Borrowing it
Nothing to install: this file belongs to MisonL/Ling. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MisonL/Ling/main/.agents/skills/systematic-debugging/SKILL.mdgit clone --depth 1 https://github.com/MisonL/LingWrote 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/skills/misonl/ling/systematic-debugging)<a href="https://agentmods.dev/skills/misonl/ling/systematic-debugging"><img src="https://agentmods.dev/badge/skills/misonl/ling/systematic-debugging.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00730 |
| Opus 5 | $0.00016 | $0.00365 |
| Sonnet 5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
systematic-debugging 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 4d 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.
What it actually says
系统化调试
来源：obra/superpowers
概览
本技能提供结构化的调试方法，避免随机猜测，确保在修复前已充分理解问题。
4 阶段调试流程
阶段 1：复现
修复前，必须稳定复现问题。
## 复现步骤
1. [精确复现步骤]
2. [下一步]
3. [预期结果 vs 实际结果]
## 复现率
- [ ] 必现 (100%)
- [ ] 经常 (50-90%)
- [ ] 偶发 (10-50%)
- [ ] 罕见 (<10%)
阶段 2：隔离
缩小问题来源范围。
## 隔离问题提问
- 问题从什么时候开始出现？
- 最近改了什么？
- 所有环境都会出现吗？
- 能否用最小代码复现？
- 触发问题的最小改动是什么？
阶段 3：理解
找到根因，而不只停留在症状层。
## 根因分析
### 5 个为什么
1. 为什么：[第一层观测]
2. 为什么：[更深层原因]
3. 为什么：[继续深入]
4. 为什么：[接近根因]
5. 为什么：[锁定根因]
阶段 4：修复与验证
修复后，验证问题已真正解决。
## 修复验证
- [ ] Bug 不再复现
- [ ] 关联功能仍然正常
- [ ] 未引入新问题
- [ ] 已添加防回归测试
调试检查清单
## 开始前
- [ ] 能稳定复现
- [ ] 有最小复现用例
- [ ] 明确预期行为
## 调查中
- [ ] 检查最近改动 (git log)
- [ ] 检查日志报错
- [ ] 必要时增加日志
- [ ] 使用调试器/断点
## 修复后
- [ ] 已记录根因
- [ ] 已验证修复
- [ ] 已添加回归测试
- [ ] 已排查相似代码
常用调试命令
# Recent changes
git log --oneline -20
git diff HEAD~5
# Search for pattern
grep -r "errorPattern" --include="*.ts"
# Check logs
pm2 logs app-name --err --lines 100
反模式
[FAIL] 随机改动 - “也许改这里就好了” [FAIL] 忽视证据 - “这不可能是原因” [FAIL] 主观断言 - 没有证据就说“肯定是 X” [FAIL] 未先复现 - 盲修 [FAIL] 止步症状 - 没有挖到根因
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.
- 4d ago First seen · 115 lines · 33 tokens per session scan A c7df37cb6ff3
systematic-debugging is a skill published in the GitHub repository MisonL/Ling (8 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 730 once invoked, about $0.0002 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 2026-09-03.
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