parallel-debugging

wshobson/agents · updated Apr 8, 2026

$npx skills add https://github.com/wshobson/agents --skill parallel-debugging
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summary

Systematic debugging framework using competing hypotheses to identify root causes across multiple failure categories.

  • Generates hypotheses across six failure mode categories: logic errors, data issues, state problems, integration failures, resource issues, and environment mismatches
  • Establishes evidence standards with citation requirements (file:line references) and confidence levels (high/medium/low) to avoid confirmation bias
  • Supports parallel agent investigation with structured re
skill.md

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  • Stale cache returning outdated data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

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.672 reviews
  • Maya Lopez· Dec 28, 2024

    parallel-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Pratham Ware· Dec 20, 2024

    Registry listing for parallel-debugging matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Soo Malhotra· Dec 20, 2024

    parallel-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Anika Harris· Dec 12, 2024

    parallel-debugging reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ama Kim· Dec 12, 2024

    Keeps context tight: parallel-debugging is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Hassan Torres· Dec 12, 2024

    parallel-debugging fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Jin Perez· Dec 8, 2024

    Useful defaults in parallel-debugging — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Lopez· Nov 27, 2024

    parallel-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Luis Choi· Nov 19, 2024

    Useful defaults in parallel-debugging — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kaira Chen· Nov 15, 2024

    Solid pick for teams standardizing on skills: parallel-debugging is focused, and the summary matches what you get after install.

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