llm-ai
4
summarize-session
Compact the conversation context by summarizing what was accomplished and updating CLAUDE.md with any learnings. Use when context is getting long or when transitioning between work sessions.
andersnygaard
data-ai
sorting
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llm-ai
4
llm4s-scala
LLM4S Scala functional LLM interfaces with Effect system integration. Use when building LLM applications in Scala with ZIO or Cats Effect, implementing type-safe AI pipelines with functional error handling, creating composable prompt systems in Scala, or leveraging Scala's type system for robust AI applications.
manutej
data-ai
llm-ai
4
skill-writer
Create and manage reusable agent skills with proper structure and frontmatter
VenTheZone
data-ai
llm-ai
4
skill-creator
Creates new AI agent skills following the Agent Skills spec. Trigger: When user asks to create a new skill, add agent instructions, or document patterns for AI.
Rzyfront
data-ai
llm-ai
4
agent-developing-agents
AI agent development standards including frontmatter structure, naming conventions, tool access patterns, model selection, and Bash-only file operations for .claude/ folders
wahidyankf
data-ai
llm-ai
4
implementation
Execute implementation tasks with different approaches (single, dual-agent, plan-based).
Yuvasee
data-ai
llm-ai
4
ec-remember
Store important project context in Engram Cogitator's persistent memory. Use after making architectural decisions, discovering codebase gotchas or workarounds, identifying recurring patterns, or when user states preferences. Triggers: "remember this", "store this", "save for later", "don't forget", architectural decision, learned something, discovered a gotcha.
MereWhiplash
data-ai
llm-ai
4
role-prompting
AIエージェント・システムロール・ペルソナのための役割プロンプト設計スキル。 責務分離、専門家思考様式の適用、効果的なプロンプト構造化の指針を提供する。 Anchors: • The Pragmatic Programmer (Andrew Hunt, David Thomas) / 適用: DRY原則・責務分離 / 目的: ロール設計の品質向上 • Domain-Driven Design (Eric Evans) / 適用: ユビキタス言語・境界づけられたコンテキスト / 目的: 責務境界の明確化 • Thinking, Fast and Slow (Daniel Kahneman) / 適用: 専門家思考様式の理解 / 目的: 適切な思考モード設計 Trigger: Use when designing AI agent roles, system prompts, persona definitions, or separating responsibilities between agents. role prompting, persona design, agent role, system prompt, responsibility separation, ロール設計, ペルソナ
daishiman
data-ai
llm-ai
4
multi-agent-orchestrator
Orchestrate parallel CLI agents (Claude Code, Codex, Gemini) for competitive evaluation. Use when user says "run multi-agent", "compare agents", "launch competitive evaluation", "use parallel agents", or complex tasks (>7/10) where multiple approaches exist and best solution matters.
tekliner
data-ai
llm-ai
4
skill-generator
Claude Codeのログ分析からSkillを自動生成するスキル。操作パターンを検出し、繰り返し作業をSkill化して自動化を支援する。
ushironoko
data-ai
llm-ai
4
add-provider
Add a new AI provider or model for recipe generation. Use when adding support for a new LLM provider (Anthropic, Google, etc.) or adding models to an existing provider.
iteratetograceness
data-ai
llm-ai
4
subagent-batching
Patterns for launching and managing parallel subagents efficiently.
JacobPEvans
data-ai
llm-ai
4
llamacpp
Complete llama.cpp C/C++ API reference covering model loading, inference, text generation, embeddings, chat, tokenization, sampling, batching, KV cache, LoRA adapters, and state management. Triggers on: llama.cpp questions, LLM inference code, GGUF models, local AI/ML inference, C/C++ LLM integration, "how do I use llama.cpp", API function lookups, implementation questions, troubleshooting llama.cpp issues, and any llama-cpp or ggerganov/llama.cpp mentions.
datathings
data-ai
llm-ai
4
prompt-engineering
Designs and optimizes prompts for large language models to achieve better, more consistent outputs. Trigger keywords: prompt, LLM, GPT, Claude, prompt engineering, AI prompts, few-shot, chain of thought.
cosmix
data-ai
llm-ai
4
prompt-testing-evaluation
プロンプトのテスト、評価、反復改善を専門とするスキル。A/Bテスト、評価メトリクス、自動化されたプロンプト品質保証により、本番環境で信頼性の高いプロンプトを実現します。 Anchors: • Test-Driven Development: By Example (Kent Beck) / 適用: Red-Green-Refactorサイクル / 目的: 反復的な品質改善 • LLM-as-a-Judge pattern / 適用: 自動評価とスコアリング / 目的: スケーラブルな品質評価 • A/B Testing for AI Systems / 適用: プロンプト比較実験設計 / 目的: データドリブンな改善 Trigger: Use when testing prompts, evaluating prompt quality, running A/B tests on prompts, implementing automated prompt evaluation, or establishing continuous prompt improvement cycles. Keywords: prompt testing, A/B testing, evaluation metrics, LLM-as-a-judge, prompt quality, automated evaluation, regression testing
daishiman
data-ai
llm-ai
4
prompt-benchmark
Systematic prompt evaluation framework with MATH, GSM8K, and Game of 24 benchmarks. Use when evaluating prompt effectiveness on standard benchmarks, comparing meta-prompting strategies quantitatively, measuring prompt quality improvements, or validating categorical prompt optimizations against ground truth datasets.
manutej
data-ai
llm-ai
4
acm-config
Interactively configures Claudikins Automatic Context Manager settings by asking the user questions about trigger threshold, snooze duration, summary length, and other preferences. Use when the user wants to customize Claudikins Automatic Context Manager behavior or runs '/acm:config'.
elb-pr
data-ai
llm-ai
4
agent-tools
Reference for configuring tool permissions when launching Claude Code agents. Use when setting up --allowedTools flags, restricting file access, or configuring agent permissions.
jpoutrin
data-ai
llm-ai
4
langgraph-development
专业的LangGraph AI应用开发技能，提供从概念到生产的完整开发指导。基于Context7最新调研，涵盖StateGraph设计、多代理协作、RAG系统实现、生产部署等核心场景。使用此技能构建有状态、多参与者、长期运行的AI代理应用。
hhhh124hhhh
data-ai
llm-ai
4
chain-of-thought-reasoning
推論パターンを選択し、根拠サマリーを短く提示するためのプロンプト設計スキル。 自己一貫性の比較、推論パターンの適用、説明の明確化を通じて、再現性の高い結論を導く。 Anchors: • The Pragmatic Programmer / 適用: 手順設計 / 目的: 実践的な整理 • Reasoning and Logic / 適用: 推論パターン / 目的: 一貫性のある説明 • Self-Consistency (Wang et al.) / 適用: 複数案比較 / 目的: 精度向上 Trigger: Use when selecting reasoning patterns, designing prompts for structured explanations, or comparing multiple solution paths for higher confidence.
daishiman
data-ai