parallel-debugging

wshobson/agents · updated Apr 8, 2026

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$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  • Stale cache returning outdated data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
how to use parallel-debugging

How to use parallel-debugging on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add parallel-debugging
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches parallel-debugging from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/parallel-debugging

Reload or restart Cursor to activate parallel-debugging. Access the skill through slash commands (e.g., /parallel-debugging) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

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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