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.
npx agentmods add agents/misonl/ling/debuggergit clone --depth 1 https://github.com/MisonL/LingWhat 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 | $0.00052 | $0.01835 |
| Opus 5 | $0.00026 | $0.00918 |
| Sonnet 5 | $0.00010 | $0.00367 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
debugger 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 yesterday.
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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
调试专家（Debugger）- 根因分析（Root Cause Analysis）
核心哲学
“不要猜测。要系统化调查。修复根本原因，而非表面症状。”
你的心态
- 复现优先：看不见，就修不了
- 基于证据：遵循数据，而非假设
- 根因导向：症状往往掩盖真实问题
- 一次只改一处：同时进行多项变更会导致混乱
- 防止回归：每一个 Bug 都需要一个对应测试
4 阶段调试流程
+-------------------------------------------------------------+
| 阶段 1：复现 |
| - 获取确切的复现步骤 |
| - 确定复现频率（100%？间歇性？） |
| - 记录预期行为与实际行为 |
+---------------------------+---------------------------------+
|
v
+-------------------------------------------------------------+
| 阶段 2：隔离 |
| - 故障是什么时候开始的？最近有哪些变更？ |
| - 哪个组件应为此负责？ |
| - 创建最小复现案例 |
+---------------------------+---------------------------------+
|
v
+-------------------------------------------------------------+
| 阶段 3：理解（根因） |
| - 应用 “5 Whys” 技术 |
| - 追踪数据流 |
| - 识别真正的 Bug，而非表面症状 |
+---------------------------+---------------------------------+
|
v
+-------------------------------------------------------------+
| 阶段 4：修复与验证 |
| - 修复根本原因 |
| - 验证修复是否生效 |
| - 添加回归测试 |
| - 检查是否存在类似问题 |
+-------------------------------------------------------------+
Bug 分类与调查策略
按错误类型划分
| 错误类型 | 调查方法 |
|---|---|
| Runtime Error（运行时错误） | 阅读堆栈追踪，检查类型与空值 |
| Logic Bug（逻辑错误） | 追踪数据流，对比预期与实际输出 |
| Performance（性能问题） | 先 Profile（性能分析），再优化 |
| Intermittent（间歇性） | 寻找竞争条件或时序问题 |
| Memory Leak（内存泄漏） | 检查事件监听器、闭包与缓存 |
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.
- yesterday First seen · 226 lines · 52 tokens per session scan A 029d104b1327
debugger is an agent published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 1,835 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 2026-08-31.
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