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概要と使いどころ
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Creates VS Code custom agent files (.agent.md) for specialized AI personas with tools, instructions, and handoffs. Use when scaffolding new custom agents, configuring agent workflows, or setting up agent-to-agent handoffs.
日本語の概要は準備中です。原文の説明を表示しています。
Automate Supabase database queries, table management, project administration, storage, edge functions, and SQL execution via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
日本語の概要は準備中です。原文の説明を表示しています。
Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines
日本語の概要は準備中です。原文の説明を表示しています。
Blockly visual programming context for the Megahub project. Use when adding, modifying, or debugging custom Blockly blocks, the Lua code generator, the toolbox, block colors, or the BLOCKS.md documentation generation script.
日本語の概要は準備中です。原文の説明を表示しています。
Code formatting and linting context for the Megahub project. Use when writing or reviewing code in C++ or JavaScript, or when running format/lint checks. Describes tool commands, style conventions, and when to apply them.
日本語の概要は準備中です。原文の説明を表示しています。
Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.
日本語の概要は準備中です。原文の説明を表示しています。
MCP server documentation conventions for high-quality generated skills. Use when writing tool descriptions, `_meta` annotations, or input schemas for MCP extraction.
日本語の概要は準備中です。原文の説明を表示しています。
Bootstrap an AI-agent skill from a TypeScript codebase by running the deterministic skillit generate/audit loop and enriching repo source (JSDoc, README, config-type properties, MCP tool annotations, examples, package.json) until the skill reaches its grade target. Use for cli, typedoc, config, or mcp (build-mode) projects; never edit SKILL.md/references directly.
日本語の概要は準備中です。原文の説明を表示しています。
Create an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".
日本語の概要は準備中です。原文の説明を表示しています。
Complete Flutter SDK installation, IDE configuration, and troubleshooting guide for Linux, macOS, and Windows. Use for installing Flutter SDK from scratch, configuring PATH environment variables across platforms, setting up VS Code/Android Studio/IntelliJ IDEA with Flutter plugins, resolving 'flutter command not found' errors, fixing flutter doctor warnings and errors, troubleshooting Android SDK/NDK installation issues, configuring Xcode and CocoaPods on macOS, installing Visual Studio C++ build tools on Windows, resolving PATH conflicts and SDK path issues, fixing network/proxy setup problems, debugging IDE plugin activation, configuring emulators and simulators, handling platform-specific build errors, resolving dependency conflicts, fixing certificate and signing issues, troubleshooting hot reload failures, and verifying successful installation with flutter doctor diagnostics.
日本語の概要は準備中です。原文の説明を表示しています。
Implements data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose active BIRD daemon incidents across config, runtime, environment, sockets, logs, source, and tooling. Use when BIRD fails to start or reload, crashes, loses sessions/routes, behaves differently after lint passes, or when birdcc and bird -p disagree. Collect read-only evidence first, protect routing secrets, and route isolated config edits to bird-agent or pure implementation research to bird-source-explorer.
日本語の概要は準備中です。原文の説明を表示しています。
Write, review, discover, lint, format, and validate BIRD 2/3 routing-daemon projects with the @birdcc toolchain. Use for bird.conf, bird2.conf, bird3.conf, bird6.conf, .bird/.bird2/.bird3 files, bird.config.json, birdcc.config.json, include graphs, BIRD filters and protocols, birdcc init/lint/fmt/lsp, or bird -p checks. Treat BIRD 1 as legacy and verify it with the actual BIRD 1 binary. Route editor and CLI installation to birdcc-installer, GitHub Actions to birdcc-cicd, runtime incidents to bird-troubleshooting, and implementation archaeology to bird-source-explorer.
日本語の概要は準備中です。原文の説明を表示しています。
Inspect, debug, and inject custom code into a running Steam client — desktop (macOS, Linux, Windows) or Steam Deck over the network — through its Chromium Embedded Framework (CEF) Chrome DevTools Protocol endpoint. Use when developing Steam UI plugins, mods, themes, or integrations — injecting CSS/JS to try a change, locating webpack modules and obfuscated class names, reading React or SteamUIStore state, inspecting Big Picture / Quick Access Menu windows, screenshotting what actually rendered, streaming console logs, or diagnosing runtime errors in Steam's UI. The same commands run against either device, or both at once. Injection is per-session and for development only. Not for building a plugin loader or making changes persist across restarts, Steamworks game SDK work, game development, or scraping the public Steam web store.
日本語の概要は準備中です。原文の説明を表示しています。
Use OpenAI Codex CLI as a peer collaboration partner — same standing as Claude, not a one-shot tool. Bring Codex in EARLY (ideation, framing decisions, stuck moments), not only for late peer-review. Hand it strategic context (target, research, draft, reasoning), iterate conversationally over ~3-8 turns, converge before surfacing to the human. Invoke when the user says "check / iterate / ideate with Codex" or "ask Codex" — and proactively when stuck between two reasonable framings, pressure-testing a load-bearing claim or structure, or about to ask the human to pick between options two agents could resolve.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill whenever the user asks to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.
日本語の概要は準備中です。原文の説明を表示しています。
Hand work down the chain master session, project orchestrator, package orchestrator. Use when the human asks how to split work across levels, which channel connects them, or what each level may do. A convention mapped onto existing mechanisms, not a new tool. GO is never inherited.
日本語の概要は準備中です。原文の説明を表示しています。
シェルスクリプト品質のためのShellCheck静的解析の設定と使用をマスター。リンティングインフラの設定、コード問題の修正、またはスクリプトの移植性の確保時に使用してください。
エラーバジェットとアラートを用いたサービスレベルインジケーター（SLI）とサービスレベル目標（SLO）を定義・実装します。信頼性ターゲットの確立、SREプラクティスの実装、サービスパフォーマンスの測定時に使用します。
堅牢でPCI準拠の決済フロー（チェックアウト、サブスクリプション、Webhook）を含むStripe決済処理を実装します。Stripe決済の統合、サブスクリプションシステムの構築、安全なチェックアウトフローの実装時に使用します。
ベクトルデータベースとセマンティック検索を使用してLLMアプリケーションのためのRetrieval-Augmented Generation（RAG）システムを構築します。知識に基づいたAI、ドキュメントQ&Aシステムの構築、またはLLMと外部ナレッジベースの統合時に使用します。
エクスプレスチェックアウト、サブスクリプション、返金管理をサポートするPayPal決済処理を統合します。PayPal決済の実装、オンライン取引の処理、Eコマースチェックアウトフローの構築時に使用します。