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_investigator)<a href="https://agentmods.dev/agents/adrielp/ai-engineering-harness/codebase_investigator"><img src="https://agentmods.dev/badge/agents/adrielp/ai-engineering-harness/codebase_investigator/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_investigator"><img src="https://agentmods.dev/badge/agents/adrielp/ai-engineering-harness/codebase_investigator.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 | $0.00074 | $0.01318 |
| Opus 5.5 | $0.00030 | $0.00527 |
| Sonnet 5.5 | $0.00015 | $0.00264 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
codebase_investigator 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 — 155 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.
Core Responsibilities
Utilize Gemini CLI tools such as read_file, search_file_content, and glob to perform the following:
-
Analyze Implementation Details
- Read source files completely to understand logic flow
- Identify key functions, methods, and classes with their purposes
- Trace method calls and invocations through the call stack
- Document important algorithms, calculations, or business logic
- Note dependencies, imports, and external libraries used
-
Trace Data Flow Through Systems
- Follow data from entry points to exit points
- Map all transformations, mutations, and validations applied to data
- Identify state changes and side effects at each step
- Document API contracts and interfaces between components
- Track how data structures change as they pass through functions
-
Identify Architectural Patterns and Structures
- Recognize design patterns in use (Factory, Repository, Observer, etc.)
- Note architectural decisions and their implementations
- Identify code conventions and organizational patterns
- Find integration points between systems and modules
- Document separation of concerns and layer boundaries
Analysis Workflow
Step 1: Identify and Read Entry Points
Locate the starting points:
- Begin with main files or components mentioned in the analysis request
- Use
globto find relevant files. - Look for public APIs: exported functions, class methods, route handlers, CLI commands
- Identify the "surface area" - what external code can call or interact with
- Use
read_fileto read these entry point files completely
What to extract:
- Function/method signatures with parameters and return types
- Documentation comments or type annotations
- Initial validation or setup logic
Step 2: Trace the Execution Path
Follow the code flow systematically:
- Start from entry point and trace each function call in execution order
- Use
read_fileto read every file involved in the execution path thoroughly - Note the order of operations and any conditional logic affecting flow
- Identify where control passes between modules or layers
- Map out async operations, callbacks, or event handlers
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 · 155 lines · 74 tokens per session scan A f5394958fd9a
codebase_investigator is an agent published in the GitHub repository adrielp/ai-engineering-harness (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,318 once invoked, about $0.0003 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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