Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: atomic-swaps

英語版ディレクトリ

Detect non-atomic interactions within DB transactions

1.1K
Stars
77/100
信頼
カテゴリ: coding-agents監査

A full-featured CRM built with React, shadcn/ui, and Supabase.

1.1K
Stars
80/100
信頼
カテゴリ: growth-marketing監査

Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibility is claimed, or multiple similar APIs may express one demand.

51K
Stars
78/100
信頼
カテゴリ: research監査

Opinionated macOS development environment automation that packages agent skills, rules, and CLI tooling for AI coding agents like Claude Code and Codex.

124
Stars
73/100
信頼
カテゴリ: coding-agents監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査

Create branded architecture, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, Venn, pyramid/funnel, treemap, bar, line, Gantt and scatter charts, high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as standalone HTML/SVG/PNG. Redraw .drawio/.drawio.png/.drawio.svg or Mermaid .mmd sources at a chosen size/detail; onboard brand tokens from a website; add semantic patterns, callouts, accessible motion, or sketchy/hand-drawn styling.

24K
Stars
75/100
信頼
カテゴリ: security監査

An advanced calendar card for Home Assistant Lovelace.

648
Stars
71/100
信頼
カテゴリ: productivity-automation監査

A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.

345
Stars
66/100
信頼
カテゴリ: finance監査

Golang concurrency patterns. Use when writing or reviewing concurrent Go code involving goroutines, channels, select, locks, sync primitives, errgroup, singleflight, worker pools, or fan-out/fan-in pipelines. Also triggers when you detect goroutine leaks, race conditions, channel ownership issues, or need to choose between channels and mutexes.

3.0K
Stars
74/100
信頼
カテゴリ: coding-agents監査

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

124
Stars
69/100
信頼
カテゴリ: rag-knowledge監査

🧠 AI Agent skills for LLMs and AI Agents - Claude, Codex, Cursor etc.

14
Stars
64/100
信頼
カテゴリ: utility監査

A Claude Code plugin that provides outline-driven development with diagram-first engineering, surgical code editing, and atomic commits.

34
Stars
69/100
信頼
カテゴリ: coding-agents監査