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© 2026 Claude Skills·Learn more about Agent Skills

Claude Skills

aka. Agent Skills

Discover skills for AI coding agents. Works with Claude Code, OpenAI Codex, Gemini CLI, Cursor, and more.

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1875 MarketplacesSpecification
13225 verified skills
#SkillStars
2721
#2721

providing-feedback

verified

Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.

ancoleman/ai-design-components
159
2722
#2722

prompt-engineering

verified

Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).

ancoleman/ai-design-components
159
2723
#2723

platform-engineering

verified

Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.

ancoleman/ai-design-components
159
2724
#2724

operating-kubernetes

verified

Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.

ancoleman/ai-design-components
159
2725
#2725

load-balancing-patterns

verified

When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.

ancoleman/ai-design-components
159
2726
#2726

ingesting-data

verified

Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.

ancoleman/ai-design-components
159
2727
#2727

implementing-realtime-sync

verified

Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.

ancoleman/ai-design-components
159
2728
#2728

generating-documentation

verified

Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.

ancoleman/ai-design-components
159
2729
#2729

evaluating-llms

verified

Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

ancoleman/ai-design-components
159
2730
#2730

embedding-optimization

verified

Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

ancoleman/ai-design-components
159
2731
#2731

spec-workflow

verified
development

This skill should be used when the user asks to "build a feature", "create a spec", "start spec-driven development", "run research phase", "generate requirements", "create design", "plan tasks", "implement spec", "check spec status", or needs guidance on the spec-driven development workflow.

tzachbon/smart-ralph
157
2732
#2732

reality-verification

verified
development

This skill should be used when the user asks to "verify a fix", "reproduce failure", "diagnose issue", "check BEFORE/AFTER state", "VF task", "reality check", or needs guidance on verifying fixes by reproducing failures before and after implementation.

tzachbon/smart-ralph
157
2733
#2733

delegation-principle

verified
development

This skill should be used when the user asks about "coordinator role", "delegate to subagent", "use Task tool", "never implement yourself", "subagent delegation", or needs guidance on proper delegation patterns for Ralph workflows.

tzachbon/smart-ralph
157
2734
#2734

interview-framework

verified
development

Standard single-question adaptive interview loop used across all spec phases

tzachbon/smart-ralph
157
2735
#2735

delegation-principle

verified
development

Core principle that the main agent is a coordinator, not an implementer. All work must be delegated to subagents.

tzachbon/smart-ralph
157
2736
#2736

smart-ralph

verified
development

This skill should be used when the user asks about "ralph arguments", "quick mode", "commit spec", "max iterations", "ralph state file", "execution modes", "ralph loop integration", or needs guidance on common Ralph plugin arguments and state management patterns.

tzachbon/smart-ralph
157
2737
#2737

communication-style

verified
development

This skill should be used when the user asks about "output formatting", "concise responses", "Matt Pocock planning style", "scannable output", "action steps format", or needs guidance on communication and output formatting rules for Ralph agents.

tzachbon/smart-ralph
157
2738
#2738

speckit-workflow

verified
development

Comprehensive understanding of the spec-kit methodology. Constitution-driven feature development with specify, plan, tasks, and implement phases.

tzachbon/smart-ralph
157
2739
#2739

smart-ralph

verified
development

Core Smart Ralph skill defining common arguments, execution modes, and shared behaviors across all Ralph plugins.

tzachbon/smart-ralph
157
2740
#2740

communication-style

verified
development

Output rules for all agents - concise, scannable, actionable. Based on Matt Pocock's planning principles.

tzachbon/smart-ralph
157
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