git:analyze-issue

neolabhq/context-engineering-kit · updated Apr 8, 2026

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$npx skills add https://github.com/neolabhq/context-engineering-kit --skill git:analyze-issue
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summary

Please analyze GitHub issue #$ARGUMENTS and create a technical specification.

skill.md

Please analyze GitHub issue #$ARGUMENTS and create a technical specification.

Follow these steps:

  1. Check if the issue is already loaded:

    • Look for the issue file in ./specs/issues/ folder
    • File naming pattern: <number-padded-to-3-digits>-<kebab-case-title>.md
    • If not found, fetch the issue details from GitHub (see step 2)
  2. Fetch the issue details (if not already loaded):

    • Read .claude/commands/load-issues.md to understand how to fetch issue details
    • Save the issue file following the load-issues.md format
  3. Understand the requirements thoroughly

  4. Review related code and project structure

  5. Create a technical specification with the format below

Technical Specification for Issue #$ARGUMENTS

Issue Summary

  • Title: [Issue title from GitHub]
  • Description: [Brief description from issue]
  • Labels: [Labels from issue]
  • Priority: [High/Medium/Low based on issue content]

Problem Statement

[1-2 paragraphs explaining the problem]

Technical Approach

[Detailed technical approach]

Implementation Plan

  1. [Step 1]
  2. [Step 2]
  3. [Step 3]

Test Plan

  1. Unit Tests:
    • [test scenario]
  2. Component Tests:
    • [test scenario]
  3. Integration Tests:
    • [test scenario]

Files to Modify

Files to Create

Existing Utilities to Leverage

Success Criteria

  • [criterion 1]
  • [criterion 2]

Out of Scope

  • [item 1]
  • [item 2]

Remember to follow our strict TDD principles, KISS approach, and 300-line file limit.

IMPORTANT: After completing your analysis, SAVE the full technical specification to: ./specs/issues/<number-padded-to-3-digits>-<kebab-case-title>.specs.md

For example, for issue #7 with title "Make code review trigger on any *.SQL and .sh file changes", save to: ./specs/issues/007-make-code-review-trigger-on-sql-sh-changes.specs.md

After saving, provide a brief summary to the user confirming:

  • Issue number and title analyzed
  • File path where the specification was saved
  • Key highlights from the specification (2-3 bullet points)
how to use git:analyze-issue

How to use git:analyze-issue 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 git:analyze-issue
2

Execute installation command

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

$npx skills add https://github.com/neolabhq/context-engineering-kit --skill git:analyze-issue

The skills CLI fetches git:analyze-issue from GitHub repository neolabhq/context-engineering-kit 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/git:analyze-issue

Reload or restart Cursor to activate git:analyze-issue. Access the skill through slash commands (e.g., /git:analyze-issue) 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)
  • No comments yet — start the thread.
general reviews

Ratings

4.746 reviews
  • Jin Tandon· Dec 24, 2024

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

  • Xiao Huang· Dec 20, 2024

    git:analyze-issue reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Shikha Mishra· Dec 4, 2024

    Registry listing for git:analyze-issue matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Arjun Ghosh· Dec 4, 2024

    git:analyze-issue is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Michael Chen· Dec 4, 2024

    git:analyze-issue fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yash Thakker· Nov 23, 2024

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

  • Min Mensah· Nov 23, 2024

    We added git:analyze-issue from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sakshi Patil· Nov 15, 2024

    git:analyze-issue reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Evelyn Li· Nov 15, 2024

    Registry listing for git:analyze-issue matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Naina Zhang· Nov 11, 2024

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

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