/codebase-analyzer/classify-repo-artifacts)<a href="https://agentmods.dev/skills/quangphu1912/codebase-analyzer/classify-repo-artifacts"><img src="https://agentmods.dev/badge/skills/quangphu1912/codebase-analyzer/classify-repo-artifacts.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00036 | $0.01189 |
| Opus 5 | $0.00018 | $0.00594 |
| Sonnet 5 | $0.00007 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
classify-repo-artifacts 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 4d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Announce at start: "Using codebase-analyzer to classify repo artifacts."
Overview
Sort every module/file into categories: core, support, generated, test, infrastructure. Essential before deep analysis — don't waste tokens analyzing generated code.
Prerequisite: Reads docs/analysis/provenance.md and docs/analysis/architecture.md.
Process
- Load provenance map (which files are source vs derived)
- Tag each top-level directory/module with category
- Core: business logic, domain models, API handlers, data access
- Support: utilities, helpers, shared libraries, middleware
- Generated: build output, codegen, protobuf, graphql generated
- Test: test files, fixtures, mocks, test utilities
- Infrastructure: CI/CD, Docker, deployment scripts, config
- Calculate signal-to-noise ratio: core / total
Classification Heuristics
| Category | Indicators |
|---|---|
| Core | Business logic, domain types, API handlers, data models |
| Support | utils/, helpers/, lib/, shared/, common/ |
| Generated | "DO NOT EDIT", codegen output, dist/ contents |
| Test | .test., .spec., tests/, test/, tests/ |
| Infra | Dockerfile, docker-compose, .github/, k8s/, terraform/ |
Entropy Analysis
Information Density Entropy
Files with abnormally high information density (many distinct operations per line) are either highly optimized or obfuscated. Files with abnormally low density are scaffolding or generated. This is a signal, not a verdict.
How to measure: Count distinct syntactic operations (function calls, assignments, control flow, operators) per line of executable code. Compare against the repository median. Files more than 2 standard deviations from the mean deserve scrutiny.
High density (>2 sigma):
- Hand-optimized algorithms, dense data transforms — likely core
- Minified or obfuscated code — likely generated or vendored
- Cryptographic routines, parser combinators — specialized core
Low density (<2 sigma):
- Boilerplate-heavy frameworks, config classes — likely support or infra
- Auto-generated CRUD scaffolding — likely generated
- Re-export barrels, type-only files — likely support
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.
- 4d ago First seen · 101 lines · 36 tokens per session scan A c7f0e07e7be5
classify-repo-artifacts is a skill published in the GitHub repository quangphu1912/codebase-analyzer (2 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 1,189 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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