majiayu000/claude-skill-registry - 12페이지

load-pickle-persona

Activates the Pickle Rick persona. Use this ONLY when the user explicitly requests to start the "Pickle Rick" mode or loop. DO NOT use this for general greetings (e.g., "hi") normal assistance.

원문 언어: 영어

understand-intent

Detect user's intent from their message and activate the right workflow silently.

원문 언어: 영어

user-input-protocol

Structured checkpoint format for requesting human input. When an agent needs a decision, it must stop, present context, show options, and wait. Activate when delegating to subagents, running background tasks, or hitting any decision point that requires human…

원문 언어: 영어

using-loaded-knowledge

MANDATORY protocol enforcing knowledge check before EVERY response - prevents explaining systems without reading docs, claiming without verification, and ignoring auto-loaded context

원문 언어: 영어

validate-agent

Validation agent that validates plan tech choices against current best practices

원문 언어: 영어

vault-guide

Interactive onboarding agent for content marketing associate candidates and vault orientation for Charlie. Walks candidates through the OpenEd content system, discovers their strengths, guides them to produce real output, and helps them write a contract…

원문 언어: 영어

vc-phase-loop

Ultimate autonomous execution engine. Plan → Execute → Test → Never Stop Until Done. Use when the user wants hands-off completion with no questions, automatic recovery from any failure, and proven results.

원문 언어: 영어

vcf

Short alias for vc-phase-loop. Use when the user invokes "vcf:" or "use vcf:" and wants maximum hands-off completion.

원문 언어: 영어

voltagent-development

Create and configure VoltAgent AI agents with tools, memory, hooks, and sub-agents. Use when building AI agents, adding agent tools, configuring VoltAgent memory, creating multi-agent workflows, or debugging VoltAgent integrations.

원문 언어: 영어

worker-focused-execution

Focused task execution for persistent team workers. Use when implementing features, following scope constraints, using TDD red-green-refactor, validating with browser/API/unit tests, or participating in voting consensus. Triggers on worker execution, feature…

원문 언어: 영어

worker

Execute beads autonomously within a track. Handles bead-to-bead context persistence via Agent Mail, uses preferred tools from AGENTS.md, and reports progress to orchestrator.

원문 언어: 영어

world-agent

Train and deploy generative UI agents using behavioral programming constraints, A2A protocol for agent interoperability, and pattern registry for composition. Use when working with agent training, trajectory generation, reward computation, A2A integration, or…

원문 언어: 영어

wow-applying-fixer-workflow

Complete fixer agent workflow including report discovery, validation strategy (HIGH/MEDIUM/FALSE_POSITIVE confidence), fix application, and fix report generation. Use when implementing or updating fixer agents.

원문 언어: 영어

wow-executing-checker-workflow

Step-by-step validation workflow for checker agents - initialize report, discover content, validate progressively, finalize with summary. Use when implementing or updating checker agents.

원문 언어: 영어

write-subagent

Use when the user wants to create a new Claude Code subagent. Guides subagent creation with focused prompts and constrained tool access.

원문 언어: 영어

llmops-evaluator

Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions

원문 언어: 영어

llmops-fine-tuner

Create your llmops-fine-tuner skill from Unsloth documentation before learning fine-tuning theory

원문 언어: 영어

llmops-decision-framework

"This skill should be used when evaluating whether fine-tuning is appropriate for an LLMOps project. Use when analyzing domain use cases, comparing training methods, calculating ROI, or deciding between API-based and self-hosted solutions."

원문 언어: 영어

persona-tuner

"This skill should be used when fine-tuning for personality, brand voice, or communication style. Use when the goal is HOW a model communicates rather than WHAT it knows."

원문 언어: 영어

agentic-tuning

"This skill should be used when fine-tuning language models for reliable tool-calling and structured output generation. Use when creating agent backends, training for function calling, or building models that need 95%+ JSON accuracy."

원문 언어: 영어

model-merging

"This skill should be used when combining multiple LoRA adapters or fine-tuned models into a single unified model. Use when students have trained separate adapters for different capabilities (persona, tool-calling, domain knowledge) and need to merge them…

원문 언어: 영어

model-alignment

Create your model-alignment skill from TRL documentation before learning DPO theory

원문 언어: 영어

model-serving

Create your model-serving skill from Ollama documentation before learning deployment theory

원문 언어: 영어

llmops-fine-tuner

Consolidate all Part 8 skills into a production-ready llmops-fine-tuner skill

원문 언어: 영어

adhd

ADHD-optimized code explainer. Generates layered, scannable explanations with hooks, maps, and progress markers.

원문 언어: 영어

adk-rag-agent

Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.

원문 언어: 영어

advanced-elicitation

Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.

원문 언어: 영어

advanced-tool-use

Context-efficient tool management via search, deferred loading, and programmatic calling

원문 언어: 영어

adversarial-spec

Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.

원문 언어: 영어

agent-ai-engineer

Expert AI engineer specializing in AI system design, model implementation, and production deployment. Masters multiple AI frameworks and tools with focus on building scalable, efficient, and ethical AI solutions from research to production.

원문 언어: 영어

agents

Imported skill agents from vercel

원문 언어: 영어

agent-cost-optimizer

Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs,…

원문 언어: 영어

agent-evals

Design and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts that apply to any SDK.

원문 언어: 영어

agent-llm-architect

Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.

원문 언어: 영어

agent-o-rama

Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems

원문 언어: 영어

agent-sdk-development

Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools,…

원문 언어: 영어

agent-selecting-models

Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.

원문 언어: 영어

example-agent

Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.

원문 언어: 영어

agent-tools

Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating…

원문 언어: 영어

agenta

LLM prompt management and evaluation platform. Version prompts, run A/B tests, evaluate with metrics, and deploy with confidence using Agenta's self-hosted solution.

원문 언어: 영어