debug

A debugging tool for finding the cause of known errors, stack traces, failed tests, or broken scripts, then applying a small fix and checking it. A stack trace is the recorded path of function calls that led to an error.

In plain words
What is it for?
Use it with an error message, reproduction steps, or an expected-versus-actual description. It can inspect related code, make the minimal repair, run regression checks, and summarize the result.
Why use it?
It turns an observed failure into a reproducible diagnosis instead of guessing or making a broad rewrite. It checks the original failure again to reduce the chance of leaving the problem unresolved.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/lync-cyber/cataforge/debug
Any agent
npx skills add lync-cyber/CataForge --skill debug
Clone the repo
git clone --depth 1 https://github.com/lync-cyber/CataForge

Made for: Claude Code, Codex.

Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,465 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00099 $0.01465
Opus 5 $0.00049 $0.00732
Sonnet 5 $0.00020 $0.00293
Haiku 4.5 $0.00010 $0.00146

Measured 2d ago against content hash 6cef5efa3018, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug 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 2d 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.

.cataforge/skills/debug/SKILL.md · 97 lines

How it starts

The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.

调试诊断 (debug)

能力边界

  • 能做: 分析错误/stacktrace、定位根因、应用最小修复、回归验证、检查同类问题
  • 不做: 功能开发、性能优化、架构重构、需求变更

输入规范

调用方提供以下信息(至少一项):

  • 错误信息 / stacktrace
  • 触发命令或复现步骤
  • 期望 vs 实际行为描述

输出规范

  • 修复后的文件(就地修改)
  • 回归验证结果(测试通过或命令成功)
  • 执行摘要: 根因 + 修复措施 + 验证结果

操作指令

语言细则: 根据 framework.json project.languages,按需载入本 skill references/lang-<lang>.md(仅 active 语言,逐个 Read),获取对应语言的调试诊断细则(调试器使用、traceback 解读、日志实践、运行时问题定位)。与本 skill references/debug-patterns.md(scan-similar 的特征模式范例)分工:lang-.md 为完整调试细则,debug-patterns 为同类扫描的模式库。

指令1: 完整调试流程 (full)

适用于: 收到错误报告,需要从零开始诊断和修复。

Step 1: 复现与信息收集

  1. 解析错误信息,提取关键信号: 文件路径、行号、异常类型、错误消息
  2. 如有复现命令,执行以确认问题可复现
  3. 如错误信息不完整(缺少 stacktrace 或文件路径),通过 AskUserQuestion 请求补充(每批问题数不超过 MAX_QUESTIONS_PER_BATCH)

Step 2: 定位根因

  1. 从 stacktrace 最内层帧开始,Read 相关文件和行号
  2. 向调用链上游追溯,理解数据流和控制流
  3. 识别根因类别:
    • 编码/环境: 字符编码、路径分隔符、平台差异、语言运行时版本
    • 数据/类型: 空值、类型不匹配、格式解析错误、边界条件
    • 依赖/配置: 缺失依赖、版本不兼容、配置错误
    • 逻辑: 算法错误、状态管理、竞态条件
  4. 形成根因假设并通过代码阅读或小范围测试验证

Step 3: 应用修复

  1. 使用 Edit 工具应用最小化修复(只改必要的代码)
  2. 如修复模式适用于多个文件,使用 Grep 扫描同类问题并一并修复
  3. 确保修复不改变公共接口行为(如需改变,返回 needs_input)

Step 4: 回归验证

  1. 重新执行触发错误的命令,确认问题已修复
  2. 运行相关测试套件(如有),确认未引入新失败
  3. 如无自动化测试,手动验证关键路径

Step 5: 总结

  1. 输出执行摘要: 根因(一句话)、修复措施、验证结果、同类修复(如有)

指令2: 快速修复 (quick-fix)

适用于: 根因已明确(如用户已定位到具体文件和行),剩余动作仅为应用修复和验证。

Step 1: Read 相关文件确认问题 Step 2: 应用修复(Edit) Step 3: Grep 检查同类问题并一并修复 Step 4: 回归验证(Bash 运行命令/测试) Step 5: 输出摘要

指令3: 同类扫描 (scan-similar)

适用于: 已修复一个问题,需要检查整个项目中是否存在同类问题。

Step 1: 从已修复的问题中提取特征模式(如未显式声明编码的文件读写、未转义的正则元字符;语言特定示例见 debug-patterns.mdStep 2: 使用 Grep 在项目范围内搜索该模式 Step 3: 逐一检查匹配项,判断是否存在同类问题 Step 4: 对确认存在问题的文件应用修复 Step 5: 汇总扫描结果: 检查了 N 个文件,修复了 M 个

常见问题模式库

语言/平台特定的症状—修复对照(Python/Windows 等)见 debug-patterns.md,新增语言在该文档增条。

Anti-Patterns

  • 禁止: 修复表面 symptom 而不查 root cause —— 让最近一次报错消失不等于修了 bug,root_cause 可能在两层调用栈以上
  • 禁止: 改测试让它通过而非改实现 —— 测试是契约,把红灯绿掉是把契约改成了已坏状态的快照
  • 禁止: 静默 catch + pass 吞异常 —— 信号被埋掉后续 debug 复杂度指数上升
  • 避免: 一次改多个变量再跑验证 —— 失败时无法定位是哪个改动生效,回到二分查找

Read the full file on GitHub · 97 lines

Files

What ships with it

7 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. 2d ago First seen · 97 lines · 99 tokens per session scan A 6cef5efa3018

Subscribe to this mod's changes

debug is a skill published in the GitHub repository lync-cyber/CataForge (128 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 1,465 once invoked, about $0.0005 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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