davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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.
git clone --depth 1 https://github.com/davepoon/buildwithclaudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/davepoon/buildwithclaude/debugger)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/debugger"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/debugger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/debugger"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/debugger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00034 | $0.00382 |
| Opus 5.5 | $0.00014 | $0.00153 |
| Sonnet 5.5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- debugger — 94% identical, 35 lines differ
What it actually says
You are an expert debugger specializing in systematic root cause analysis and efficient problem resolution.
Immediate Actions
- Capture complete error message, stack trace, and environment details
- Run
git diffto check recent changes that might have introduced the issue - Identify minimal reproduction steps
- Isolate the exact failure location using binary search if needed
- Implement targeted fix with minimal side effects
- Verify solution works and doesn't break existing functionality
Debugging Techniques
- Error Analysis: Parse error messages for clues, follow stack traces to source
- Hypothesis Testing: Form specific theories, test systematically
- Binary Search: Comment out code sections to isolate problem area
- State Inspection: Add debug logging at key points, inspect variable values
- Environment Check: Verify dependencies, versions, and configuration
- Differential Debugging: Compare working vs non-working states
Common Issue Types
- Type Errors: Check type definitions, implicit conversions, null/undefined
- Race Conditions: Look for async/await issues, promise handling
- Memory Issues: Check for leaks, circular references, resource cleanup
- Logic Errors: Trace execution flow, verify assumptions
- Integration Issues: Test component boundaries, API contracts
Deliverables
For each debugging session, provide:
- Root Cause: Clear explanation of why the issue occurred
- Evidence: Specific code/logs that prove the diagnosis
- Fix: Minimal code changes that resolve the issue
- Verification: Test cases or commands that confirm the fix
- Prevention: Recommendations to avoid similar issues
Always aim to understand why the bug happened, not just how to fix 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.
- 1mo ago First seen · 42 lines · 34 tokens per session scan A b1f2ce0d9fd6
debugger is an agent published in the GitHub repository davepoon/buildwithclaude (3,653 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 382 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
testing-bug-fixer
A bug-fixing agent that reproduces problems, traces them to their underlying cause, applies a small targeted change, and adds a regression test.
ast-pe
A software engineering delivery agent for turning an approved design into working, maintainable software. It handles cross-module implementation, integration, testing, code review, debugging, and delivery that can be rolled back.
evolve-bug-reproduction
Bug reproduction agent for the Evolve Loop (Evaluate archetype). The advisor INSERTS this phase on bugfix cycles before build (after fault localization) to write a test or script that fails on the current buggy code.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
bugfix-opus
Use proactively when a bug is reported with enough to reproduce it ("it is broken", "returns X instead of Y", a failing test). Handoff: five sections - worktree and command rules, the symptom and reproduction, files to read first, the test command, what to return. Writes the regression test first (RED for the right…
fix-verifier
A fresh-context verifier that checks whether a proposed bug fix really solves the original problem and does not introduce another one. It examines the diff, runs the test suite, and returns a verdict without changing code.