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systematic-debugging
用于遇到缺陷、测试失败或异常行为，且必须在提出修复方案前调查根因的场景。未经根因分析不得只修复表象。
Overview
用于遇到缺陷、测试失败或异常行为，且必须在提出修复方案前调查根因的场景。未经根因分析不得只修复表象。
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Systematic Debugging - 系统化调试
铁律
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
违反规则的信件就是违反规则的精神。
无例外：
- 不跳过根因调查直接修复
- 不基于猜测进行修复
- 不尝试多个更改同时测试
- 不在不完全理解的情况下修复
常见合理化
| 借口 | 现实 |
|---|---|
| "快速修复现在，稍后调查" | 永不会有"稍后"。技术债积累 |
| "尝试改变 X，看是否有效" | 猜测不是调试。可能碰巧修复但未理解 |
| "添加多个更改，运行测试" | 无法确定哪个更改有效。浪费调试时间 |
| "可能大概差不多是 X" | 模糊理解导致错误修复 |
| "不完全理解但可能有效" | 理解不完全=修复不完整 |
红色标志 - 停止并重新开始
- "快速修复现在，稍后调查"
- "尝试改变 X，看是否有效"
- "添加多个更改，运行测试"
- "可能大概差不多是 X，让我修复"
- "不完全理解但可能有效"
- 基于猜测而非证据的修复
所有这些意味着：停止修复。回到根因调查阶段。
四阶段框架
阶段 1：根因调查
任何修复前：
-
仔细阅读错误消息
- 不要跳过错误或警告
- 它们通常包含确切解决方案
-
一致复现
- 你能可靠触发吗？
-
检查最近变更
- 什么变更可能导致此问题？
-
多组件系统收集证据
# 对于每个组件边界： - 记录进入组件的数据 - 记录退出组件的数据 - 验证环境/配置传播 - 检查每层状态 运行一次以收集显示何处中断的证据 然后分析证据以识别失败组件 然后调查该特定组件 -
追踪数据流
- 向后追踪到坏值起源
阶段 2：模式分析
- 找到工作示例
- 与参考对比
- 识别差异
- 理解依赖
阶段 3：假设与测试
- 形成单一假设："我认为 X 是根本原因，因为 Y"
- 最小化测试：进行最小可能更改
- 验证后再继续
- 3+ 次修复失败时：质疑架构
阶段 4：实现
- 创建失败测试用例
- 实施单一修复：只改验证该假设所需的最小范围
- 验证修复：一次只验证一个假设，不把多个修复混在同一次验证里
- 收敛范围：避免顺手重构、无关格式化或新增未证明必要的抽象
- 如果 3+ 次修复失败：质疑架构
核心理念
系统化调试 不是碰运气，而是方法化地解决问题的科学流程。
┌─────────────────────────────────────────────────────────┐
│ 收集证据 → 形成假设 → 系统验证 → 定位根因 → 实施修复 │
└─────────────────────────────────────────────────────────┘
核心原则：
- 基于证据，而非猜测
- 治疗根本原因，而非症状
- 一次验证一个假设
- 记录整个过程
何时使用本技能
在以下场景时激活：
- 出现错误、异常、Bug
- 需要分析堆栈追踪（Stack Trace）
- 提到"调试"、"排查问题"、"定位根因"
- 生产环境问题分析
- 跨文件或跨模块的问题追踪
- 间歇性 Bug（时有时无）
调试工作流程
步骤 1：收集证据 📋
信息收集清单：
| 证据类型 | 收集方法 | 重要性 |
|---|---|---|
| 完整堆栈追踪 | 复制完整错误信息 | ⭐⭐⭐⭐⭐ |
| 相关日志 | 查看应用日志 | ⭐⭐⭐⭐⭐ |
| 复现步骤 | 记录如何触发问题 | ⭐⭐⭐⭐ |
| 环境信息 | OS、版本、依赖 | ⭐⭐⭐ |
| 代码变更 | 最近的 Git 提交 | ⭐⭐⭐⭐ |
| 用户输入 | 触发问题的数据 | ⭐⭐⭐ |
堆栈追踪分析示例：
# 示例错误
Traceback (most recent call last):
File "app.py", line 45, in process_order
result = payment_service.charge(amount)
File "payment.py", line 78, in charge
return api_client.post("/charge", data)
File "api.py", line 23, in post
response = self.session.request(method, url, **kwargs)
ssl.SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed
分析要点：
- 从下往上读：最下面是根本原因
- 追踪调用链：app.py → payment.py → api.py
- 识别错误类型：SSL 证书验证失败
- 定位失败点：api.py 第 23 行
步骤 2：形成假设 💡
假设生成规则：
- 基于证据：不要凭空猜测
- 优先考虑最近变更
- 从简单到复杂排序
- 一次只关注一个假设
假设模板：
假设：[问题描述] 的原因是 [根本原因]
触发条件：[什么情况下会出现]
验证方法：[如何证明或否定]
假设示例：
假设：SSL 错误的原因是生产环境缺少证书文件
触发条件：部署到生产环境后首次调用 API
验证方法：检查 /etc/ssl/certs/ 目录是否存在证书
常见根因分类：
| 类别 | 典型原因 | 检查方法 |
|---|---|---|
| 配置问题 | 环境变量、配置文件错误 | 检查 .env、config |
| 依赖问题 | 版本冲突、缺失依赖 | 检查 requirements.txt |
| 数据问题 | 空值、格式错误、边界值 | 检查输入数据 |
| 并发问题 | 竞态条件、死锁 | 检查锁、线程安全 |
| 资源问题 | 内存泄漏、连接池耗尽 | 检查资源使用 |
| 逻辑错误 | 边界条件、空值处理 | 代码审查 |
步骤 3：系统验证 🔍
验证原则：
- 一次验证一个假设（控制变量）
- 设计最小化验证实验
- 记录验证过程和结果
- 用排除法缩小范围
验证方法：
# 方法 1：添加日志
def process_order(order_id):
logger.info(f"Processing order: {order_id}")
order = get_order(order_id)
logger.debug(f"Order data: {order}")
# ...
# 方法 2：断点调试
import pdb; pdb.set_trace()
# 方法 3：简化输入
def test():
# 用最小输入验证
process_order({"id": 1, "amount": 0})
验证记录模板：
## 假设验证记录
### 假设 1：证书文件缺失
- **验证方法**：检查文件是否存在
- **验证结果**：❌ 文件存在
- **结论**：假设不成立，排除
### 假设 2：证书过期
- **验证方法**：检查证书有效期
- **验证结果**：✅ 证书已于 3 天前过期
- **结论**：假设成立，定位根因
步骤 4：定位根因 🎯
区分症状与根因：
使用五问法（5 Whys）深入挖掘：
问题：订单处理失败
Why 1: 为什么失败？
→ API 调用返回 500 错误
Why 2: 为什么 API 返回 500？
→ 数据库查询超时
Why 3: 为什么查询超时？
→ 缺少索引导致全表扫描
Why 4: 为什么缺少索引？
→ 新增字段时未同步添加索引
Why 5: 为什么未添加索引？
→ 没有 Code Review 流程
根因：缺少 Code Review 机制（而非"API 返回 500"）
根因特征：
- ✅ 可操作：可以采取措施
- ✅ 可预防：可以防止再次发生
- ✅ 系统级：不是个人失误
步骤 5：实施修复 🔧
修复原则：
- 最小化修复范围
- 添加回归测试
- 修复后验证
- 更新文档
修复清单：
## 修复计划
### 代码修复
- [ ] 修改代码：[具体位置]
- [ ] 添加测试：[测试用例]
- [ ] 运行测试确认通过
### 预防措施
- [ ] 添加检查机制
- [ ] 更新文档
- [ ] 团队分享经验
### 验证
- [ ] 本地验证
- [ ] 测试环境验证
- [ ] 生产环境验证
调试技巧库
技巧 1：二分法调试
当问题可能出现在多个位置时：
# 在中间点添加日志
def complex_function(data):
logger.info("Step 1: Start")
result1 = step1(data)
logger.info(f"Step 1 result: {result1}")
logger.info("Step 2: Start")
result2 = step2(result1)
logger.info(f"Step 2 result: {result2}")
logger.info("Step 3: Start")
result3 = step3(result2)
logger.info(f"Step 3 result: {result3}")
return result3
技巧 2：最小化复现
# 从复杂场景中提取最小复现用例
def test_minimal_reproduction():
# 不需要完整的订单数据
order = {"amount": 100} # 最小输入
result = process_payment(order)
assert result.success
技巧 3：对比法调试
# 对比工作版本和失败版本
def test_working_vs_broken():
# 工作版本
result_working = old_api_call(data)
# 失败版本
try:
result_broken = new_api_call(data)
except Exception as e:
print(f"Broken: {e}")
# 对比差异
compare_results(result_working, result_broken)
技巧 4：环境隔离
# 使用 Docker 隔离环境
docker run -it python:3.11 bash
# 或使用虚拟环境
python -m venv debug_env
source debug_env/bin/activate
常见问题模式
模式 1：时序问题
症状：间歇性失败，时好时坏
根因：竞态条件、异步操作未正确等待
调试方法：
# 添加重试和延迟
from tenacity import retry, stop_after_attempt, wait_fixed
@retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
def flaky_operation():
# 可能失败的操作
pass
模式 2：内存问题
症状：性能逐渐下降，最终崩溃
根因：内存泄漏
调试方法：
import tracemalloc
tracemalloc.start()
# ... 运行代码 ...
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
for stat in top_stats[:10]:
print(stat)
模式 3：状态污染
症状：测试单独通过，批量运行失败
根因：测试间共享状态
调试方法：
@pytest.fixture(autouse=True)
def reset_state():
# 每个测试前重置状态
reset_database()
reset_cache()
yield
cleanup()
验证清单
修复完成后，检查：
- 根本原因已识别（非症状）
- 修复方案最小化
- 添加回归测试
- 本地验证通过
- 文档已更新（如需要）
- 团队已分享经验
- 预防措施已实施
相关参考
约束
公共硬约束
本块由 docs/templates/skill-common-constraints.md 统一维护；每个 SKILL.md 的 ## 约束 必须逐字同步本块，不得在副本中改写公共规则。
- 任务需要落盘时，使用唯一的
./.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/根目录；共享材料放入shared/，Skill 专属材料放入该 Skill 的input/、output/、log/。 - 正式交付物、源代码和正式计划按项目约定保存，不写入任务工作区；未经授权不覆盖、删除、迁移或远程写入。
- 项目维护变更检查 BAC 可用性并记录需求、AI 产出、工具结果、文件改动和验证摘要；BAC 只做过程审计，不替代署名、责任或合规判断。
- 不记录 API Key、访问令牌、密码、Cookie、环境/凭据文件、私有 Prompt、身份信息、本地用户名、主机名或不必要的大体积原始数据。
- 文件路径必须规范化并限制在授权项目范围内；外部 URL、子进程和网络访问遵循最小权限，防止路径遍历、SSRF 和命令注入。
- Skill 版本唯一记录在自身
config.yaml:skill_info.version；公开 API、协议、目录或配置变更同步文档与CHANGELOG.md。 bensz-collect-bugs是一个 Agent Skill；仅将 Bensz Agent Skill 或 Bensz 基础设施本身的设计缺陷交给它。先脱敏写入~/.bensz-skills/bugs/，当前任务不中断，只有用户明确要求才公开上报，禁止直接修改用户已安装的 Skill 源码。
File metadata
name: systematic-debugging
description: 用于遇到缺陷、测试失败或异常行为，且必须在提出修复方案前调查根因的场景。未经根因分析不得只修复表象。
metadata:
short-description: 系统化调试与根因分析
keywords:
- systematic-debugging
- 调试
- Bug 修复
- 根因分析
- 问题诊断
- 堆栈追踪
- 生产环境调试
- root cause
- debugging
category: 调试
author: Bensz Conan
platform: Claude Code | OpenAI Codex | ChatGPT
iron-law: |
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRSTView original text
---
name: systematic-debugging
description: 用于遇到缺陷、测试失败或异常行为，且必须在提出修复方案前调查根因的场景。未经根因分析不得只修复表象。
metadata:
short-description: 系统化调试与根因分析
keywords:
- systematic-debugging
- 调试
- Bug 修复
- 根因分析
- 问题诊断
- 堆栈追踪
- 生产环境调试
- root cause
- debugging
category: 调试
author: Bensz Conan
platform: Claude Code | OpenAI Codex | ChatGPT
iron-law: |
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
---
# Systematic Debugging - 系统化调试
## 铁律
```
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
```
**违反规则的信件就是违反规则的精神。**
**无例外**：
- 不跳过根因调查直接修复
- 不基于猜测进行修复
- 不尝试多个更改同时测试
- 不在不完全理解的情况下修复
---
## 常见合理化
| 借口 | 现实 |
|------|------|
| "快速修复现在，稍后调查" | 永不会有"稍后"。技术债积累 |
| "尝试改变 X，看是否有效" | 猜测不是调试。可能碰巧修复但未理解 |
| "添加多个更改，运行测试" | 无法确定哪个更改有效。浪费调试时间 |
| "可能大概差不多是 X" | 模糊理解导致错误修复 |
| "不完全理解但可能有效" | 理解不完全=修复不完整 |
---
## 红色标志 - 停止并重新开始
- "快速修复现在，稍后调查"
- "尝试改变 X，看是否有效"
- "添加多个更改，运行测试"
- "可能大概差不多是 X，让我修复"
- "不完全理解但可能有效"
- 基于猜测而非证据的修复
**所有这些意味着：停止修复。回到根因调查阶段。**
---
## 四阶段框架
### 阶段 1：根因调查
**任何修复前**：
1. **仔细阅读错误消息**
- 不要跳过错误或警告
- 它们通常包含确切解决方案
2. **一致复现**
- 你能可靠触发吗？
3. **检查最近变更**
- 什么变更可能导致此问题？
4. **多组件系统收集证据**
```bash
# 对于每个组件边界：
- 记录进入组件的数据
- 记录退出组件的数据
- 验证环境/配置传播
- 检查每层状态
运行一次以收集显示何处中断的证据
然后分析证据以识别失败组件
然后调查该特定组件
```
5. **追踪数据流**
- 向后追踪到坏值起源
### 阶段 2：模式分析
1. **找到工作示例**
2. **与参考对比**
3. **识别差异**
4. **理解依赖**
### 阶段 3：假设与测试
1. **形成单一假设**："我认为 X 是根本原因，因为 Y"
2. **最小化测试**：进行最小可能更改
3. **验证后再继续**
4. **3+ 次修复失败时：质疑架构**
### 阶段 4：实现
1. **创建失败测试用例**
2. **实施单一修复**：只改验证该假设所需的最小范围
3. **验证修复**：一次只验证一个假设，不把多个修复混在同一次验证里
4. **收敛范围**：避免顺手重构、无关格式化或新增未证明必要的抽象
5. **如果 3+ 次修复失败：质疑架构**
---
## 核心理念
**系统化调试** 不是碰运气，而是方法化地解决问题的科学流程。
```
┌─────────────────────────────────────────────────────────┐
│ 收集证据 → 形成假设 → 系统验证 → 定位根因 → 实施修复 │
└─────────────────────────────────────────────────────────┘
```
**核心原则**：
- **基于证据，而非猜测**
- **治疗根本原因，而非症状**
- **一次验证一个假设**
- **记录整个过程**
---
## 何时使用本技能
在以下场景时激活：
- 出现错误、异常、Bug
- 需要分析堆栈追踪（Stack Trace）
- 提到"调试"、"排查问题"、"定位根因"
- 生产环境问题分析
- 跨文件或跨模块的问题追踪
- 间歇性 Bug（时有时无）
---
## 调试工作流程
### 步骤 1：收集证据 📋
**信息收集清单**：
| 证据类型 | 收集方法 | 重要性 |
|---------|---------|--------|
| **完整堆栈追踪** | 复制完整错误信息 | ⭐⭐⭐⭐⭐ |
| **相关日志** | 查看应用日志 | ⭐⭐⭐⭐⭐ |
| **复现步骤** | 记录如何触发问题 | ⭐⭐⭐⭐ |
| **环境信息** | OS、版本、依赖 | ⭐⭐⭐ |
| **代码变更** | 最近的 Git 提交 | ⭐⭐⭐⭐ |
| **用户输入** | 触发问题的数据 | ⭐⭐⭐ |
**堆栈追踪分析示例**：
```python
# 示例错误
Traceback (most recent call last):
File "app.py", line 45, in process_order
result = payment_service.charge(amount)
File "payment.py", line 78, in charge
return api_client.post("/charge", data)
File "api.py", line 23, in post
response = self.session.request(method, url, **kwargs)
ssl.SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed
```
**分析要点**：
1. **从下往上读**：最下面是根本原因
2. **追踪调用链**：app.py → payment.py → api.py
3. **识别错误类型**：SSL 证书验证失败
4. **定位失败点**：api.py 第 23 行
### 步骤 2：形成假设 💡
**假设生成规则**：
1. **基于证据**：不要凭空猜测
2. **优先考虑最近变更**
3. **从简单到复杂排序**
4. **一次只关注一个假设**
**假设模板**：
```
假设：[问题描述] 的原因是 [根本原因]
触发条件：[什么情况下会出现]
验证方法：[如何证明或否定]
```
**假设示例**：
```
假设：SSL 错误的原因是生产环境缺少证书文件
触发条件：部署到生产环境后首次调用 API
验证方法：检查 /etc/ssl/certs/ 目录是否存在证书
```
**常见根因分类**：
| 类别 | 典型原因 | 检查方法 |
|------|---------|---------|
| **配置问题** | 环境变量、配置文件错误 | 检查 .env、config |
| **依赖问题** | 版本冲突、缺失依赖 | 检查 requirements.txt |
| **数据问题** | 空值、格式错误、边界值 | 检查输入数据 |
| **并发问题** | 竞态条件、死锁 | 检查锁、线程安全 |
| **资源问题** | 内存泄漏、连接池耗尽 | 检查资源使用 |
| **逻辑错误** | 边界条件、空值处理 | 代码审查 |
### 步骤 3：系统验证 🔍
**验证原则**：
1. **一次验证一个假设**（控制变量）
2. **设计最小化验证实验**
3. **记录验证过程和结果**
4. **用排除法缩小范围**
**验证方法**：
```python
# 方法 1：添加日志
def process_order(order_id):
logger.info(f"Processing order: {order_id}")
order = get_order(order_id)
logger.debug(f"Order data: {order}")
# ...
# 方法 2：断点调试
import pdb; pdb.set_trace()
# 方法 3：简化输入
def test():
# 用最小输入验证
process_order({"id": 1, "amount": 0})
```
**验证记录模板**：
```markdown
## 假设验证记录
### 假设 1：证书文件缺失
- **验证方法**：检查文件是否存在
- **验证结果**：❌ 文件存在
- **结论**：假设不成立，排除
### 假设 2：证书过期
- **验证方法**：检查证书有效期
- **验证结果**：✅ 证书已于 3 天前过期
- **结论**：假设成立，定位根因
```
### 步骤 4：定位根因 🎯
**区分症状与根因**：
使用**五问法**（5 Whys）深入挖掘：
```
问题：订单处理失败
Why 1: 为什么失败？
→ API 调用返回 500 错误
Why 2: 为什么 API 返回 500？
→ 数据库查询超时
Why 3: 为什么查询超时？
→ 缺少索引导致全表扫描
Why 4: 为什么缺少索引？
→ 新增字段时未同步添加索引
Why 5: 为什么未添加索引？
→ 没有 Code Review 流程
根因：缺少 Code Review 机制（而非"API 返回 500"）
```
**根因特征**：
- ✅ **可操作**：可以采取措施
- ✅ **可预防**：可以防止再次发生
- ✅ **系统级**：不是个人失误
### 步骤 5：实施修复 🔧
**修复原则**：
1. **最小化修复范围**
2. **添加回归测试**
3. **修复后验证**
4. **更新文档**
**修复清单**：
```markdown
## 修复计划
### 代码修复
- [ ] 修改代码：[具体位置]
- [ ] 添加测试：[测试用例]
- [ ] 运行测试确认通过
### 预防措施
- [ ] 添加检查机制
- [ ] 更新文档
- [ ] 团队分享经验
### 验证
- [ ] 本地验证
- [ ] 测试环境验证
- [ ] 生产环境验证
```
---
## 调试技巧库
### 技巧 1：二分法调试
当问题可能出现在多个位置时：
```python
# 在中间点添加日志
def complex_function(data):
logger.info("Step 1: Start")
result1 = step1(data)
logger.info(f"Step 1 result: {result1}")
logger.info("Step 2: Start")
result2 = step2(result1)
logger.info(f"Step 2 result: {result2}")
logger.info("Step 3: Start")
result3 = step3(result2)
logger.info(f"Step 3 result: {result3}")
return result3
```
### 技巧 2：最小化复现
```python
# 从复杂场景中提取最小复现用例
def test_minimal_reproduction():
# 不需要完整的订单数据
order = {"amount": 100} # 最小输入
result = process_payment(order)
assert result.success
```
### 技巧 3：对比法调试
```python
# 对比工作版本和失败版本
def test_working_vs_broken():
# 工作版本
result_working = old_api_call(data)
# 失败版本
try:
result_broken = new_api_call(data)
except Exception as e:
print(f"Broken: {e}")
# 对比差异
compare_results(result_working, result_broken)
```
### 技巧 4：环境隔离
```bash
# 使用 Docker 隔离环境
docker run -it python:3.11 bash
# 或使用虚拟环境
python -m venv debug_env
source debug_env/bin/activate
```
---
## 常见问题模式
### 模式 1：时序问题
**症状**：间歇性失败，时好时坏
**根因**：竞态条件、异步操作未正确等待
**调试方法**：
```python
# 添加重试和延迟
from tenacity import retry, stop_after_attempt, wait_fixed
@retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
def flaky_operation():
# 可能失败的操作
pass
```
### 模式 2：内存问题
**症状**：性能逐渐下降，最终崩溃
**根因**：内存泄漏
**调试方法**：
```python
import tracemalloc
tracemalloc.start()
# ... 运行代码 ...
snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')
for stat in top_stats[:10]:
print(stat)
```
### 模式 3：状态污染
**症状**：测试单独通过，批量运行失败
**根因**：测试间共享状态
**调试方法**：
```python
@pytest.fixture(autouse=True)
def reset_state():
# 每个测试前重置状态
reset_database()
reset_cache()
yield
cleanup()
```
---
## 验证清单
修复完成后，检查：
- [ ] 根本原因已识别（非症状）
- [ ] 修复方案最小化
- [ ] 添加回归测试
- [ ] 本地验证通过
- [ ] 文档已更新（如需要）
- [ ] 团队已分享经验
- [ ] 预防措施已实施
---
## 相关参考
- [系统化调试指南](../references/debugging-systematic.md)
- [根因追踪](root-cause-tracing.md)
## 约束
<!-- BEGIN COMMON CONSTRAINTS -->
<!-- Source-Hash: sha256:15120201e9e0c7569517261d57ecefb63ac279c26ed13876f8e95b6dc35854d3 -->
<!-- Template-ID: skill-common-constraints; Template-Version: 1; Sync-Policy: exact-block -->
### 公共硬约束
本块由 `docs/templates/skill-common-constraints.md` 统一维护；每个 `SKILL.md` 的 `## 约束` 必须逐字同步本块，不得在副本中改写公共规则。
- 任务需要落盘时，使用唯一的 `./.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/` 根目录；共享材料放入 `shared/`，Skill 专属材料放入该 Skill 的 `input/`、`output/`、`log/`。
- 正式交付物、源代码和正式计划按项目约定保存，不写入任务工作区；未经授权不覆盖、删除、迁移或远程写入。
- 项目维护变更检查 BAC 可用性并记录需求、AI 产出、工具结果、文件改动和验证摘要；BAC 只做过程审计，不替代署名、责任或合规判断。
- 不记录 API Key、访问令牌、密码、Cookie、环境/凭据文件、私有 Prompt、身份信息、本地用户名、主机名或不必要的大体积原始数据。
- 文件路径必须规范化并限制在授权项目范围内；外部 URL、子进程和网络访问遵循最小权限，防止路径遍历、SSRF 和命令注入。
- Skill 版本唯一记录在自身 `config.yaml:skill_info.version`；公开 API、协议、目录或配置变更同步文档与 `CHANGELOG.md`。
- `bensz-collect-bugs` 是一个 Agent Skill；仅将 Bensz Agent Skill 或 Bensz 基础设施本身的设计缺陷交给它。先脱敏写入 `~/.bensz-skills/bugs/`，当前任务不中断，只有用户明确要求才公开上报，禁止直接修改用户已安装的 Skill 源码。
<!-- End of canonical common constraints. -->
<!-- END COMMON CONSTRAINTS -->
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Source needs review
The tracked source changed or could not be synchronized. Review the current source before installing.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 48 GitHub stars
- Stars/forks activity: 48 stars, 7 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- huangwb8/skills
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Sep 8, 2026
- Registry updated
- Sep 9, 2026
Version reported in registry metadata; check source releases before relying on it.
Quality
58/100
Promising
Trust
58/100
Do not auto-install
Audit
72/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 48 GitHub stars
- Stars/forks activity: 48 stars, 7 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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},
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"eval": "https://www.openagentskill.com/api/agent/evals?slug=huangwb8-systematic-debugging&task=Use%20systematic-debugging%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20systematic-debugging%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20systematic-debugging%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/huangwb8-systematic-debugging/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/huangwb8-systematic-debugging"
}
}For the creator
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- huangwb8
- Source
- huangwb8/skills
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