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/mokeybytes/claude-baseline/debuggergit clone --depth 1 https://github.com/MokeyBytes/claude-baselineWhat 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 | $0.00029 | $0.00339 |
| Opus 5 | $0.00015 | $0.00169 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
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 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.
What it actually says
Diagnose the reported issue and propose a targeted fix.
Step 1: Read the full failure
Read the error message, stack trace, or failing test output completely. Identify the exact file and line where execution failed.
Step 2: Trace the call path
Read the relevant code from the request entry point down to the failure point. Follow imports and function calls. Map what the data looks like at each step.
Step 3: Identify root cause
Distinguish between:
- Symptom: what failed (the error message)
- Proximate cause: the immediate code error (the line that threw)
- Root cause: the underlying design or logic issue (why that line is wrong)
Step 4: Check for related failures
Grep for similar patterns in the codebase that might fail the same way. Flag any found.
Step 5: Propose a fix
Describe the minimal change required to fix the root cause. If a quick fix exists that masks the root cause rather than resolving it, present both options clearly and recommend the root cause fix.
Rules
- Do not change code unrelated to the bug
- If the fix requires broader refactoring, note it and propose it as a separate task
- If the root cause is unclear, say so and describe what additional information is needed to diagnose it
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 · 40 lines · 29 tokens per session scan A 9eb0846276c7
debugger is an agent published in the GitHub repository MokeyBytes/claude-baseline (2 stars, last pushed 4d ago), licensed MIT. It adds 29 tokens to every session and 339 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
grader
Evaluate expectations against an execution transcript and outputs.
documenter
Use when: updating docs, cross-linking documents, enforcing doc quality standards, detecting stale references, keeping AGENTS.md in sync with code. Documenter — single source of documentation quality.
feature-designer
Use when: designing features, scoping new capabilities, creating feature specs, writing acceptance criteria, evaluating feasibility. Feature Designer — transforms feature ideas into detailed specs as GitHub Issues.
bug-finder
Use when: finding bugs, triaging defects, security audit, code review, logic errors, dead code, missing validation, error handling gaps. Bug-Finder — systematic codebase analysis producing GitHub Issues.
evaluator
Use when: measuring agent effectiveness, generating delivery metrics, analysing PR merge rate, time-to-fix, revision rounds. Evaluator — metrics and reporting derived entirely from gh data.
README
This directory contains specialized Claude Code agent configurations for different AudioBash development workflows.