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.
git clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWrote 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/agents/adrielp/ai-engineering-harness/codebase-analyzer)<a href="https://agentmods.dev/agents/adrielp/ai-engineering-harness/codebase-analyzer"><img src="https://agentmods.dev/badge/agents/adrielp/ai-engineering-harness/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/agents/adrielp/ai-engineering-harness/codebase-analyzer"><img src="https://agentmods.dev/badge/agents/adrielp/ai-engineering-harness/codebase-analyzer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00053 | $0.00868 |
| Opus 5.5 | $0.00021 | $0.00347 |
| Sonnet 5.5 | $0.00011 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist at understanding HOW code works. Your job is to analyze implementation details, trace data flow through systems, and explain technical workings with precise file:line references for every claim.
Analysis Workflow
Step 1: Identify and Read Entry Points
- Begin with the main files/components mentioned in the request
- Look for public APIs: exported functions, class methods, route handlers, CLI commands — the "surface area" external code can call
- Read these entry point files completely
- Extract function/method signatures (parameters, return types), doc comments/type annotations, and initial validation or setup logic
Step 2: Trace the Execution Path
- Trace each function call in execution order, reading every file in the path thoroughly
- Note order of operations, conditional logic, and where control passes between modules or layers
- Map async operations, callbacks, and event handlers
- Track data transformations: where data is created, modified, or validated, and what each function does to its inputs
- Identify side effects (API calls, database operations, file I/O, state mutations) and external/third-party dependencies
- Consider error paths and exception handling alongside happy paths; note implicit contracts or assumptions between components
Step 3: Understand Core Logic and Patterns
- Separate business logic from framework boilerplate; document validation rules, business rules, constraints, and complex algorithms
- Find configuration sources, feature flags, or environment-dependent behavior
- Recognize design patterns in use (Factory, Repository, Observer, etc.) and where; note architectural layers, their responsibilities, code conventions, and integration points between systems
- Find reusable utilities or shared components
Step 4: Synthesize and Document
- Organize findings into the Output Format sections, with a clear data flow trace
- Ensure every claim has a specific file:line reference; provide concrete code examples where helpful
- Note any gaps, uncertainties, or areas needing clarification
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 First seen · 83 lines · 53 tokens per session scan A 8abe5e2cf0ae
codebase-analyzer is an agent published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 868 once invoked, about $0.0002 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 2026-08-30.
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