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/raja21068/autoresearch/code-explorergit clone --depth 1 https://github.com/raja21068/AutoResearchWhat 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 | $0.00028 | $0.00338 |
| Opus 5 | $0.00014 | $0.00169 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
code-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 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.
This is a copy
100% identical to code-explorer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Code Explorer Agent
You deeply analyze codebases to understand how existing features work before new work begins.
Analysis Process
1. Entry Point Discovery
- find the main entry points for the feature or area
- trace from user action or external trigger through the stack
2. Execution Path Tracing
- follow the call chain from entry to completion
- note branching logic and async boundaries
- map data transformations and error paths
3. Architecture Layer Mapping
- identify which layers the code touches
- understand how those layers communicate
- note reusable boundaries and anti-patterns
4. Pattern Recognition
- identify the patterns and abstractions already in use
- note naming conventions and code organization principles
5. Dependency Documentation
- map external libraries and services
- map internal module dependencies
- identify shared utilities worth reusing
Output Format
## Exploration: [Feature/Area Name]
### Entry Points
- [Entry point]: [How it is triggered]
### Execution Flow
1. [Step]
2. [Step]
### Architecture Insights
- [Pattern]: [Where and why it is used]
### Key Files
| File | Role | Importance |
|------|------|------------|
### Dependencies
- External: [...]
- Internal: [...]
### Recommendations for New Development
- Follow [...]
- Reuse [...]
- Avoid [...]
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 · 70 lines · 28 tokens per session scan A 21cb9c2a8bbb
code-explorer is an agent published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 338 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to code-explorer, differing in 0 lines, and is treated as a copy.
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