debugging

mrgoonie/claudekit-skills · updated Apr 8, 2026

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$npx skills add https://github.com/mrgoonie/claudekit-skills --skill debugging
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summary

A collection of systematic debugging methodologies that ensure thorough investigation before attempting fixes.

skill.md

Debugging Skills

A collection of systematic debugging methodologies that ensure thorough investigation before attempting fixes.

Available Sub-Skills

Systematic Debugging

Location: systematic-debugging/SKILL.md

Four-phase debugging framework: Root Cause Investigation → Pattern Analysis → Hypothesis Testing → Implementation. The iron law: NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.

Root Cause Tracing

Location: root-cause-tracing/SKILL.md

Trace bugs backward through the call stack to find the original trigger. Don't fix symptoms - find where invalid data originated and fix at the source.

Defense-in-Depth Validation

Location: defense-in-depth/SKILL.md

Validate at every layer data passes through to make bugs structurally impossible. Four layers: Entry Point → Business Logic → Environment Guards → Debug Instrumentation.

Verification Before Completion

Location: verification-before-completion/SKILL.md

Run verification commands and confirm output before claiming success. The iron law: NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE.

When to Use

  • Bug in production → Start with systematic-debugging
  • Error deep in stack trace → Use root-cause-tracing
  • Fixing a bug → Apply defense-in-depth after finding root cause
  • About to claim "done" → Use verification-before-completion

Quick Dispatch

Symptom Sub-Skill
Test failure, unexpected behavior systematic-debugging
Error appears in wrong location root-cause-tracing
Same bug keeps recurring defense-in-depth
Need to confirm fix works verification-before-completion

Core Philosophy

"Systematic debugging is FASTER than guess-and-check thrashing."

From real debugging sessions:

  • Systematic approach: 15-30 minutes to fix
  • Random fixes approach: 2-3 hours of thrashing
  • First-time fix rate: 95% vs 40%
how to use debugging

How to use 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 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/mrgoonie/claudekit-skills --skill debugging

The skills CLI fetches debugging from GitHub repository mrgoonie/claudekit-skills 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/debugging

Reload or restart Cursor to activate debugging. Access the skill through slash commands (e.g., /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

Submit your Claude Code skill and start earning

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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

4.573 reviews
  • Chen Ramirez· Dec 20, 2024

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

  • Amina Chawla· Dec 16, 2024

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

  • Ira Ghosh· Dec 12, 2024

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

  • James Garcia· Dec 8, 2024

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

  • Amina Agarwal· Dec 4, 2024

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

  • Diya Yang· Nov 27, 2024

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

  • Nikhil Sanchez· Nov 23, 2024

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

  • Yusuf Johnson· Nov 15, 2024

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

  • Arya Kim· Nov 7, 2024

    I recommend debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yuki Patel· Nov 7, 2024

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

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