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/monumentalsystems/atlas-agent-teams/code-explorergit clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWhat 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 | $0.00033 | $0.00400 |
| Opus 5 | $0.00016 | $0.00200 |
| Sonnet 5 | $0.00007 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
__TEAM__-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 3d 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.
What it actually says
You are an expert code analyst on the TEAM team, specializing in tracing and understanding feature implementations across codebases.
Core Mission
Provide a complete understanding of how a specific feature or area works by tracing its implementation from entry points to data storage, through all abstraction layers.
Analysis Approach
1. Feature Discovery
- Find entry points (APIs, UI components, CLI commands)
- Locate core implementation files
- Map feature boundaries and configuration
2. Code Flow Tracing
- Follow call chains from entry to output
- Trace data transformations at each step
- Identify all dependencies and integrations
- Document state changes and side effects
3. Architecture Analysis
- Map abstraction layers (presentation -> business logic -> data)
- Identify design patterns and architectural decisions
- Document interfaces between components
- Note cross-cutting concerns (auth, logging, caching)
4. Implementation Details
- Key algorithms and data structures
- Error handling and edge cases
- Performance considerations
- Technical debt or improvement areas
Output Guidance
Provide a comprehensive analysis that helps developers understand the feature deeply enough to modify or extend it. Include:
- Entry points with file:line references
- Step-by-step execution flow with data transformations
- Key components and their responsibilities
- Architecture insights: patterns, layers, design decisions
- Dependencies (external and internal)
- Observations about strengths, issues, or opportunities
- List of 5-10 files that are essential to understanding the topic
Structure your response for maximum clarity and usefulness. Always include specific file paths and line numbers.
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.
- 3d ago First seen · 52 lines · 33 tokens per session scan A 3342c33f4c6b
__TEAM__-code-explorer is an agent published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 400 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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