2万 件 ・ 関連度順
Expert TypeScript engineering with advanced patterns, architectural design, performance optimization, testing strategies, and production-grade code quality. Use when building scalable TypeScript applications, implementing complex type systems, optimizing performance, designing maintainable architectures, or when the user asks for TypeScript engineering excellence, advanced patterns, or professional-grade code practices.
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。
Guides Rust performance optimization. Use when profiling, benchmarking, reducing allocations, improving cache locality, choosing between rayon/async/threads, or applying SIMD/parallelism.
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。
Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use persistent mode only when user says "caveman mode", "talk like caveman", "use caveman", or invokes /caveman.
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。
Route Expo application, EAS, native module, styling, deployment, and upgrade work to focused Expo skills. Use when building or operating an Expo or Expo Router project.
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 when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
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。
Refactor code to improve quality while maintaining functionality. Use when you need to improve code structure, eliminate duplication, optimize performance, or enhance maintainability without changing 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。
Code review focused exclusively on over-engineering. Finds what to delete: reinvented standard library, unneeded dependencies, speculative abstractions, dead flexibility. One line per finding: location, what to cut, what replaces it. Use when the user says "review for over-engineering", "what can we delete", "is this over-engineered", "simplify review", or invokes /ponytail-review. Complements correctness-focused review, this one only hunts complexity.
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。
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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。
Iterate on tasks using debug instrumentation and browser tools. Use when you need to debug web applications, verify frontend behavior, or fix issues without asking the user to manually test.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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 distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
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 when you need to visually interact with a GUI — test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Install: pip install cua.
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。
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
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。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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。
Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.
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。
武將數值平衡調校 SKILL — 以雙軸演算法（maxStat + avg5）自動計算 rarityTier 與 characterCategory，並執行 EP 重算、適性校驗、因子分派、稀有度分布統計。USE FOR: 新武將入庫前的數值校準、全量武將的平衡盤點、稀有度自動分類。DO NOT USE FOR: 故事生成（用 general-story-writer）、資料爬取匯入（用 general-data-pipeline）。
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。
武將故事批次生成 SKILL — 根據武將的 GeneralConfig（含 characterCategory + rarityTier），生成 historicalAnecdote、bloodlineRumor、六格 storyStripCells 故事條。USE FOR: 填充 generals.json 中的 "???" 欄位、批次生成武將背景故事、統一故事風格。DO NOT USE FOR: 數值平衡調整（用 general-balance-tuner）、資料爬取（用 general-data-pipeline）。
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。
Pick a product archetype from a curated catalog before any UI gets built — the single biggest lever for not looking vibecoded. Routes the request to one of B2B-productivity, enterprise-trust, premium-consumer, playful-consumer, editorial, brutalist-distinctive, dev-tool, or marketing-landing, and returns a complete design philosophy: typography pair, color logic, motion curve, density, icon stance, reference sites, and a "feels like X" claim. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, classify a product visually, says "what should this look like", "make this feel like [Linear/Stripe/Apple/Duolingo/...]", "give me a design direction", "what's the right aesthetic for this", or when frontend-design routes here during archetype selection. Sub-skill of frontend-design.
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。
Detect and resolve naming collisions, overlapping intent triggers, and insufficient intent diversity across the skill library. Load when a new skill is created (called by universal-skill-creator), during improvement passes (called by improve-skills), or when the user asks to deconflict skills, check for naming collisions, audit trigger overlap, review intent coverage, find duplicate triggers, check skill names, or says "are any skills too similar", "which skills overlap", "deconflict the library", "check for confusing skill names", "intent audit".
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。
Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.
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。
Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".
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。
Mirror issues and status across issue trackers with stable external-ID mapping to avoid duplicates. Load when syncing GitHub and Linear issues, mirroring ticket status, or keeping two trackers in sync. Also triggers on "issue sync", "sync issues", "mirror issues to", "keep Linear and GitHub in sync", or bidirectional issue tracker updates. Requires API access on both sides — stops if credentials missing.
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。
Author pull requests with intent-separated commits, blast-radius disclosure, and verification evidence. Load when opening a PR, writing a PR description, separating refactor from feature commits, or summarizing a change set for review. Also triggers on "write the PR", "PR body", "intent-separated commits", "split refactor from feature", "pr summary", or after safe-change cycles complete. Extends git-workflow-and-versioning — does not replace it.
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。
Orchestrator for the learn-from suite - auto-detects source type (academic paper, GitHub repo, blog/web article, or in-conversation learnings) and routes to the correct sub-skill for credibility check, security scan, insight extraction, and application. Load when the user says "learn from", "learn from this", "extract insights from", "apply learnings from", "what can we learn from", or provides a URL, file path, or pasted content that should be ingested as knowledge. Single entry point for all learning workflows.
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。