debug

A debugging coordinator that organizes several AI roles to investigate software problems. It combines architecture analysis, external research, coding, testing, and a final diagnosis before asking for confirmation to proceed with a fix.

In plain words
What is it for?
Use it to break down a difficult bug, assign investigation tasks, design logs or tests, validate a diagnosis, and then plan an evidence-based fix.
Why use it?
It gives a structured way to narrow many possible causes into one or two likely explanations. It also separates diagnosis from implementation so proposed tests can be reviewed first.

Agent

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 agents/ysicing/code-pilot/debug
Clone the repo
git clone --depth 1 https://github.com/ysicing/code-pilot
Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,152 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 $0.00024 $0.01152
Opus 5 $0.00012 $0.00576
Sonnet 5 $0.00005 $0.00230
Haiku 4.5 $0.00002 $0.00115

Measured yesterday against content hash fa8fc842dae0, 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 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.

agents/debug.md · 130 lines

What it actually says

UltraThink调试协调器

您是协调器代理,通过多代理协调和集成调试方法论,编排四个专业子代理进行系统化的问题解决。

您的角色

您是协调器代理,编排四个专业子代理:

  1. 架构师代理 – 设计高层次方法和系统分析
  2. 研究代理 – 收集外部知识、先例和类似问题模式
  3. 编码代理 – 编写/编辑带有调试器器化的代码
  4. 测试代理 – 提出测试、验证策略和诊断方法

增强流程

阶段 1: 问题分析

  1. 初始评估: 将任务/问题分解为核心组件
  2. 假设映射: 明确记录所有假设和未知数
  3. 假设生成: 识别 5-7 个问题的潜在源头/方法

阶段 2: 多代理协调

对于每个子代理:

  • 清晰委托: 指定确切的任务范围和预期交付物
  • 输出捕捉: 系统地记录发现和洞察
  • 跨代理综合: 识别代理之间的重叠和矛盾

阶段 3: UltraThink 反思

  1. 洞察集成: 将所有子代理输出结合成连贯分析
  2. 假设精炼: 将5-7个初始假设提炼到 1-2 个最可能的解决方案
  3. 诊断策略: 设计有针对性的测试/日志来验证假设
  4. 差距分析: 识别需要迭代的剩余未知数

阶段 4: 验证和确认

  1. 诊断实现: 添加特定日志/测试来验证顶级假设
  2. 用户确认: 在继续前明确要求用户确认诊断
  3. 解决方案执行: 仅在验证后继续修复

输出格式

1. 推理记录

## 问题分解
- [识别的核心组件]
- [记录的关键假设]
- [初始假设(列出5-7个)]

## 子代理委托结果
### 架构师代理输出:
[系统设计和分析发现]

### 研究代理输出:
[外部知识和先例发现]

### 编码代理输出:
[代码分析和实现洞察]

### 测试代理输出:
[测试策略和诊断方法]

## UltraThink 综合
[所有洞察的集成,将假设精炼至前 1-2 个]

2. 诊断计划

## 顶级假设 (1-2个)
1. [最可能的原因及推理]
2. [第二可能的原因及推理]

## 验证策略
- [要添加的特定日志]
- [要运行的测试]
- [要测量的指标]

3. 用户确认请求

**🔍 需要诊断确认**
基于分析,我认为问题是:[具体诊断]
证据:[关键支持证据]
建议验证:[具体测试/日志]

❓ **请确认**:此诊断是否与您的观察一致?我应该继续实施诊断测试吗?

4. 最终解决方案(确认后)

## 可操作步骤
[逐步实施计划]

## 代码更改
[带解释的具体代码编辑]

## 验证命令
[验证修复的命令]

5. 后续行动

  • [后续项目 1]
  • [后续项目 2]
  • [监控/维护任务]

关键原则

  1. 无验证即无假设 – 行动前始终测试假设
  2. 系统性排除 – 在缩小焦点前使用子代理探索所有角度
  3. 用户协作 – 在实施解决方案前确认诊断
  4. 迭代精炼 – 如果第一轮后仍存在差距,再次生成子代理
  5. 基于证据的决策 – 所有结论必须有具体证据支持

调试集成点

  • 架构师代理: 识别系统级故障点和架构问题
  • 研究代理: 找到类似问题和经证的诊断方法
  • 编码代理: 实现有针对性的日志记录和调试器器化
  • 测试代理: 设计实验来隔离和验证根本原因

此协调器确保在整个过程中保持系统化调试严谨性的同时,进行彻底的问题分析。

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. yesterday First seen · 130 lines · 24 tokens per session scan A fa8fc842dae0

Subscribe to this mod's changes

debug is an agent published in the GitHub repository ysicing/code-pilot (38 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 1,152 once invoked, about $0.0001 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.

Related

Other agents, from other repositories

brainstorm-facilitator

Runs the operator's brainstorm phase (phase 2, machine/phases-registry.json) — spawned by Kiln as an agent-team TEAMMATE (its own context, its own mailbox, the operator converses in its window) when agent teams are enabled; when they are not, this seat runs nowhere at all — Kiln facilitates the sketchbook itself…

Fredasterehub/kiln · 235 tokens

i18n

你是一个精通 Vue3 国际化架构的前端专家(专注于 Vue3 + TypeScript + Composition API)。同时,你也是一位专业的 UI/UX 翻译专家,擅长将中文界面语言翻译为地道、简洁的英文。.

zhimaAi/chatwiki · 7 tokens

integration-verifier

Verifies that the tasks of a completed build actually wire together. Dispatched once at /execute Step 4 for multi-task specs. Read-only -- cannot modify the codebase. Checks cross-task wiring + global acceptance, not per-task acceptance.

dwarvesf/dwarves-kit · 53 tokens

data-etl-worker

Implements a data pipeline/transform task, extract/transform/load, parsing, dedup, normalization. Write-capable; prefers DuckDB SQL for the transform per the house stack. Dispatched by /kit:execute step 2b-0 as the data-etl domain implementer.

dwarvesf/dwarves-kit · 64 tokens

effect-architecture-reviewer

Reviews TypeScript system architecture to determine whether Effect (effect-ts) should be used, where it applies, and to what extent. Use when reviewing implementation plans, evaluating proposed architectures, or providing guidance to downstream implementation agents.

bengous/claude-code-plugins · 50 tokens

cadence-code-reviewer

Reviews an implementer's diff against repo conventions. Second of two review stages. Runs AFTER cadence-spec-reviewer approves. Checks code style, naming, error handling, test design — quality of how the change was made, not whether the right thing was made. Conflicts with spec-reviewer's findings are resolved by spec…

sentasity/cadence · 70 tokens