llama-factory

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill llama-factory
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summary

Comprehensive assistance with llama-factory development, generated from official documentation.

skill.md

Llama-Factory Skill

Comprehensive assistance with llama-factory development, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

  • Working with llama-factory
  • Asking about llama-factory features or APIs
  • Implementing llama-factory solutions
  • Debugging llama-factory code
  • Learning llama-factory best practices

Quick Reference

Common Patterns

Quick reference patterns will be added as you use the skill.

Reference Files

This skill includes comprehensive documentation in references/:

  • _images.md - Images documentation
  • advanced.md - Advanced documentation
  • getting_started.md - Getting Started documentation
  • other.md - Other documentation

Use view to read specific reference files when detailed information is needed.

Working with This Skill

For Beginners

Start with the getting_started or tutorials reference files for foundational concepts.

For Specific Features

Use the appropriate category reference file (api, guides, etc.) for detailed information.

For Code Examples

The quick reference section above contains common patterns extracted from the official docs.

Resources

references/

Organized documentation extracted from official sources. These files contain:

  • Detailed explanations
  • Code examples with language annotations
  • Links to original documentation
  • Table of contents for quick navigation

scripts/

Add helper scripts here for common automation tasks.

assets/

Add templates, boilerplate, or example projects here.

Notes

  • This skill was automatically generated from official documentation
  • Reference files preserve the structure and examples from source docs
  • Code examples include language detection for better syntax highlighting
  • Quick reference patterns are extracted from common usage examples in the docs

Updating

To refresh this skill with updated documentation:

  1. Re-run the scraper with the same configuration
  2. The skill will be rebuilt with the latest information
how to use llama-factory

How to use llama-factory 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 llama-factory
2

Execute installation command

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

$npx skills add https://github.com/davila7/claude-code-templates --skill llama-factory

The skills CLI fetches llama-factory from GitHub repository davila7/claude-code-templates 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/llama-factory

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

4.771 reviews
  • Arya Huang· Dec 28, 2024

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

  • Shikha Mishra· Dec 24, 2024

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

  • Tariq Diallo· Dec 24, 2024

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

  • Neel Ndlovu· Dec 12, 2024

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

  • Sakshi Patil· Nov 23, 2024

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

  • Meera Harris· Nov 19, 2024

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

  • Yash Thakker· Nov 15, 2024

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

  • Amina Rahman· Nov 15, 2024

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

  • Zara Thompson· Nov 3, 2024

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

  • Amelia Menon· Oct 22, 2024

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

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