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Llama Farm

Official

@llama-farm · United States of America

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20Public Repos
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30Published Skills

Local models, agents, and databases delivered anywhere.

Skills Distribution
DomainDeveloper To...System Architectur.. (40%)Frontend & UI Engi.. (30%)Specification & Li.. (30%)

Agent Skills by Llama Farm

Showing 30 vetted skills indexed across 2 GitHub repositories.

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demo-workflow

Coordinate artifact pipelines to generate product demo videos with timing data.

Official
Advanced
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2

openspec-new-change

Guide users through creating an OpenSpec change with the experimental artifact workflow.

Official
Intermediate
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openspec-archive-change

Archive completed OpenSpec changes with delta-spec comparison and metadata preservation.

Official
Advanced
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openspec-ff-change

Generate complete OpenSpec artifact sets with dependency-aware sequencing.

Official
Intermediate
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openspec-verify-change

Verify codebase implementations against delta specifications and change artifacts.

Official
Advanced
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openspec-explore

Facilitates early-stage project brainstorming and need analysis.

Official
Advanced
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openspec-bulk-archive-change

Batch-archive completed changes via the openspec CLI with conflict resolution.

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Advanced
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openspec-onboard

Guide users through a complete OpenSpec workflow cycle in a live codebase.

Official
Intermediate
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openspec-continue-change

Create the next OpenSpec change artifact based on workflow status.

Official
Intermediate
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openspec-sync-specs

Merge delta specs into main specs under openspec/specs.

Official
Advanced
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openspec-apply-change

Guide selection, status checks, and task execution for OpenSpec changes.

Official
Advanced
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835

rag-skills

Audit and optimize RAG pipelines with LlamaIndex, ChromaDB, and Celery best practices.

Official
Basic
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835

go-skills

Provides idiomatic Go patterns for CLI development with Cobra, Bubble Tea, and Lipgloss.

Official
Basic
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835

cli-skills

Design Go CLI applications with Cobra, Bubbletea, and Lipgloss patterns.

Official
Intermediate
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835

designer-skills

Provide design patterns and checklists for React UI with TailwindCSS, TanStack Query, and Radix UI.

Official
Advanced
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835

typescript-skills

Enforce strict typing and readonly props in React and Electron TypeScript codebases.

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Basic
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835

code-review

Analyze code diffs for security vulnerabilities, anti-patterns, and quality issues.

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Advanced
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835

reflect

Analyze user sessions and propose structured improvements to other skills.

Official
Basic
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835

server-skills

Enforce server-side pattern standardization for FastAPI, Celery, and Pydantic.

Official
Intermediate
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835

config-skills

Standardize and validate LlamaFarm configurations using Pydantic v2 and JSONSchema.

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Intermediate
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835

python-skills

Standardize Python development across LlamaFarm components with typing, testing, and security checks.

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Advanced
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835

electron-skills

Consolidate Electron architecture patterns for secure desktop app development.

Official
Advanced
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835

temp-files

Create and manage temporary files in the /tmp/claude/{sanitized-cwd} directory.

Official
Basic
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commit-push-pr

Stage changes, create conventional commits, push to origin, and open a PR via gh CLI.

Official
Advanced

Frequently Asked Questions About Llama Farm

FAQPage Schema
What specific engineering tasks are enabled by these patterns?▼

These patterns enable standardized Go development, React 18 UI architecture, and rigorous OpenSpec delta-specification management. They facilitate consistent configuration validation, secure desktop development via Electron, and structured code review processes to ensure high-quality, maintainable service deployments.

Which technical personas benefit most from these standards?▼

Full-stack engineers, systems architects, and technical leads working in monorepo environments benefit from these standardized patterns. They are designed for developers requiring consistent, type-safe implementations across Go services, React interfaces, and complex specification-driven development cycles.

What are the primary prerequisites for implementing these standards?▼

Implementation requires a foundational environment supporting Go, TypeScript, and Python 3.10+. Users must have access to standard development environments, including Git for worktree management and local runtime support for FastAPI and PyTorch-based inference services.