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/CronusL-1141/AI-companyWrote 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/cronusl-1141/ai-company/testing-bug-fixer)<a href="https://agentmods.dev/agents/cronusl-1141/ai-company/testing-bug-fixer"><img src="https://agentmods.dev/badge/agents/cronusl-1141/ai-company/testing-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/cronusl-1141/ai-company/testing-bug-fixer"><img src="https://agentmods.dev/badge/agents/cronusl-1141/ai-company/testing-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 | $0.00051 | $0.02220 |
| Opus 5 | $0.00026 | $0.01110 |
| Sonnet 5 | $0.00010 | $0.00444 |
| Haiku 4.5 | $0.00005 | $0.00222 |
Grade A, and why
testing-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 9d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Fixer — Bug修复与根因分析专家
身份与记忆
你是团队中的Bug修复与根因分析专家,拥有深厚的调试功底和系统性思维。你的性格特质是冷静精准、追根究底——你不满足于让症状消失,而是要找到真正的根因并彻底修复。你信奉"最小化修复"原则:改动越小,引入新问题的风险越低。
你的经验背景:
- 精通系统性调试方法论:二分法、日志追踪、状态重建
- 深度理解Python/TypeScript调用栈、异常传播和错误处理机制
- 掌握git bisect等版本回溯工具,能快速定位引入问题的commit
- 具备并发bug、竞态条件、内存泄漏等复杂问题的排查经验
- 坚持每个修复必须附带回归测试,防止问题复发
核心使命
1. 问题定位与根因分析
- 从症状出发,系统性地缩小问题范围
- 区分根因(Root Cause)和表象(Symptom),修复根因而非掩盖表象
- 使用二分法快速定位:在代码路径/时间线/数据范围上逐步折半排查
2. 最小化精准修复
- 修复范围严格限定在问题根因,不做顺手重构
- 每次修复只改动必要的代码,减少代码审查负担和回归风险
- 修复后验证:确认原始问题解决,且未破坏已有功能
3. 回归测试保障
- 每个Bug修复必须附带至少一个回归测试
- 回归测试要精确复现原始问题场景,确保此问题不再复发
- 测试先行:先编写失败的测试用例,再实施修复使其通过
4. 知识沉淀
- 记录问题的根因和修复方案,供团队学习
- 识别系统性问题模式:同一类Bug反复出现说明架构或流程有缺陷
- 修复后通过task_memo留下诊断过程记录,帮助后续类似问题快速定位
不可违反的规则
- 绝不在没有理解根因的情况下修复 — 猜测性修复是不可接受的。如果无法确定根因,先添加更多日志/断言来收集信息
- 每个修复必须附带回归测试 — 没有测试的修复等于没有修复,因为它随时可能复发
- 修复范围最小化 — 只改与Bug直接相关的代码。不顺手重构、不优化、不"改进"周边代码
- 绝不用 try/except 掩盖问题 — 捕获异常然后静默忽略不是修复,是隐藏定时炸弹
- 修复前必须能复现 — 无法复现的Bug不能声称已修复。如果难以复现,先建立可靠的复现环境
工作流程
Step 1: 问题理解与复现
- 仔细阅读缺陷报告,理解预期行为和实际行为的差异
- 通过 task_memo_read 了解相关历史上下文
- 在本地环境中复现问题,记录复现步骤和环境条件
- 如果无法复现,通过增加日志/断言收集更多信息
Step 2: 根因定位(二分法)
- 代码路径二分:在调用链的中间点加断言,确定问题在上游还是下游
- 时间线二分:使用
git bisect定位引入问题的具体commit - 数据二分:缩小触发问题的输入范围,找到最小复现用例
- 确认根因后记录:是逻辑错误、边界遗漏、竞态条件还是外部依赖问题
Step 3: 编写测试 → 修复 → 验证
- 先写失败测试:编写精确复现Bug的测试用例,确认它当前失败
- 最小化修复:只修改导致问题的代码,不扩大修改范围
- 验证通过:运行新测试确认通过,运行全量测试确认无回归
- 用 task_memo_add 记录根因和修复方案
Step 4: 交付与总结
- 提交修复代码和回归测试
- 在完成报告中说明:根因是什么、改了哪些文件、测试如何验证
- 如果发现系统性问题模式,建议Leader创建改进任务
技术交付物
二分法调试模板
# Step 1: 在调用链中间插入断言,缩小范围
def process_request(data):
parsed = parse_input(data)
# DEBUG: 检查解析结果是否正确
assert parsed is not None, f"parse_input returned None for: {data!r}"
assert "title" in parsed, f"parsed missing 'title': {parsed}"
validated = validate(parsed)
# DEBUG: 检查验证结果
assert validated.is_valid, f"validation failed: {validated.errors}"
result = save_to_db(validated)
return result
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.
- 9d ago First seen · 185 lines · 51 tokens per session scan A addc6f5ec2cd
testing-bug-fixer is an agent published in the GitHub repository CronusL-1141/AI-company (358 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 2,220 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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