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parallel-debugging

wshobson/agents

Debug complex issues systematically using competing hypotheses and parallel investigation.

What is parallel-debugging?

A framework for debugging multifaceted problems using the Analysis of Competing Hypotheses (ACH) methodology. Organize parallel agent investigation across six failure mode categories, collect evidence with proper citation, and arbitrate competing root causes using confidence levels and causal chains.

  • Generate hypotheses across 6 failure mode categories: logic errors, data issues, state problems, integration failures, resource issues, and environment problems
  • Collect and categorize evidence by strength (direct, correlational, testimonial, absence) with file:line citations
  • Assign confidence levels to findings based on evidence quality and causal chain clarity
  • Arbitrate between competing hypotheses by comparing confidence, evidence count, and causal strength
  • Validate fixes against the identified root cause and related edge cases

How to install parallel-debugging

npx skills add https://github.com/wshobson/agents --skill parallel-debugging
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How to use parallel-debugging

  1. 1.Generate 2-3 hypotheses per failure mode category (6 categories total) based on the bug description and symptoms
  2. 2.Assign each hypothesis to a parallel investigator to collect evidence
  3. 3.Have each investigator cite evidence with file:line references and assess confidence level (high >80%, medium 50-80%, low <50%)
  4. 4.Collect all investigation results and categorize each hypothesis as confirmed, plausible, falsified, or inconclusive
  5. 5.Compare confirmed hypotheses by confidence level, evidence count, and causal chain strength to identify the root cause
  6. 6.Validate the fix addresses the root cause, doesn't introduce new issues, and covers related edge cases

Use cases

Good for
  • Debugging a bug with multiple plausible root causes across different modules
  • Performing systematic root cause analysis when initial debugging attempts fail
  • Organizing parallel investigation workflows where different agents explore different hypotheses
  • Avoiding confirmation bias by systematically evaluating evidence against all hypotheses
  • Determining whether an issue has a single root cause or multiple contributing factors
Who it's for
  • Software engineers debugging complex issues
  • DevOps engineers performing root cause analysis
  • QA teams investigating multi-component failures
  • Technical leads coordinating parallel debugging efforts

parallel-debugging FAQ

What if multiple hypotheses are confirmed with equal confidence?

The issue may have multiple contributing causes. Rank by number of supporting evidence pieces and strength of causal chain. Consider whether fixing one cause resolves the issue or if all must be addressed.

How do I cite evidence properly?

Always include file path and line number with a direct quote or reference. Example: 'The validation function at src/validators/user.ts:87 does not check for empty strings, only null/undefined.'

What counts as strong evidence?

Direct evidence is strongest: code showing the actual bug, logs proving a specific failure, or a clear causal chain. Correlational evidence (timing of changes) is medium strength. Testimonial or absence of evidence is weaker.

When should I use this skill instead of ad-hoc debugging?

Use this when a bug has multiple plausible causes, initial debugging hasn't worked, the issue spans multiple components, or you need to avoid confirmation bias and document your reasoning.

How do I handle inconclusive hypotheses?

If evidence is insufficient to confirm or falsify a hypothesis, generate new hypotheses based on the evidence gathered during investigation, then continue parallel investigation on the new candidates.

Full instructions (SKILL.md)

Source of truth, from wshobson/agents.


name: parallel-debugging 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. version: 1.0.2

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