Agent Skillswshobson/agents › parallel-debugging

parallel-debugging

GitHub

基于竞争假设分析(ACH)的并行调试技能,用于处理多根因、跨模块的复杂Bug。通过并行调查、证据收集与根因仲裁,系统化排除干扰,避免确认偏误,精准定位问题根源。

plugins/agent-teams/skills/parallel-debugging/SKILL.md wshobson/agents

Trigger Scenarios

存在多个潜在根因的复杂Bug 需要系统性根因分析 初始调试未找到问题 涉及多模块或组件的问题

Install

npx skills add wshobson/agents --skill parallel-debugging -g -y
More Options

Non-standard path

npx skills add https://github.com/wshobson/agents/tree/main/plugins/agent-teams/skills/parallel-debugging -g -y

Use without installing

npx skills use wshobson/agents@parallel-debugging

指定 Agent (Claude Code)

npx skills add wshobson/agents --skill parallel-debugging -a claude-code -g -y

安装 repo 全部 skill

npx skills add wshobson/agents --all -g -y

预览 repo 内 skill

npx skills add wshobson/agents --list

SKILL.md

Frontmatter
{
    "name": "parallel-debugging",
    "version": "1.0.2",
    "description": "Debug complex issues using competing hypotheses with parallel investigation, evidence collection, and root cause arbitration. Use this skill when debugging bugs with multiple potential causes, performing root cause analysis, or organizing parallel investigation workflows."
}

Parallel Debugging

Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation.

When to Use This Skill

  • Bug has multiple plausible root causes
  • Initial debugging attempts haven't identified the issue
  • Issue spans multiple modules or components
  • Need systematic root cause analysis with evidence
  • Want to avoid confirmation bias in debugging

Hypothesis Generation Framework

Generate hypotheses across 6 failure mode categories:

1. Logic Error

  • Incorrect conditional logic (wrong operator, missing case)
  • Off-by-one errors in loops or array access
  • Missing edge case handling
  • Incorrect algorithm implementation

2. Data Issue

  • Invalid or unexpected input data
  • Type mismatch or coercion error
  • Null/undefined/None where value expected
  • Encoding or serialization problem
  • Data truncation or overflow

3. State Problem

  • Race condition between concurrent operations
  • Stale cache returning outdated data
  • Incorrect initialization or default values
  • Unintended mutation of shared state
  • State machine transition error

4. Integration Failure

  • API contract violation (request/response mismatch)
  • Version incompatibility between components
  • Configuration mismatch between environments
  • Missing or incorrect environment variables
  • Network timeout or connection failure

5. Resource Issue

  • Memory leak causing gradual degradation
  • Connection pool exhaustion
  • File descriptor or handle leak
  • Disk space or quota exceeded
  • CPU saturation from inefficient processing

6. Environment

  • Missing runtime dependency
  • Wrong library or framework version
  • Platform-specific behavior difference
  • Permission or access control issue
  • Timezone or locale-related behavior

Evidence Collection Standards

What Constitutes Evidence

Evidence Type Strength Example
Direct Strong Code at file.ts:42 shows if (x > 0) should be if (x >= 0)
Correlational Medium Error rate increased after commit abc123
Testimonial Weak "It works on my machine"
Absence Variable No null check found in the code path

Citation Format

Always cite evidence with file:line references:

**Evidence**: The validation function at `src/validators/user.ts:87`
does not check for empty strings, only null/undefined. This allows
empty email addresses to pass validation.

Confidence Levels

Level Criteria
High (>80%) Multiple direct evidence pieces, clear causal chain, no contradicting evidence
Medium (50-80%) Some direct evidence, plausible causal chain, minor ambiguities
Low (<50%) Mostly correlational evidence, incomplete causal chain, some contradicting evidence

Result Arbitration Protocol

After all investigators report:

Step 1: Categorize Results

  • Confirmed: High confidence, strong evidence, clear causal chain
  • Plausible: Medium confidence, some evidence, reasonable causal chain
  • Falsified: Evidence contradicts the hypothesis
  • Inconclusive: Insufficient evidence to confirm or falsify

Step 2: Compare Confirmed Hypotheses

If multiple hypotheses are confirmed, rank by:

  1. Confidence level
  2. Number of supporting evidence pieces
  3. Strength of causal chain
  4. Absence of contradicting evidence

Step 3: Determine Root Cause

  • If one hypothesis clearly dominates: declare as root cause
  • If multiple hypotheses are equally likely: may be compound issue (multiple contributing causes)
  • If no hypotheses confirmed: generate new hypotheses based on evidence gathered

Step 4: Validate Fix

Before declaring the bug fixed:

  • Fix addresses the identified root cause
  • Fix doesn't introduce new issues
  • Original reproduction case no longer fails
  • Related edge cases are covered
  • Relevant tests are added or updated

Version History

  • 367cb6a Current 2026-08-20 06:48

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Metadata

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Version
367cb6a
Hash
31b6e288
Indexed
2026-08-20 06:48

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