1,218 件 ・ 関連度順
概要と使いどころ
Handles PDF/UA accessibility, real redaction, metadata scrubbing, signing, and extraction. Use when redacting or signing PDFs; for plain create/merge prefer the official pdf 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。
Builds docxtpl templates, DOCX extraction for RAG, and EAA accessibility checks. Use when templating Word or extracting tables; plain edits and redlines: official docx 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。
Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.
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 for every task that plans, writes, edits, debugs, tests, refactors, reviews, or maintains code. Apply to professional repositories, scripts, apps, services, prototypes, beginner work, vibe coding, and bounded code changes inside large systems. Choose the fewest clear lines, files, paths, states, and abstractions that correctly implement the requested behavior. Do not invent fallbacks, compatibility paths, defensive layers, or future scale without evidence. Do not use for ordinary conversation, emotional support, prose, translation, summarization, social-media content, or general research or technical discussion that is not part of planning, changing, or reviewing code. Never weaken real security, integrity, concurrency, compatibility, privacy, accessibility, legal, or regulatory requirements.
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。
Refactoring code: variable naming, function extraction, magic number constants, dead code removal. Does not change behavior. Not for bugs/security (Judge), tests (Radar), or features (Builder).
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。
Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.
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。
Verifying YAGNI, cutting scope, and proposing complexity reductions. A 'subtraction' agent questioning the justification for every feature, dependency, doc, and config. Does not write code.
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。
Web3 Infrastructure Architect: Indexers, RPC, wallets, account abstraction, key management and off-chain services.
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 Concrete-Pictorial-Abstract learning sequence for a mathematical concept using manipulatives. Use when teaching maths through Singapore method or when students struggle with abstraction.
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。
Plan how to build with You.com. Use when adding web search, content extraction, or research to an app or agent: finds the right You.com API, MCP server, SDK, or docs path (for example Vercel AI SDK, LangChain, Python SDK, or REST) through the `you-discover` tool and Docs MCP.
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。
Retrieve detailed Amazon product information by ASIN, including title, Item Highlights when extractable from raw HTML, images, bullet points, specifications, A+ content, pricing, ratings & reviews, variants, and more. Trigger when users mention Amazon product details, ASIN lookup, Item Highlights, product page data, listing analysis, bullet point extraction, product image retrieval, variant inspection, competitor listing research, price lookup, review breakdown, product specification query, or similar terms. Even if the user does not explicitly say "product details," trigger this skill whenever the request involves retrieving structured data from Amazon product pages via ASIN.
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 and rewrite Turkish so it reads as native Turkish rather than translated English. Use for Turkish marketing copy, product and UI text, localization, documentation, support, legal or security prose, and reviews of AI-written Turkish. Detect translationese, copied clause structure, nominalization, wrong case and collocations, generic helper verbs, SaaS and sales calques, jargon leakage, forced slogans, corporate abstraction, cross-sector template residue, unsupported operational claims, and page-level repetition.
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。
Use for Korean (Hangul) fan translations of retro console or PC games, including ROM or disc analysis, text-engine reverse engineering, Hangul fonts and custom encodings, script extraction and reinsertion, code hooks, reproducible product builds, and emulator verification. Apply it to new investigations and follow-up work in existing Korean-patch projects. 레트로 게임 한글화·한글패치·한글 패치·ROM 번역의 신규 조사와 기존 프로젝트 후속 작업에 사용한다.
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 when a user needs to inspect, score, repair, or regression-test academic paper formatting in DOCX files, especially thesis, journal, or conference documents with a separate format specification. The skill guides Codex through local-first document handling, rule extraction, formatting repair, report review, and contribution-safe validation.
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。
Startup Launch Day Playbook — Hour-by-hour execution checklist for your big launch day. Covers timezone timing, channel sequencing, real-time crisis management, and post-launch momentum. By @WeiYipei.
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。
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
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 mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.
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。
Repo-wide structural-maintainability review — code-judo restructurings, 1k-line file guard, anti-spaghetti branching, canonical-layer enforcement, anti-magic abstractions, explicit type/boundary contracts.
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 writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).
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。
Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.
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 high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.
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。