Dev & Engineering web-designpresentationsimage-generationragknowledge-basevite

Garden Skills — A Curated Collection of Agent Skills for AI Coding Agents

Production-ready Agent Skills for Claude Code, Cursor, Codex, etc., covering web design, video presentations, image generation, and local knowledge retrieval.

FollowAgents review · FARS-2.1
Not recommended
48/ 100 5-point scale 2.4 / 5
1 2 3 4 5 6
1Trust11 / 29 · 1.9/5

Evidence shows: skills include user confirmation checkpoints (e.g., collaboration checkpoints in web-video-presentation and beautiful-article), but least privilege principle is not explicitly stated; data flow transparency is limited, no explanation of how data is processed or stored; sensitive data handling not mentioned; dependency security lacks dependency list or audit; external effects (e.g., network calls, file writes) not clearly specified; rollback mechanism not mentioned; source attribution only via README links and download links, not verified. Deductions: lack of explicit permission declarations, data flow explanations, and dependency security information.

2Reliability6 / 14 · 2.1/5

Evidence shows: README and skill descriptions are consistent across multiple language versions; skill structure is clear; dependency availability not verified, but external tools like pdftotext, pandas are mentioned; failure messages not evident in static files. Deductions: dependency availability and failure handling not explicit in source.

3Adaptability10 / 18 · 2.8/5

Evidence shows: explicitly targets AI coding agents like Claude Code, Cursor, Codex; scenarios cover broad range; capability boundaries defined via skill descriptions and README categories; trigger precision not explicit, but skills have clear SKILL.md; environment fit via multiple installation methods and compatibility notes. Deductions: trigger precision and specific environment fit details insufficient.

4Convention11 / 18 · 3.1/5

Evidence shows: clear information architecture with table of contents and categories; detailed installation instructions with multiple methods; naming stable, skill names consistent; examples and FAQ via case studies in README; known limitations not explicitly listed; license is MIT; versioning and changelog via GitHub Releases and tags; maintenance responsibility via contribution guide and PR welcome. Deductions: known limitations not explicit, version changelog not directly in source.

5Effectiveness7 / 13 · 2.7/5

Evidence shows: output usability demonstrated via cases and demos; marginal value via rich features and themes; cost-benefit not explicit, but free open-source license provided. Deductions: cost-benefit not quantified, output usability not tested.

6Verifiability3 / 8 · 1.9/5

Evidence shows: README claims have some link support, but no independent verification; cross-source corroboration limited, mainly self-documentation; facts and inferences not clearly separated. Deductions: lack of independent verification and fact-inference separation.

Evidence confidence: Low Reviewed Aug 09, 2026 Reviewed revision aaf9a82f5efd
Before you use it
  • Static review cannot verify actual runtime behavior; all scores are inferred from source and documentation.
  • Dependency security lacks dependency list or audit; check dependencies before use.
  • Data flow and sensitive data handling not explicit; be cautious with sensitive data.
  • External effects (e.g., network calls, file writes) not explicit; review skill behavior before use.
Review evidence [1][2][3][4][5]
See the full review method →

What does this agent do, and when should you use it?

Garden Skills is ConardLi's open-source collection of Agent Skills that follow Anthropic's SKILL.md specification and work with Claude Code, Cursor, Codex, and other AI coding agents. The repository contains five skills: web-design-engineer polishes AI-generated web artifacts into deliberate, visually memorable front-end work with a design system and 25 style recipes; web-video-presentation turns scripts and articles into record-ready 16:9 presentations with 23 themes and pluggable TTS; gpt-image-2 offers 79 structured prompt templates for image generation and editing across three runtime modes; kb-retriever provides progressive, source-cited retrieval from local knowledge directories; beautiful-article transforms URLs, PDFs, or notes into share-ready, beautifully typeset articles. Each skill is self-contained, with install options including npx CLI, Claude Code plugin market, pinned ZIPs, or git submodules, all released with versioned artifacts. Licensed under MIT, it serves as a reference for production-grade skill development.

Garden Skills provides loadable capability modules for AI coding agents via standard SKILL.md files. web-design-engineer conducts a design read, declares a design system, selects from 25 anchored style recipes (e.g., Linear, Aesop, Bloomberg), then builds and verifies React prototypes. web-video-presentation converts scripts into narration outlines, constructs a 1920×1080 Vite + React stage, and optionally synthesizes narration audio via MiniMax or OpenAI TTS. gpt-image-2 detects its execution mode (local Garden generation, host-native delegation, or advisor-only prompting) and applies structured templates to generate or edit images, saving artifacts under garden-gpt-image-2/. kb-retriever navigates local knowledge/ directories using hierarchical data_structure.md indexes, combines keyword search with windowed reads, and processes PDF/Excel via pdftotext/pdfplumber and pandas, returning answers with sources. beautiful-article runs an editorial loop from source extraction through plan, double-confirmation, build, review, and repair, outputting self-contained HTML (with optional PDF).

  1. A frontend developer wants to transform a ChatGPT-generated landing page mockup into a polished, production-ready design with a coherent design system.
  2. A tech writer needs to turn a detailed tutorial into a screen-recordable video presentation, choosing among 23 built-in themes.
  3. A product designer requires professional UI mockups, product visuals, or infographics, and wants structured prompt templates with reusable outputs.
  4. A team with an internal documentation knowledge base wants an AI assistant that can answer questions with cited sources from Markdown, PDF, and Excel files.
  5. An editor wants to convert a URL, PDF, or set of notes into a long-form, beautifully formatted article with options for ten article types and eleven authoring themes.

What are this agent's strengths and limitations?

Pros
  • Modular design adhering to the SKILL.md spec, enabling portability across multiple agents with minimal adaptation.
  • web-design-engineer provides 25 detailed style recipes with anti-cliché guidance, yielding distinctive, high-quality output beyond generic AI UI patterns.
  • web-video-presentation includes 23 themes and a pluggable TTS runner with two built-in providers, streamlining video production.
  • gpt-image-2 offers 79 prompt templates and three runtime modes, adapting to different execution environments.
  • Every skill ships with versioned ZIP artifacts and checksums, ensuring reproducible installations in CI pipelines.
Limitations
  • Dependency on third-party agent platforms (e.g., Claude Code) to support the Skills feature; compatibility with all agents is not fully guaranteed beyond the six listed.
  • Some features (e.g., TTS in web-video-presentation) require external services (MiniMax or OpenAI), incurring potential costs.
  • gpt-image-2's advisor-only mode (Mode C) generates prompts only, requiring manual execution for image creation.
  • beautiful-article is at version 0.1.0, indicating early-stage maturity and possible instability.
  • kb-retriever caps retrieval at 5 search rounds; for very complex knowledge bases this may limit depth of exploration.

How do you install or deploy this agent?

Install via npx skills CLI: npx skills add ConardLi/garden-skills to install all skills, or use -s to install a specific one (e.g., npx skills add ConardLi/garden-skills -s web-design-engineer). For Claude Code, subscribe to the plugin marketplace: /plugin marketplace add ConardLi/garden-skills, then /plugin install <pack>@garden-skills. For CI or air-gapped environments, download the pinned ZIP with SHA-256 checksum from GitHub Releases and unzip into your agent's skills directory (e.g., .claude/skills/). You can also manually copy the skill folder after git clone, or add the repo as a git submodule.

How do you use this agent?

After installation, the skill becomes available when the agent scans the workspace. For example, with web-design-engineer in Claude Code, simply ask: "Apply a Linear-style design to this landing page." For web-video-presentation, provide a script or article; the skill generates a presentation framework and optionally calls TTS (requires MiniMax or OpenAI API keys). gpt-image-2 in Garden mode saves outputs under garden-gpt-image-2/. For kb-retriever, maintain a knowledge/ directory locally and ask questions; the skill returns citations. For beautiful-article, supply a source URL or file and follow the interactive checkpoints.

How does this agent compare with similar options?

Related projects include Anthropic's official skills repository and community collections like travisvn/awesome-claude-skills and obra/superpowers. Garden Skills differentiates itself with specialized, production-oriented skills for web design, video presentation, and image generation, offering unique style recipe galleries and theme systems not found in generic skill lists.

FAQ

Are these skills free to use?
The repository is MIT-licensed and free, but some skills rely on third-party services (e.g., TTS APIs) which may have their own costs.
Do these skills work with non-Anthropic agents?
The README lists six agents (Claude Code, Cursor, Codex, etc.) as tested, but actual compatibility may vary by agent version; verify in your target environment.
How do I update installed skills?
If installed via npx skills, run npx skills update. For ZIP or submodule installs, re-download the latest release or pull the submodule.
Can I use these skills offline?
The skills are local files and can be used offline, but features that call external APIs (TTS, image generation) require network access.

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