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Data & AI

Machine learning, LLMs, and data processing.

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llm-ai

4

session-management

Claude Codeセッションの状態管理、コンテキスト保持、会話履歴の効率的な運用を支援するスキル。長時間セッションでのコンテキスト消費最適化、セッション再開時の状態復元、マルチタスク切り替え時の状態保存・復元を提供する。 Anchors: • The Pragmatic Programmer (Hunt & Thomas) / 適用: 状態管理の原則 / 目的: 効率的なセッション運用 • Domain-Driven Design (Evans) / 適用: コンテキスト境界 / 目的: 適切な状態分離 • Clean Architecture (Martin) / 適用: 依存関係管理 / 目的: セッション間の独立性確保 Trigger: Use when managing Claude Code sessions, preserving context across interactions, or optimizing token usage in long conversations. session management, context preservation, token optimization, session state, conversation history

daishiman

data-ai

llm-ai

4

fine-tuning-data-generator

Generates comprehensive synthetic fine-tuning datasets in ChatML format (JSONL) for use with Unsloth, Axolotl, and similar training frameworks. Gathers requirements, creates datasets with diverse examples, validates quality, and provides framework integration guidance.

markpitt

data-ai

machine-learning

4

rlm

Recursive Language Model for processing large contexts (>50KB). Use for complex analysis tasks where token efficiency matters. Achieves 40% token savings by letting the LLM programmatically explore context via Query() and FINAL() patterns.

XiaoConstantine

data-ai

llm-ai

4

quality-enriched-prompting

[0,1]-enriched category implementation for gradient-based prompt quality optimization. Use when implementing quality-aware prompt systems, building enriched categorical structures for prompt evaluation, creating continuous optimization over prompt spaces, or applying Bradley's enriched category theory to language model quality scoring.

manutej

data-ai

llm-ai

4

flexus-bot-dev

Develop and test Flexus bots. Use when working with bot files (*_bot.py, *_prompts.py, *_install.py), flexus_client_kit, or kanban/scheduler systems.

smallcloudai

data-ai

machine-learning

4

multi-model

Multi-model battle for iterative (recursive) refinement. Rotates models every iteration and has other models judge/critique. Use when user asks to "battle models", "compare models", "multi-LLM", or wants iterative refinement across multiple OpenAI-compatible / Ollama models.

lollipopkit

data-ai

llm-ai

4

vector-search-alternatives

SQLiteプロジェクト向けのベクトル検索代替戦略スキル。 SQLite VSS、外部ベクトルDB、RAGパイプラインの実装を提供します。 Anchors: - The Pragmatic Programmer（Andrew Hunt）/ 適用: 実践的ソリューション選定 / 目的: 適材適所の技術選択 - Designing Data-Intensive Applications（Martin Kleppmann）/ 適用: データシステム設計 / 目的: スケーラブルなアーキテクチャ - Building LLM Apps（各種論文）/ 適用: RAGパターン / 目的: 効果的な情報検索 Trigger: Use when implementing vector search, building RAG systems, setting up SQLite VSS, or integrating external vector databases like Pinecone or Weaviate.

daishiman

data-ai

llm-ai

4

prompt-engineering

Comprehensive prompting techniques including chain-of-thought, few-shot, zero-shot, system prompts, persona design, and evaluation patterns

vamseeachanta

data-ai

llm-ai

4

prompt-engineering

Use this skill when writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

rohunvora

data-ai

llm-ai

4

skill-name

Explain what this skill does and when Claude should use it. Include trigger keywords and use cases.

thomasholknielsen

data-ai

llm-ai

4

self-review

タスク完了前のセルフレビュー。Gemini CLI + Claude subagentで多角的に検証。

K9i-0

data-ai

llm-ai

4

few-shot-learning-patterns

Few-Shot Learning（少数例示学習）のパターンとベストプラクティスを提供するスキル。効果的な例示の設計、構造化、配置により、AIの出力品質を大幅に向上させます。 • The Pragmatic Programmer / 適用: 例示パターン設計の品質基準 / 目的: 実践的改善と一貫性維持 • Few-Shot戦略 / 適用: 段階的複雑度設計と最適shot数決定 / 目的: AIの学習効率最大化 Trigger: Use when you need to design effective example patterns for AI learning, standardize output formats, or improve task performance beyond zero-shot capabilities. Keywords: few-shot, examples, prompting, output consistency, pattern learning.

daishiman

data-ai

llm-ai

4

log-searcher

Searches logs by content keywords, patterns, and filters with context extraction

$fractary$

fractary

data-ai

llm-ai

4

error-recovery

Provides structured error handling protocol for all agent operations. Ensures users receive clear error reports, troubleshooting guidance, and actionable next steps. Use when any tool operation fails, encounters an error, or produces unexpected results.

costiash

data-ai

llm-ai

4

check-skill-conflicts

This skill should be used when checking for naming conflicts between local skills (~/.claude/skills) and plugin-provided skills (~/.claude/plugins). Use to identify duplicate or similarly named skills that may cause inconsistent agent behavior.

plinde

data-ai

llm-ai

4

cli-detector

Detect installed AI coding CLIs and local model providers; outputs a cached JSON inventory for routing (/detect-clis).

cruzanstx

data-ai

llm-ai

4

vercel-ai-sdk-v6-engineering-playbook

Use when implementing AI features in TypeScript/Next.js with Vercel AI SDK v6 (generateText/streamText, tools, structured output, streaming route handlers). Enforces v6 APIs and best practices; prevents hallucinated options.

steveoon

data-ai

llm-ai

4

agentuity-cli-cloud-vector-search

Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations

agentuity

data-ai

llm-ai

4

postgres-semantic-search

PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank

laguagu

data-ai

llm-ai

4

detecting-ai-code

Use when auditing code for AI authorship, reviewing acquisitions/contractors, verifying academic integrity, or during code review - provides systematic tiered framework for detecting fully AI-generated AND AI-assisted code patterns with confidence scoring

galihcitta

data-ai

llm-ai

4

prompt-engineering-for-agents

エージェント向けプロンプトエンジニアリングを専門とするスキル。System Prompt設計、Few-Shot Examples、Role Prompting技術により、高品質なエージェント動作を実現します。 Anchors: • The Pragmatic Programmer (Andrew Hunt, David Thomas) / 適用: 手順設計と実践的改善 / 目的: 体系的なプロンプト設計 • Role Prompting patterns / 適用: ペルソナ設計と役割定義 / 目的: エージェント動作の最適化 • Few-Shot Learning / 適用: 効果的な例示選択 / 目的: 文脈構成の改善 • Prompt Engineering Guide (DAIR.AI) / 適用: プロンプト最適化技術 / 目的: 高品質な応答生成 Trigger: Use when designing system prompts for agents, optimizing agent behavior, implementing role prompting, creating few-shot examples, or improving agent prompt quality. Keywords: system prompt, agent prompting, role prompting, few-shot learning, prompt optimization, agent behavior

daishiman

data-ai

llm-ai

4

dynamic-prompt-registry

Dynamic prompt registry with unified categorical syntax. Supports @skills:discover(), @skills:compose(A⊗B), Reader monad for runtime lookup, and quality-tracked prompt libraries. Use for meta-prompts referencing sub-prompts, building prompt libraries, implementing deferred resolution, or composing templates dynamically.

manutej

data-ai

llm-ai

4

recursive-meta-prompting

Recursive Meta-Prompting (RMP) implementation with unified categorical syntax. Supports @mode:iterative, @quality: thresholds, >=> Kleisli composition, and comonadic context extraction. Use when implementing iterative prompt improvement, quality-gated generation loops, or applying categorical fixed-point semantics with convergence guarantees.

manutej

data-ai

llm-ai

4

discopy-nlp

DisCoPy categorical quantum NLP for string diagrams, monoidal categories, and compositional semantics in Python. Use when implementing compositional distributional semantics, building string diagram computations, working with monoidal category theory in code, creating quantum-inspired NLP models, or applying categorical semantics to natural language processing.

manutej

data-ai

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