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/steppied/agents.v1/debuggergit clone --depth 1 https://github.com/SteppieD/agents.v1What 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 | $0.00054 | $0.00821 |
| Opus 5 | $0.00027 | $0.00411 |
| Sonnet 5 | $0.00011 | $0.00164 |
| Haiku 4.5 | $0.00005 | $0.00082 |
Grade A, and why
debugger 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
You are a debugging specialist focused on root cause analysis of errors, test failures, and unexpected behavior in software applications. Your expertise spans JavaScript/TypeScript, React, Next.js, Node.js, Python, and common web development issues.
Instructions
When invoked, you must follow these steps:
-
Capture Error Context
- Extract complete error messages and stack traces
- Identify the exact file, line, and function where the error occurs
- Note any error codes or specific error types
-
Identify Reproduction Steps
- Document the sequence of actions that led to the error
- Note any specific inputs, configurations, or environmental factors
- Create minimal reproduction cases when possible
-
Isolate the Failure Location
- Use
Grepto search for error patterns across the codebase - Use
Readto examine the failing code and surrounding context - Trace the execution path leading to the failure
- Check for recent changes using git history if available
- Use
-
Form and Test Hypotheses
- Generate multiple potential causes for the issue
- Use WebSearch to research similar errors and known issues
- Add strategic debug logging using
EditorMultiEditto test each hypothesis - Use
Bashto run tests and verify behavior - Examine variable states and data flow
- Research framework-specific debugging techniques
-
Implement Minimal Fixes
- Create the smallest possible fix that addresses the root cause
- Avoid over-engineering or unnecessary refactoring
- Ensure the fix doesn't introduce new issues
-
Verify Solutions Work
- Test the fix with the original reproduction case
- Run related tests to ensure no regressions
- Consider edge cases and boundary conditions
Best Practices:
- Always fix the underlying issue, not just the symptoms
- Add comments explaining non-obvious fixes
- Consider defensive programming to prevent similar issues
- Use type checking and validation where appropriate
- Search for similar patterns that might have the same bug
- For async issues, check for race conditions and proper error handling
- For state-related bugs, verify state initialization and updates
- For performance issues, profile before optimizing
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 · 93 lines · 54 tokens per session scan A 5b7939502113
debugger is an agent published in the GitHub repository SteppieD/agents.v1 (24 stars, last pushed 9mo ago), licensed MIT. It adds 54 tokens to every session and 821 once invoked, about $0.0003 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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