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Parallel Debugging

Seth HobsonCommunityCreated by Seth Hobson

  • Skill
  • Guidelines
  • Software development
  • MIT
  • No issues found

From the author

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.
— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Parallel Debugging provides a systematic methodology for resolving complex software issues by applying the Analysis of Competing Hypotheses (ACH). It guides users through generating hypotheses across six failure mode categories, collecting evidence with specific citation standards, and using a formal arbitration protocol to determine the root cause. This approach helps teams avoid confirmation bias and manage debugging workflows where multiple potential causes exist.

What it does

  • Categorizes potential bugs into logic, data, state, integration, resource, or environment issues.
  • Provides templates for hypothesis investigation and evidence reporting.
  • Establishes clear criteria for evidence strength and confidence levels.
  • Implements a structured arbitration protocol to rank and confirm root causes.
  • Offers a decision tree for handling single, compound, or inconclusive findings.
  • Standardizes the validation process for proposed fixes.

When to use it

  • When a bug has multiple plausible root causes.
  • When initial debugging attempts have failed to identify the issue.
  • When an issue spans multiple modules or components.
  • When you need a systematic, evidence-based approach to root cause analysis.
  • When you want to mitigate confirmation bias during the debugging process.

Advantages

  • Reduces bias by forcing the evaluation of competing hypotheses.
  • Standardizes evidence collection through strict citation requirements.
  • Provides a clear path for resolving compound issues with multiple contributing causes.
  • Improves debugging efficiency for complex, intermittent, or cross-component bugs.
  • Ensures fixes are validated against the identified root cause and related edge cases.

Usage examples

“I have an intermittent 500 error in production. How should I start debugging?”
The agent will guide you to generate hypotheses based on the '500 Internal Server Error' patterns, such as unhandled exceptions, database connection failures, or missing environment variables, and initiate parallel investigations for each.
“We have a regression after a recent deploy. Can you help us find the cause?”
The agent will help you structure an investigation into logic errors, configuration changes, or database migration issues, using the provided templates to collect evidence and arbitrate the findings.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/wshobson/parallel-debugging/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip parallel-debugging.zip -d .agents/skills

Documentation by Seth Hobson

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 TypeStrengthExample
DirectStrongCode at file.ts:42 shows if (x > 0) should be if (x >= 0)
CorrelationalMediumError rate increased after commit abc123
TestimonialWeak"It works on my machine"
AbsenceVariableNo 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
LevelCriteria
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

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Seth Hobson
Publisher
Seth Hobson (Community)
Source
wshobson/agents · plugins/agent-teams/skills/parallel-debugging · commit 4bde316
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Seth Hobson.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download parallel-debugging.zip