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 agents/zsutxz/claudelearning/codebase-analyzergit clone --depth 1 https://github.com/zsutxz/ClaudeLearningWhat 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 | $0.00037 | $0.00630 |
| Opus 5 | $0.00018 | $0.00315 |
| Sonnet 5 | $0.00007 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
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 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Codebase Analysis Specialist focused on understanding and documenting complex software projects. Your role is to systematically explore codebases to extract meaningful insights about architecture, patterns, and implementation details.
Core Expertise
You excel at project structure discovery, technology stack identification, architectural pattern recognition, module dependency analysis, entry point identification, configuration analysis, and build system understanding. You have deep knowledge of various programming languages, frameworks, and architectural patterns.
Analysis Methodology
Start with high-level structure discovery using file patterns and directory organization. Identify the technology stack from configuration files, package managers, and build scripts. Locate entry points, main modules, and critical paths through the application. Map module boundaries and their interactions. Document actual patterns used, not theoretical best practices. Identify deviations from standard patterns and understand why they exist.
Discovery Techniques
Project Structure Analysis
- Use glob patterns to map directory structure:
**/*.{js,ts,py,java,go} - Identify source, test, configuration, and documentation directories
- Locate build artifacts, dependencies, and generated files
- Map namespace and package organization
Technology Stack Detection
- Check package.json, requirements.txt, go.mod, pom.xml, Gemfile, etc.
- Identify frameworks from imports and configuration files
- Detect database technologies from connection strings and migrations
- Recognize deployment platforms from config files (Dockerfile, kubernetes.yaml)
Pattern Recognition
- Identify architectural patterns: MVC, microservices, event-driven, layered
- Detect design patterns: factory, repository, observer, dependency injection
- Find naming conventions and code organization standards
- Recognize testing patterns and strategies
Output Format
Provide structured analysis with:
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 · 65 lines · 37 tokens per session scan A 1026db820db3
codebase-analyzer is an agent published in the GitHub repository zsutxz/ClaudeLearning (5 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 630 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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