1,308 件 ・ 関連度順
概要と使いどころ
Optimizes hot module replacement and fast refresh for development speed with Vite, Next.js, and webpack configurations. Use when users request "hot reload", "HMR optimization", "fast refresh", "dev server speed", or "development performance".
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。
Speed-optimized offensive checklist for rapid assessment: quick-win vulnerability patterns, fast recon shortcuts, automated scanner configurations, and triage shortcuts. Use for time-boxed assessments, CTF-speed engagements, or initial rapid surface mapping.
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。
Expert landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).
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。
Designing HTTP cache headers that work correctly across browsers, CDNs, and shared proxies — `Cache-Control` directives per RFC 9111, `stale-while-revalidate` and `stale-if-error` per RFC 5861, the Vary header for varying responses, and surrogate keys for tag-based purging. Grounded in IETF RFCs and Cloudflare/Fastly docs. NOT for service-worker or browser-only caching, Redis/memcached application caching, or edge-function compute.
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。
Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).
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。
macOS上のOpenClawをmainの最新版へ更新し、Gatewayとアプリの動作を確認します。CIの失敗修正や、公開を伴わないリリース検証も扱います。
PostgreSQLのテーブル設計、検索を速める索引、行単位のアクセス制御を見直し、遅いSQLの調査や接続設定の改善に役立つ実装例を参照するスキル。
作業の依存関係を整理し、独立した調査・実装・検証を並行して進めるためのスキルです。変更先の競合を避け、各作業の完了状況と確認結果を表にまとめます。
Next.js 16以降の開発で、起動や変更反映の遅さ、Turbopackとwebpackの使い分けを確認します。キャッシュやproxy.tsの命名も扱うスキルです。
Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
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。
APIキーなしでWeb・ニュース・画像・動画を検索し、地域や期間で絞った結果のタイトルやURLを取得する、通常の検索機能の代替にもなるスキル。
Fast vector similarity search at billion scale.
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。
文章をAIが扱う小さな単位に分け、大量のテキスト処理や独自の語彙学習を支援するスキル。分割結果と元の文章の位置対応、入力の長さ調整も扱います。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.
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。
Retro Quarterly Review presentation template in a bold blue + orange editorial language. Use when users ask for a high-impact quarterly review / roadmap deck with heavyweight slab headlines, clean cream paper sections, structured grids, and fast premium motion pacing (3 slides, each hold under 3s in video mode).
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine".
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation; Ed25519-sign the result for paper→live tamper evidence (ADR-126 Phase 4)
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。
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。