bug-fix-loop-coordinator

bug-fix-loop-coordinator is a skill for Claude Code, Codex from TashanGKD/tashan-cursor-skills. It costs 116 tokens per session (5,743 once invoked), scanned A, original, MIT.

A document-driven process for fixing software bugs in priority order and testing each fix. TDD, or test-driven development, means writing a failing test before changing the code.

In plain words
What is it for?
Use it after testing to coordinate P0, P1, and P2 fixes, run targeted regression tests, track repair rounds, and decide whether the project is ready to release.
Why use it?
It keeps bug status and repair evidence in shared documents instead of relying on conversation history. It also prevents stopping while serious bugs remain.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after testing to coordinate P0, P1, and P2 fixes, run targeted regression tests, track repair rounds, and decide whether the project is ready to release.

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Install with agentmods
npx agentmods add skills/tashangkd/tashan-cursor-skills/bug-fix-loop-coordinator
Install

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.

Any agent
npx skills add TashanGKD/tashan-cursor-skills --skill bug-fix-loop-coordinator
Clone the repo
git clone --depth 1 https://github.com/TashanGKD/tashan-cursor-skills

Made for: Claude Code, Codex.

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README.md
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Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,743 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00116 $0.05743
Opus 5 $0.00058 $0.02871
Sonnet 5 $0.00023 $0.01149
Haiku 4.5 $0.00012 $0.00574

Measured 9d ago against content hash 466e075483ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

bug-fix-loop-coordinator 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.

skills/bug-fix-loop-coordinator/SKILL.md · 446 lines

How it starts

The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Bug 修复循环协调者(bug-fix-loop-coordinator)

核心定位:测试→修复→回归的自动循环引擎。 信息传递媒介:全程通过文档(追踪台、修复记录、轮次追踪表)交换状态,不依赖对话历史。 终止条件:技术问题追踪台.md 中 P0=0 且 P1=0,全量回归通过。


知识导航表(执行前必读)

层级 文档 用途
D0 认知根 _内部总控/认知结构/L1_系统性文档/技术架构思维维度/知识库/测试后Bug修复到上线全流程_调研_20260324.md 修复流程最佳实践:先复现→TDD→最小改动→回归
D0-AI _内部总控/认知结构/L1_系统性文档/技术架构思维维度/知识库/AI_Agent平台生产架构_调研_20260325.md AI 平台架构全景(Layer 0-9,含代码示例和生产数据);修复排序规则见 _内部总控/开发规范/AI平台架构分层框架.md(薄包装层)
D1 项目追踪 项目群/[项目]/技术架构师/技术问题追踪台.md 信息源:所有 Bug 的状态(未解决/修复中/已修复/回归通过)
D2 轮次记录 项目群/[项目]/3_开发计划/test-round-tracker.md 循环进度追踪:第几轮,上一轮的状态快照
D3 测试规格 项目群/[项目]/测试工程师/test-spec-*.md(最新版) 回归测试的执行标准
D4 修复记录 项目群/[项目]/内部总控/修复记录.md 每次修复的 TDD 证据

核心设计原则

单一信息源:技术问题追踪台.md 是唯一的 Bug 状态来源
  → 所有角色只写入追踪台,不通过对话传递 Bug 信息

文档驱动循环:
  追踪台有未解决P0/P1 → 继续循环
  追踪台P0+P1清零 + 全量回归通过 → 退出循环

TDD 约束(通过 bug-fix-tdd-guard Rule 强制执行):
  修复顺序:先写失败测试 → 再改代码 → 再运行全量CI
  
最小改动原则:
  每个 Bug 独立 PR,不合并,不借机重构

激活后立即执行

Step 0  【确认项目路径】
        从对话上下文确认当前项目目录
        Read: 项目群/[项目]/技术架构师/技术问题追踪台.md
        → 若文件不存在:
          告知用户「技术问题追踪台不存在,请先运行测试工程师(关卡C)生成追踪台」
          停止执行
        → 存在则继续

Step 1  【轮次记录初始化】
        Read: 项目群/[项目]/3_开发计划/test-round-tracker.md
        → 若不存在:从模板创建(见附录A)
        → 记录当前轮次号 N = 已有轮次数 + 1
        更新 test-round-tracker.md,写入本轮开始状态:
          - 轮次 N
          - 开始时间
          - 本轮来自上一轮的遗留 Bug 数(读追踪台统计)

Step 1.5  【D0 产品认知根确认——在处理任何 Bug 之前】
        
        这一步确保修复循环始终在「产品意图」的框架下运行,
        而不是纯粹根据技术严重程度做决策。
        
        Read: 项目群/[项目]/产品经理/产品定义.md(或等价路径)
        
        提取并记录以下内容作为本轮循环的「产品上下文」:
        
        1. 核心价值承诺(产品对用户最重要的 3 条承诺)
        2. 各功能闭环(A/B/C/D/E/F)的用户价值
        3. 产品诚信强制项(标注了「强制设计规则」的条目)
        4. 当前 MVP 边界(哪些在范围内,哪些不在)
        
        将此上下文写入本轮循环的「产品视角评估基准」(内存中持有,dispatch 时传递给 fixer)
        
        IF 产品定义不存在:
          → 警告:「⚠️ 未找到产品定义文档。修复循环将只能做「不影响用户行为」的技术修复。
                    涉及用户可见行为的 Bug 需要产品定义支持,请先创建产品定义。」
          → 继续,但标记所有涉及用户行为的 Bug 为「需人工决策」

Step 1.8  【架构层分类(AI 平台专项,替代纯 P0/P1/P2 排序)】
        Read: _内部总控/开发规范/AI平台架构分层框架.md
        
        对追踪台中所有未解决 Bug 进行架构层标注:
          Layer 0/1(部署模型/状态管理):状态不持久化、build lock、任务可见性等
          Layer 2(并发模型):rate limit、并发死锁、资源竞争
          Layer 3(LLM 调用管理):截断、context 不完整、token 超限
          Layer 4(数据隔离):跨用户数据串扰
          Layer 5(Agent 框架):路由绕过、工作流入口混乱、工具调用错误
          Layer 6-9(可观测/安全/性能/扩展):监控缺失、注入风险等
          前端:UI/交互/显示问题
        
        修复排序原则(从 AI平台架构分层框架.md):
          1. 先修低层(Layer 0/1 优先于 Layer 5 优先于前端)
          2. 同层内:AI 确定先修,AI 不确定列出选项等用户决策
          3. 不用 P0/P1/P2 作为主排序轴(仅作同层内参考)
        
        构建架构层排序后的执行队列:
        arch_sorted_queue = [
          [Layer 1] BUG-XX(状态管理问题),
          [Layer 3] PRIN-01-XX(LLM截断)× N,
          [Layer 5] BUG-YY(Agent框架问题),
          [前端] BUG-ZZ(UI问题),
          ...
        ]
        
        输出摘要:
        「🏗️ 架构层分析完成:
         Layer 1(状态管理):N 个
         Layer 3(LLM调用):N 个
         Layer 5(Agent框架):N 个
         前端:N 个
         
         修复将从 Layer 1 开始,逐层推进。」

Step 2  【读取 Bug 清单 + 产品影响评估 + 构建层序队列】
        Read: 技术问题追踪台.md
        
        统计(辅助参考):
        - P0_bugs = [未解决的P0列表]
        - P1_bugs = [未解决的P1列表]
        - P2_bugs = [未解决的P2列表]
        
        【产品影响判断(标注,不改变层序主轴)】
        使用 Step 1.5 的「产品上下文」,对每个 Bug 判断产品重要性:
        □ 破坏产品核心价值承诺 → 标注「产品高优」
        □ 涉及产品诚信问题 → 标注「产品高优」
        □ 出现在认知飞轮关键节点 → 标注「产品高优」
        
        【构建层序修复队列(主轴:架构层,第二轴:AI确定/不确定)】
        参考 Step 1.8 的层分类结果,构建最终队列:
          [L1 层 Bug(AI确定)] → [L1 层 Bug(AI不确定,等决策)] →
          [L3 层 Bug(AI确定)] → ... → [FE Bug] → ...
        
        同层内「产品高优」标注的 Bug 排在其他 Bug 前面
        
        IF 所有 Bug 均已清零:
          → 跳至 Step 7(触发全量回归)
        
        输出摘要:
        「📊 第 N 轮修复循环(层序)
         架构层:L1=N | L3=N | L5=N | FE=N
         辅助:P0=N | P1=N | P2=N
         修复顺序:先低层(L1→FE),同层内 AI 确定优先」

Step 3  【按层序逐条修复】
        FOR each bug in 层序队列:
          Step 3.1  输出当前Bug信息:
                    「🔧 处理 Bug: [TC-XXX] [架构层] [Bug描述]
                     复现步骤:[从追踪台读取]
                     涉及模块:[从追踪台读取]
                     AI 确定?[是/否]」
          
          IF AI 不确定(多方案或涉及设计决策):
            → 列出选项,等待用户决策后再修复
          
          Step 3.2  确定负责角色:
                    - 后端Bug → role-后端开发
                    - 前端Bug → role-前端开发
                    - AI行为Bug → role-AI工程师
                    
          Step 3.3  Spawn fixer 子智能体执行修复(含产品上下文):
                    Read: 修复记录.md(了解是否有类似修复参考)
                    
                    从 Step 1.5 的产品上下文中,提取与此 Bug 相关的功能描述
                    
                    调用 /fixer 子智能体(隔离上下文,防止开发偏见),传入:
                    ---
                    模式:单Bug修复(模式A)
                    Bug ID:[TC-XXX]
                    Bug描述:[从追踪台读取的完整描述]
                    失败现象:[从追踪台读取]
                    复现步骤:[从追踪台读取]
                    期望结果:[从 test-spec 读取对应验收标准]
                    涉及模块:[从追踪台读取]
                    代码库路径:[当前项目路径]
                    追踪台路径:[技术问题追踪台.md 完整路径]
                    修复记录路径:[修复记录.md 完整路径]
                    
                    【产品上下文(来自 Step 1.5)—— fixer D0 使用】
                    产品定义路径:[产品定义.md 完整路径]
                    相关功能描述:[从产品定义中提取与此 Bug 相关的功能段落]
                    用户价值承诺:[该功能对用户的核心价值,1-2句话]
                    产品诚信约束:[如有强制设计规则,列出]
                    ---
                    
                    ⚠️ 注意:fixer 必须先完成 D0(产品认知根确认)再开始技术修复。
                    如果 fixer 报告「这是产品设计问题,不是实现问题」→ 写入产品问题追踪台,不做技术修复。
                    
                    ⚠️ 为什么用子智能体而不是开发角色:
                    子智能体从干净上下文启动,不知道「协调者知道什么」,
                    以纯粹的怀疑者视角分析 Bug,避免「既知道答案又验证答案」的偏见。
          
          Step 3.4  等待 fixer 子智能体返回报告,解析结果:
                    
                    IF fixer 返回 status=success:
                      → 确认 TDD 证据存在(失败测试 + 通过测试 + CI 通过)
                      → 确认追踪台已更新(fixer 会自动更新,但要验证)
                      → 输出:「✅ [TC-XXX] fixer 报告修复成功」
                      → 继续 Step 3.5(独立回归验证)
                    
                    IF fixer 返回 status=escalated(3次失败):
                      → 输出:「⚠️ [TC-XXX] fixer 三次修复失败,需人工介入」
                      → 更新追踪台状态为「需人工介入」
                      → 暂停当前 P0 处理,等待用户指令
                    
                    IF fixer 返回 status=cannot_reproduce:
                      → 输出:「⚠️ [TC-XXX] 无法复现,复现步骤需要更新」
                      → 向用户报告,请求提供更多复现信息
                    
                    输出:「▶️ 触发针对 [TC-XXX] 的独立回归验证...」
                    触发 role-测试工程师(回归模式),传入:
                      - 复现该Bug的测试用例
                      - 相关联的测试用例(影响范围内的)
                    
          Step 3.5  解析回归结果:
                    IF 回归通过:
                      更新 技术问题追踪台.md → 状态: 「已修复(轮次N,日期)」
                      追加到 修复记录.md(TDD证据:测试ID + 修复说明)
                      输出:「✅ [TC-XXX] 修复验证通过」
                    ELSE:
                      输出:「❌ [TC-XXX] 回归失败,重新分析...」
                      更新追踪台状态为「修复中(验证失败)」
                      回到 Step 3.2 重新分析根因(最多3次,超过则标记为「需人工介入」)
          
          Step 3.6  IF 本 Bug 标记「需人工介入」:
                    输出:「⚠️ [TC-XXX] 三次修复未通过回归,需要人工介入」
                    等待用户指令(暂停循环,不继续下一个P0)

Step 4  【P0 全清检查】
        Re-Read: 技术问题追踪台.md
        IF 仍有未解决P0:
          → 回到 Step 3(处理下一个P0)
        ELSE:
          → 输出「✅ P0 全清,开始处理 P1」
          → 继续 Step 5

Step 5  【P1 修复循环(类同 Step 3,严重程度 P1)】
        FOR each bug in P1_bugs(实时从追踪台读取最新列表):
          [与 Step 3.1~3.6 相同的流程,severity = P1]
          
        P1 处理完毕后:更新 test-round-tracker.md(本轮P1修复完成)

Step 6  【本轮 P2 处置(默认自动跳过,除非调用方明确指定处理)】
        Read: P2_bugs(当前未解决P2列表)
        
        Read: p2_handling 参数(从调用方输入读取,默认 = "skip")
        
        IF p2_handling == "skip"(默认):
          → 输出 P2 清单(仅列表,不等待):
            「📋 跳过 [P2_count] 个 P2(记入技术债,不阻止上线):
             [ID] [描述]
             ...
             ⏩ 自动进入全量回归验证」
          → 将所有 P2 在追踪台标记为「已知技术债(跳过上线)」
          → 直接进入 Step 7
        
        IF p2_handling == "fix_all":
          → 与 P1 相同流程逐个修复,完成后进入 Step 7
        
        IF p2_handling == "ask"(仅当调用方明确传入时):
          → 输出 P2 清单,暂停等待用户决策:
            「P2 不阻止上线。现在修还是跳过?[A/B/C]」
          → 等待用户回复

Step 7  【全量回归测试(上线前最终验证)】
        输出:「▶️ P0+P1 全清,触发全量回归测试...」
        触发 role-测试工程师(全量模式):
          - 执行完整 test-spec 中所有测试用例
          - 传入:test-spec 路径、当前版本号
          
Step 8  【解析全量回归结果】
        IF 全量回归通过(无新P0/P1):
          → 进入 Step 9(输出上线报告)
        ELSE 发现新 Bug(回归引入的):
          更新追踪台:将新 Bug 写入(标注「Round N 全量回归发现」)
          更新 test-round-tracker.md(本轮发现新Bug)
          IF 新 Bug 有 P0/P1:
            → 输出:「⚠️ 全量回归发现新P0/P1,进入第 N+1 轮循环」
            → 回到 Step 1(轮次N+1)
          ELSE(只有P2新Bug):
            → 更新追踪台
            → 回到 Step 6 让用户决策
            
Step 9  【收敛:输出可上线结论】
        Read: 技术问题追踪台.md(最终状态)
        Read: test-round-tracker.md(所有轮次记录)
        Read: 修复记录.md(所有修复的TDD证据)
        
        输出综合报告(写入 测试工程师/最终测试报告.md):
        
        「✅ 测试-修复循环完成
        
         执行概览:
           共 N 轮修复循环
           修复 Bug 总数:P0×A + P1×B + P2×C
           全量回归:通过(Round N)
        
         Bug 处置情况:
           ✅ 已修复:[列表]
           📋 技术债(P2跳过):[列表]
        
         可上线结论:✅ 所有阻塞问题已清除,可以进入 DevOps
         
         下一步:触发 role-DevOps 执行上线发布」
        
        触发 P0 Bug 的 Blameless Postmortem(若本次有P0):
        「📝 建议:有 [P0_count_total] 个 P0 Bug,建议在上线后24h内完成 Postmortem
          路径:内部总控/Postmortem-[日期].md
          目的:分析为什么这些P0能逃过测试,改进测试体系」

Read the full file on GitHub · 446 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 9d ago First seen · 446 lines · 116 tokens per session scan A 466e075483ad

Subscribe to this mod's changes

bug-fix-loop-coordinator is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 116 tokens to every session and 5,743 once invoked, about $0.0006 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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