semantic-versioning

aj-geddes/useful-ai-prompts · updated Apr 8, 2026

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$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill semantic-versioning
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summary

Establish semantic versioning practices to maintain consistent version numbering aligned with release significance, enabling automated version management and release notes generation.

skill.md

Semantic Versioning

Table of Contents

Overview

Establish semantic versioning practices to maintain consistent version numbering aligned with release significance, enabling automated version management and release notes generation.

When to Use

  • Package and library releases
  • API versioning
  • Version bumping automation
  • Release note generation
  • Breaking change tracking
  • Dependency management
  • Changelog management

Quick Start

Minimal working example:

# package.json
{
  "name": "my-awesome-package",
  "version": "1.2.3",
  "description": "An awesome package",
  "main": "dist/index.js",
  "repository": { "type": "git", "url": "https://github.com/org/repo.git" },
  "scripts": { "release": "semantic-release" },
  "devDependencies":
    {
      "semantic-release": "^21.0.0",
      "@semantic-release/changelog": "^6.0.0",
      "@semantic-release/git": "^10.0.0",
      "@semantic-release/github": "^9.0.0",
      "conventional-changelog-cli": "^3.0.0",
    },
}

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Semantic Versioning Configuration Semantic Versioning Configuration
Conventional Commits Format Conventional Commits Format
Semantic Release Configuration Semantic Release Configuration
Version Bumping Script Version Bumping Script
Changelog Generation Changelog Generation

Best Practices

✅ DO

  • Follow strict MAJOR.MINOR.PATCH format
  • Use conventional commits
  • Automate version bumping
  • Generate changelogs automatically
  • Tag releases in git
  • Document breaking changes
  • Use prerelease versions for testing

❌ DON'T

  • Manually bump versions inconsistently
  • Skip breaking change documentation
  • Use arbitrary version numbering
  • Mix features in patch releases
how to use semantic-versioning

How to use semantic-versioning 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 semantic-versioning
2

Execute installation command

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

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill semantic-versioning

The skills CLI fetches semantic-versioning from GitHub repository aj-geddes/useful-ai-prompts 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/semantic-versioning

Reload or restart Cursor to activate semantic-versioning. Access the skill through slash commands (e.g., /semantic-versioning) 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.

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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

4.852 reviews
  • Noor Agarwal· Dec 24, 2024

    We added semantic-versioning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Anaya Reddy· Dec 16, 2024

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

  • Chaitanya Patil· Dec 8, 2024

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

  • Piyush G· Nov 27, 2024

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

  • Zara Tandon· Nov 15, 2024

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

  • Advait Rahman· Nov 7, 2024

    We added semantic-versioning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aisha Johnson· Nov 7, 2024

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

  • Advait Diallo· Oct 26, 2024

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

  • Layla Abebe· Oct 26, 2024

    semantic-versioning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Oct 18, 2024

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

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