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github-to-skills▌

kkkkhazix/khazix-skills · updated Apr 9, 2026

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$npx skills add https://github.com/kkkkhazix/khazix-skills --skill github-to-skills
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summary

This skill automates the conversion of GitHub repositories into fully functional AI skills.

skill.md

GitHub to Skills Factory

This skill automates the conversion of GitHub repositories into fully functional AI skills.

Core Functionality

  1. Analysis: Fetches repository metadata (Description, README, Latest Commit Hash).
  2. Scaffolding: Creates a standardized skill directory structure.
  3. Metadata Injection: Generates SKILL.md with extended frontmatter (tracking source, version, hash) for future automated management.
  4. Wrapper Generation: Creates a scripts/wrapper.py (or similar) to interface with the tool.

Usage

Trigger: /GitHub-to-skills <github_url> or "Package this repo into a skill: "

Required Metadata Schema

Every skill created by this factory MUST include the following extended YAML frontmatter in its SKILL.md. This is critical for the skill-manager to function later.

---
name: <kebab-case-repo-name>
description: <concise-description-for-agent-triggering>
# EXTENDED METADATA (MANDATORY)
github_url: <original-repo-url>
github_hash: <latest-commit-hash-at-time-of-creation>
version: <tag-or-0.1.0>
created_at: <ISO-8601-date>
entry_point: scripts/wrapper.py # or main script
dependencies: # List main dependencies if known, e.g., ["yt-dlp", "ffmpeg"]
---

Workflow

  1. Fetch Info: The agent first runs scripts/fetch_github_info.py to get the raw data from the repo.
  2. Plan: The agent analyzes the README to understand how to invoke the tool (CLI args, Python API, etc.).
  3. Generate: The agent uses the skill-creator patterns to write the SKILL.md and wrapper scripts, ensuring the extended metadata is present.
  4. Verify: Checks if the commit hash was correctly captured.

Resources

  • scripts/fetch_github_info.py: Utility to scrape/API fetch repo details (README, Hash, Tags).
  • scripts/create_github_skill.py: Orchestrator to scaffold the folder and write the initial files.

Best Practices for Generated Skills

  • Isolation: The generated skill should install its own dependencies (e.g., in a venv or via uv/pip) if possible, or clearly state them.
  • Progressive Disclosure: Do not dump the entire repo into the skill. Only include the necessary wrapper code and reference the original repo for deep dives.
  • Idempotency: The github_hash field allows the future skill-manager to check if remote_hash != local_hash to trigger updates.
how to use github-to-skills

How to use github-to-skills 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 github-to-skills
2

Execute installation command

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

$npx skills add https://github.com/kkkkhazix/khazix-skills --skill github-to-skills

The skills CLI fetches github-to-skills from GitHub repository kkkkhazix/khazix-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/github-to-skills

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

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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

4.6★★★★★32 reviews
  • ★★★★★Omar Garcia· Dec 28, 2024

    github-to-skills reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Carlos Huang· Dec 24, 2024

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

  • ★★★★★Aditi Torres· Dec 16, 2024

    We added github-to-skills from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Chaitanya Patil· Dec 8, 2024

    Registry listing for github-to-skills matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Piyush G· Nov 27, 2024

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

  • ★★★★★Carlos Zhang· Nov 19, 2024

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

  • ★★★★★Advait Mehta· Nov 7, 2024

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

  • ★★★★★Henry Wang· Oct 26, 2024

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

  • ★★★★★Shikha Mishra· Oct 18, 2024

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

  • ★★★★★Daniel Rahman· Oct 10, 2024

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

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