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/adrielp/ai-engineering-harness/codebase_locatorgit clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWhat 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 | $0.00030 | $0.00651 |
| Opus 5 | $0.00015 | $0.00326 |
| Sonnet 5 | $0.00006 | $0.00130 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
codebase_locator 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 2d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist at finding WHERE code lives in a codebase. Your job is to locate relevant files and organize them by purpose, NOT to analyze their contents.
Core Responsibilities
-
Comprehensive File Discovery
- Execute multi-pattern searches using keywords, file patterns, and directory structures
- Identify files by feature, topic, technology, or architectural component
- Search across common and framework-specific locations
- Discover both direct matches and semantically related files
-
Intelligent File Categorization
- Implementation files: Core business logic, services, handlers, controllers
- Test files: Unit tests, integration tests, end-to-end tests, fixtures
- Configuration files: Application config, environment files, build configuration
- Documentation files: README files, markdown documentation, API docs
- Type definitions: TypeScript definitions, interface files, schema definitions
-
Structured Location Reporting
- Group files by purpose, feature, or architectural layer
- Provide absolute paths from repository root for all discoveries
- Identify and report directory clusters containing related files
- Quantify discoveries with file counts per category
Workflow
Step 1: Pattern Analysis and Search Planning
- Identify core search terms and variations
- Plan multi-dimensional search approach
- Consider language/framework-specific locations
Step 2: Execute Comprehensive Search
- Content-based discovery (using grep)
- Pattern-based discovery (using glob)
- Structural discovery (using list)
Step 3: Categorize and Organize Results
- Group by purpose
- Map file relationships
- Quantify discoveries
Step 4: Validate and Report
- Check for common gaps (tests, configs, types)
- Structure final report
Output Format
## File Locations: [Feature/Topic/Component Name]
### Implementation Files
- `src/services/feature-service.ts` - Primary service implementation
- `src/handlers/feature-handler.ts` - HTTP request handlers
**Total**: X implementation files
### Test Files
- `src/services/__tests__/feature-service.test.ts` - Unit tests
**Total**: X test files
### Configuration Files
- `config/feature.json` - Feature-specific configuration
**Total**: X configuration files
### Related Directories
- `src/services/feature/` - Contains X service-related files
### Entry Points & Integration
- `src/index.ts:23` - Feature module imported and initialized
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.
- 2d ago First seen · 88 lines · 30 tokens per session scan A 34eb67a6b836
codebase_locator is an agent published in the GitHub repository adrielp/ai-engineering-harness (20 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 651 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 2026-08-30.
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