diagnose

GitHub

用于调查未知原因的系统异常、间歇性故障或行为突变。通过区分事实与假设,构建可证伪的根因假设,利用现有日志和最小化探针收集证据,最终输出结构化诊断报告,定位基础设施或代码层面的根本原因。

.claude/skills/diagnose/SKILL.md avibebuilder/claude-prime

Trigger Scenarios

遇到无法解释的行为变化或指标异常 本地正常但生产环境失败或间歇性错误 代码逻辑正确但结果错误 需要排查根因而非实施已知修复

Install

npx skills add avibebuilder/claude-prime --skill diagnose -g -y
More Options

Non-standard path

npx skills add https://github.com/avibebuilder/claude-prime/tree/main/.claude/skills/diagnose -g -y

Use without installing

npx skills use avibebuilder/claude-prime@diagnose

指定 Agent (Claude Code)

npx skills add avibebuilder/claude-prime --skill diagnose -a claude-code -g -y

安装 repo 全部 skill

npx skills add avibebuilder/claude-prime --all -g -y

预览 repo 内 skill

npx skills add avibebuilder/claude-prime --list

SKILL.md

Frontmatter
{
    "name": "diagnose",
    "description": "Investigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging\/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any \"why did this change?\" or \"why is this happening?\" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.",
    "argument-hint": "bug-description"
}

Think harder.

Process

Check conversation context and skip completed steps.

1. Understand the symptom

  • Read the bug report, errors, logs, and surrounding code carefully
  • Clarify reproduction steps, expected behavior, and environment when they are unclear
  • Separate confirmed facts from working assumptions. List them explicitly:
    • Fact (confirmed): the server returns 200
    • Assumption (unconfirmed): the client receives the full HTML body Misidentifying an assumption as a fact is the most common source of wasted investigation.

2. Build hypotheses

  • Form 2-4 plausible root-cause hypotheses that are mechanistically distinct — different failure layers (e.g., server render vs. client hydration vs. network layer), not variations of the same idea

  • Rank them by likelihood

  • For each hypothesis, state both sides:

    • Confirm if: [what observation would prove this is the cause]
    • Eliminate if: [what observation would rule this out]

    A hypothesis you can't falsify in both directions is too vague to test.

3. Choose the lightest evidence method

Start with the cheapest source of truth that can kill hypotheses:

  • existing logs, traces, stack traces, metrics, and error output
  • static code inspection around the suspected path
  • config, environment, deploy, cache, queue, and permissions state that could explain the symptom
  • targeted reproduction in the relevant environment

Only add new instrumentation when existing evidence is insufficient.

  • If you need runtime probes, read diagnose/references/runtime-debugging.md
  • Use #region agent log / #endregion markers for any instrumentation you add
  • Tag each log point with the relevant hypothesisId
  • Log only the minimum fields needed to discriminate between hypotheses; never log secrets, tokens, passwords, cookies, or full sensitive payloads
  • If runtime probes require starting the local debug server, ask the user before launching it
  • For browser/UI bugs, combine with the agent-browser skill when reproduction or inspection needs it

4. Gather evidence and iterate

  • Use existing logs, traces, failing tests, or artifacts before asking for a fresh reproduction
  • When reproduction is needed, ask the user to trigger the bug — tie each request to the hypothesis it tests
  • Correlate each finding with the hypothesis it supports or eliminates; narrow based on evidence, not confidence
  • If ambiguity remains, refine hypotheses and add narrower probes — but stop and report when another round is unlikely to produce new discriminating evidence

5. Report the diagnosis

Output structured diagnosis:

## Diagnosis: [Issue Title]

### Symptoms
- [What was observed]

### Evidence
- [Finding] — `file:line` or runtime source — hypothesis X
- ...

### Root Cause
[Confirmed or most likely cause, with evidence]

### Hypotheses Tested
| # | Hypothesis | Confirm if | Eliminate if | Result |
|---|-----------|-----------|-------------|--------|
| A | ... | [what observation would prove this] | [what observation would rule this out] | Confirmed/Eliminated/Inconclusive |

### Recommended Next Steps
- [What to do next — usually hand off to `/fix` with this diagnosis]

### Active Instrumentation
- [List files with `#region agent log` blocks still in place, or `None`]

Constraints

  • NO fixing — investigation and diagnosis only. "Recommended Next Steps" hands off to /fix with the diagnosis; it does not prescribe specific parameter values, code snippets, or step-by-step implementation instructions.
  • Evidence over assumptions — if the code looks wrong but runtime evidence says otherwise, trust the runtime
  • If you add #region agent log blocks, leave them in place for /fix to verify the repair and call them out in the final report

Bug

$ARGUMENTS

Version History

  • 80bcfa4 Current 2026-07-05 12:10

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