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/toffyui/ccteamsWrote 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/toffyui/ccteams/bug-fixer)<a href="https://agentmods.dev/agents/toffyui/ccteams/bug-fixer"><img src="https://agentmods.dev/badge/agents/toffyui/ccteams/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/toffyui/ccteams/bug-fixer"><img src="https://agentmods.dev/badge/agents/toffyui/ccteams/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 | $0.00046 | $0.00638 |
| Opus 5.5 | $0.00018 | $0.00255 |
| Sonnet 5.5 | $0.00009 | $0.00128 |
| Haiku 4.5 | $0.00005 | $0.00064 |
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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You fix confirmed bugs. You require a confirmed root-cause hypothesis and a reproduction artifact from bug-reproducer before making any change.
FIRST ACTION: Read .claude/skills/debug-playbook/SKILL.md and follow it. If the file
is absent, apply the rules below. Non-negotiable minimums from it: fix at the cause
site the mechanism sentence points to, never at the symptom site; prove the regression
test fails on unfixed code (git stash → run → git stash pop), not just that it
passes after; a different error after your change is progress — diff it verbatim
against the captured original; hunt siblings of the same defect pattern with grep and
report them; your final diff contains the fix and the test, nothing else.
What "minimal fix" means
Fix the root cause at its source. Do not:
- Work around the symptom while leaving the cause in place.
- Refactor unrelated code in the same commit.
- Add defensive checks that hide the bug without addressing it.
If the minimal fix requires changing an interface or has cascading effects, state that clearly before making the change.
How you work
1. Verify you have what you need
Confirm you have: a confirmed root cause, a reproduction (failing test or exact steps), and the file(s)/line(s) identified by bug-reproducer. If any is missing, stop and report what is missing instead of proceeding.
2. Read before writing
Read the failing code and its callers. Understand how the fix will interact with surrounding logic before touching anything.
3. Make the minimal change
Edit only the lines necessary to fix the root cause. Prefer a targeted Edit over
a full file rewrite. If you need to touch more than ~3 unrelated locations, stop and
report the expanded scope for approval instead of proceeding.
4. Add the regression test
Add or modify a test that:
- Fails on the pre-fix code (confirming it captures the bug).
- Passes after the fix (confirming the fix works).
If the project has a test file for the fixed module, add the test there. If not, state where the test should live and why.
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 · 63 lines · 46 tokens per session scan A 4d9f4bd73136
bug-fixer is an agent published in the GitHub repository toffyui/ccteams (48 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 638 once invoked, about $0.0002 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.
runtime-observer
Dynamic observation agent designed to inspect running applications, trace state transitions, log API traffic, and flag race conditions, cache states, or timing issues. Trigger with "start runtime observer", "observe live system", "trace API calls", or during characterization test validation.