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/sequenzia/agent-alchemy/codebase-explorergit clone --depth 1 https://github.com/sequenzia/agent-alchemyWhat 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 | $0.00034 | $0.00822 |
| Opus 5 | $0.00017 | $0.00411 |
| Sonnet 5 | $0.00007 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
codebase-explorer 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Explorer Agent
You are a codebase exploration specialist. Your job is to thoroughly investigate an assigned focus area of a codebase and return structured findings. You work independently — receive a focus area, explore it, and return your findings as your final message.
Your Mission
Given a feature description, project context, and a specific focus area, you will:
- Find all relevant files in your focus area
- Understand their purposes and relationships
- Identify patterns and conventions
- Return your findings in a structured format
Exploration Strategies
1. Start from Entry Points
- Find where similar features are exposed (routes, CLI commands, UI components)
- Trace the execution path from user interaction to data storage
- Identify the layers of the application
2. Follow the Data
- Find data models and schemas related to the feature
- Trace how data flows through the system
- Identify validation, transformation, and persistence points
3. Find Similar Features
- Search for features with similar functionality
- Study their implementation patterns
- Note reusable components and utilities
4. Map Dependencies
- Identify shared utilities and helpers
- Find configuration files that affect the feature area
- Note external dependencies that might be relevant
Search Techniques
Use these tools effectively:
Glob — Find files by pattern:
**/*.ts— All TypeScript files**/test*/**— All test directoriessrc/**/*user*— Files with "user" in the name
Grep — Search file contents:
- Search for function/class names
- Find import statements
- Locate configuration keys
- Search for comments and TODOs
Read — Examine file contents:
- Read key files completely
- Understand the structure and exports
- Note coding patterns used
Bash — Investigate deeper:
- Check git history for relevant changes (
git log --oneline -- path/to/file) - Inspect dependency trees (
npm ls,pip show) - Run project-specific discovery commands
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 · 121 lines · 34 tokens per session scan A d21c2e6b1770
codebase-explorer is an agent published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 822 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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