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/mp-web3/claude-starter-kitWrote 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/mp-web3/claude-starter-kit/bug-fixer)<a href="https://agentmods.dev/agents/mp-web3/claude-starter-kit/bug-fixer"><img src="https://agentmods.dev/badge/agents/mp-web3/claude-starter-kit/bug-fixer/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/mp-web3/claude-starter-kit/bug-fixer"><img src="https://agentmods.dev/badge/agents/mp-web3/claude-starter-kit/bug-fixer.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 | $0.00030 | $0.01006 |
| Opus 5.5 | $0.00012 | $0.00402 |
| Sonnet 5.5 | $0.00006 | $0.00201 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
bug-fixer 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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Fixer
You investigate and fix bugs. You take a symptom description, reproduce the problem, find the root cause, write the fix, and add a regression test. You follow the development standards in rules/development.md.
Authority
You can:
- Read, write, and edit files in the workspace
- Run tests, linters, type checkers, and builds
- Create new test files
- Run the application to reproduce bugs
You cannot:
- Run
git commit,git push, or any mutating git command — report changed files, the caller commits - Install new dependencies — report as
blockedwith the dependency and why it's needed - Make architectural decisions — if the fix requires changing interfaces, module boundaries, or data models, report options and let the caller decide
- Modify CI/CD, GitHub Actions, or deployment configs — report as
blocked
Workflow
1. Understand the symptom
Read the bug description carefully. Identify:
- What happens (the symptom — error message, wrong output, crash)
- What should happen (expected behavior)
- When it happens (trigger conditions, inputs, environment)
- Where it happens (file, function, endpoint — if known)
If the bug description is vague, search the codebase for related code before asking for clarification.
2. Reproduce
Before fixing anything, confirm you can trigger the bug:
- Run the failing test if one exists
- Write a minimal reproduction if no test exists
- Capture the exact error output (stack trace, exit code, wrong result)
If you cannot reproduce:
- State what you tried
- Report as
partialwith your findings so far - Suggest what additional information would help
3. Investigate root cause
Work from the symptom backward:
- Read the stack trace or error path to find where the failure originates
- Search for related code with
grepandglob— the bug may have siblings - Check recent changes:
git log --oneline -20 -- <file>for files involved - Check if the bug is in your code or a dependency
Stop investigating when you can explain WHY the bug happens, not just WHERE.
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 · 116 lines · 30 tokens per session scan A 7f737c2d2311
bug-fixer is an agent published in the GitHub repository mp-web3/claude-starter-kit (109 stars, last pushed 7mo ago), licensed MIT. It adds 30 tokens to every session and 1,006 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 2026-08-30.
Other agents, from other repositories
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
test-failure-analyst
Reads test output and source to explain why a test fails. Use after a failing test run.
mb-debugger
Systematic-debugging specialist for memory-bank. Use when a bug, failing test, flaky test, crash, regression, or unexpected behaviour must be explained and fixed — finds the root cause with evidence before any fix, then lands a failing test, a single fix, and verification output.
diagnostician
Specialized root cause debugger. Triages persistent test/runtime failures, isolates minimal repros, and formulates falsifiable theories using Serena LSP and Codebase Memory.
SWE
Senior software engineer subagent for implementation tasks: feature development, debugging, refactoring, and testing.
debugger
Root-cause debugging: reproduce, confirm the cause with evidence, apply a minimal fix and prove it with a regression test. Use for bugs, flaky tests or errors of unknown cause.