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 skills add notque/vexjoy-agent --skill codebase-analyzergit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/codebase-analyzer)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/codebase-analyzer"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/codebase-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/notque/vexjoy-agent/codebase-analyzer"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/codebase-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00015 | $0.01777 |
| Opus 5.5 | $0.00006 | $0.00711 |
| Sonnet 5.5 | $0.00003 | $0.00355 |
| Haiku 4.5 | $0.00002 | $0.00178 |
Grade A, and why
codebase-analyzer 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 1mo 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Analyzer Skill
Statistical rule discovery through measurement of Go codebases. Python scripts count patterns to avoid LLM training bias, then statistics are interpreted to derive confidence-scored rules. The core principle is Measure First, Interpret Second -- what IS in the code is the local standard, not what an LLM thinks "should be" there.
Deep References
Load on demand when the corresponding signal appears.
| Signal | Reference | Content |
|---|---|---|
| Three-lens methodology | references/three-lenses.md |
Consistency, Signature, Idiom lens details |
| Phase banners, error catalog, reconciliation | references/phase-details.md |
Phase templates, rule format, error catalog |
| 100-metric catalog | references/metrics-catalog.md |
All metrics across 25 categories |
| Worked examples | references/examples.md |
Single repo, multi-repo, evolution tracking workflows |
Instructions
Phase 1: CONFIGURE
Goal: Validate target and select analyzer variant.
Read and follow the repository's CLAUDE.md before doing anything else -- project instructions override default behaviors.
Step 1: Validate the target
- Confirm path points to a Go repository root with .go files
- Check for standard structure (cmd/, internal/, pkg/)
- Verify sufficient file count: 50+ files for meaningful rules, 100+ ideal. Below 50 files, statistics produce high variance -- patterns that look consistent may be coincidence. For small repos, combine analysis across multiple team repos rather than treating thin data as definitive.
Step 2: Select cartographer variant
| Variant | Script | Metrics | Use When |
|---|---|---|---|
| Omni (recommended) | cartographer_omni.py |
100 across 25 categories | Full codebase profiling |
| Basic | cartographer.py |
~15 categories | Quick pattern overview |
| Ultimate | cartographer_ultimate.py |
6 focused categories | Performance pattern detection |
Step 3: Verify environment
- Python 3.7+ available
- No external dependencies needed (uses only Python standard library)
- Output directories exist or can be created
What ships with it
7 files 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.
- 1mo ago Changed · -169 lines · +4 tokens per session a19aa88f755a
- 1mo ago Changed · -17 lines b9bec5f585be
- 1mo ago First seen · 203 lines · 15 tokens per session scan A 24044a621d75
codebase-analyzer is a skill published in the GitHub repository notque/vexjoy-agent (443 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 1,777 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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