skill-creator

bobmatnyc/claude-mpm-skills · updated May 14, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill skill-creator
0 commentsdiscussion
summary

Create effective skills that extend Claude's capabilities through specialized knowledge, workflows, and tools. Skills are modular packages that transform Claude from a general-purpose agent into a specialized agent with procedural knowledge for specific domains.

skill.md

Skill Creator

Overview

Create effective skills that extend Claude's capabilities through specialized knowledge, workflows, and tools. Skills are modular packages that transform Claude from a general-purpose agent into a specialized agent with procedural knowledge for specific domains.

This skill exemplifies its own teachings by using progressive disclosure to keep the entry point lean while providing deep detail in reference files.

When to Use This Skill

Activate when:

  • Creating a new skill from scratch
  • Updating or refactoring an existing skill
  • Adding progressive disclosure to monolithic skills
  • Understanding skill structure and best practices
  • Planning skill components (scripts, references, assets)
  • Packaging skills for distribution

Core Principles

  1. Example-Driven Design: Start with concrete usage examples, not abstract concepts
  2. Progressive Disclosure: Keep entry point <200 lines (optimal: 140-160), detailed content in references
  3. Reusable Components: Extract scripts for repeated code, references for knowledge, assets for templates
  4. Imperative Voice: Use verb-first instructions throughout (not second person)
  5. Purpose-Built Resources: Each component should solve specific repetitive needs

What Skills Provide

Four Core Capabilities:

  1. Specialized workflows - Multi-step procedures for specific domains
  2. Tool integrations - Instructions for working with file formats or APIs
  3. Domain expertise - Company-specific knowledge, schemas, business logic
  4. Bundled resources - Scripts, references, and assets for complex tasks

Three-Level Loading System:

  1. Metadata (name + description) - Always in context (~100 words)
  2. SKILL.md body - When skill triggers (<200 lines, optimal: 140-160)
  3. Bundled resources - As needed by Claude (unlimited)

Quick Start

Six-Step Creation Process

Step 1: Understand with Concrete Examples Gather 3-5 realistic usage examples. Ask: "What would users say to trigger this skill?" and "What tasks should it accomplish?" → Complete guide

Step 2: Plan Reusable Components Analyze examples to identify: scripts (repeated code), references (domain knowledge), assets (templates). → Planning guide

Step 3: Initialize Skill Run scripts/init_skill.py <skill-name> --path <output-directory> to generate template structure. → Initialization details

Step 4: Edit Skill Implement scripts/references/assets, then update SKILL.md using imperative voice. Apply progressive disclosure if >150 lines. → Editing guide

Step 5: Package and Validate Run scripts/package_skill.py <path/to/skill-folder> to validate and create distributable zip. → Packaging guide

Step 6: Iterate Use on real tasks, notice struggles, update skill accordingly. → Iteration guide

Skill Anatomy

skill-name/
├── SKILL.md (required)           # Entry point with frontmatter + markdown
├── scripts/ (optional)           # Executable code (Python/Bash)
├── references/ (optional)        # Documentation loaded as needed
└── assets/ (optional)            # Templates, images, files for output

Component Guidelines:

  • Scripts: When same code is repeatedly rewritten or deterministic execution needed
  • References: For detailed specs, workflows, schemas, API docs (150-500 lines each)
  • Assets: For templates, boilerplate, images, fonts used in outputs
  • Entry Point: Core workflow, navigation, reminders (140-160 lines optimal)

Complete structure guide

Progressive Disclosure Pattern

When to apply: Skills >150 lines total

Implementation:

  1. Add progressive_disclosure frontmatter with summary, when_to_use, quick_start
  2. Reduce entry point to 140-160 lines (core workflow + navigation)
  3. Create 3-5 reference files (150-500 lines each)
  4. Organize by topic: structure, workflow, best practices, examples
  5. Add navigation section linking all references

Benefits:

  • Entry loads only essential content
  • Deep detail available when needed
  • Better organization and maintainability
  • 20-30% reduction in entry point size

Complete progressive disclosure guide

Meta-example: This skill-creator demonstrates progressive disclosure:

  • Entry: 150 lines (28% reduction from 209)
  • References: 5 files with complete implementation details
  • Recently optimized: mcp-builder (160 lines), testing-anti-patterns (140 lines)

Navigation

Core Concepts

  • 🏗️ Skill Structure - Anatomy, components (scripts/references/assets), progressive disclosure architecture. Load when planning skill layout or understanding resource types.

Step-by-Step Process

  • 🔄 Creation Workflow - Complete 6-step process from examples to iteration. Load when creating new skill or following structured workflow.

Design Patterns

  • 📊 Progressive Disclosure - Three-level loading, implementation guide, anti-patterns, examples. Load when refactoring skills >150 lines or optimizing context usage.

Quality Standards

  • ✅ Best Practices - Writing style, metadata quality, content organization, anti-patterns. Load when writing/reviewing skill content or ensuring quality.

Real-World Examples

  • 📚 Examples - Complete skill examples: mcp-builder, testing-anti-patterns, pdf-editor, brand-guidelines, database-builder, frontend-builder. Load when starting new skill or seeking patterns.

Key Reminders

  • Start with examples - 3-5 concrete usage scenarios before designing
  • Use init script - scripts/init_skill.py creates proper structure automatically
  • Imperative voice - "To accomplish X, do Y" (not "should do X")
  • Progressive disclosure - Entry <200 lines, details in references (for skills >150 lines)
  • Avoid duplication - Information lives in ONE place (entry summary, reference detail)
  • Component clarity - Scripts for code, references for knowledge, assets for templates
  • Validate before sharing - scripts/package_skill.py validates and packages
  • Iterate continuously - Use on real tasks, update based on struggles

Red Flags - STOP

STOP when:

  • "Let me write all the details in SKILL.md" → Move to references (progressive disclosure)
  • "I'll use second person" → Switch to imperative voice
  • "Same information in entry and reference" → Delete duplication
  • Using generic description → Be specific about activation conditions
  • Leaving example files from init script → Delete unused resources
  • Skipping validation → Always run package_skill.py before sharing
  • Creating skill without examples → Gather concrete usage scenarios first
  • Entry point >200 lines → Apply progressive disclosure pattern

ALL of these mean: STOP. Review principles and references.

Integration with Other Skills

Meta-Skills:

  • skill-creator (this skill) - Creates other skills, demonstrates its own patterns

Development Skills:

  • mcp-builder - Example of progressive disclosure implementation
  • testing-anti-patterns - Example of ultra-lean entry point (140 lines)

Workflow Skills:

  • documentation - Writing clear, structured content
  • verification-before-completion - Testing skills before packaging

Real-World Impact

From skill optimization experience:

  • Progressive disclosure: 20-30% reduction in entry point size
  • mcp-builder: 209 → 160 lines (23% reduction, 6 references)
  • testing-anti-patterns: → 140 lines (ultra-lean with 4 references)
  • skill-creator: 209 → 150 lines (28% reduction, 5 references)
  • Context efficiency: Load only needed references (saves 50-80% context)
  • Maintainability: Update specific references without touching entry point
  • Clarity: Better organization improves discoverability and comprehension

Remember: Skills are modular packages that transform Claude into a specialized agent. Apply progressive disclosure for skills >150 lines. This skill demonstrates the pattern it teaches.

how to use skill-creator

How to use skill-creator 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 skill-creator
2

Execute installation command

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

$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill skill-creator

The skills CLI fetches skill-creator from GitHub repository bobmatnyc/claude-mpm-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/skill-creator

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

GET_STARTED →

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

4.660 reviews
  • Aditi Liu· Dec 28, 2024

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

  • Soo Malhotra· Dec 28, 2024

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

  • Henry Zhang· Dec 24, 2024

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

  • Liam Martin· Dec 24, 2024

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

  • Ira Harris· Dec 20, 2024

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

  • Ganesh Mohane· Dec 16, 2024

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

  • Charlotte White· Dec 8, 2024

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

  • Liam Thompson· Nov 27, 2024

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

  • Soo Johnson· Nov 19, 2024

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

  • Sofia Zhang· Nov 15, 2024

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

showing 1-10 of 60

1 / 6