2,784 件 ・ 関連度順
概要と使いどころ
Oracle-bound spec-review check that every structured Rollout prerequisite binds to an enforcement-type acceptance criterion. A prerequisite whose `enforced-by` is missing, dangling, or points at a non-enforcement AC is a BLOCKER (hard-blocks implementation entry via the checker fan-out verdict); a prerequisite left in free prose is ADVISORY. Read-only. Runs in the spec-review fan-out before implementation.
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。
Orchestrates `impeccable` for every design task inside a workflow phase. Captures intent in natural language, classifies it (design / development / copy), translates design intents into a sequence of impeccable subcommand invocations, runs them in main context with state persistence at `.claude/state/design/<slug>.json`, and returns a structured report. ALWAYS invokes `impeccable` under the hood for the underlying design move; never picks aesthetic direction itself; never writes product code directly. Per CLAUDE.md Article X.2, all design tasks in workflow phases route through this 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。
Use when a request needs external sources, current facts, listings, a supplied URL, papers, or library and API documentation. When using Firecrawl for the request, use firecrawl_search to find sources and firecrawl_scrape to read a supplied or discovered URL. Firecrawl also retrieves documents, collects structured data, and tracks website changes. Respect explicit source and tool preferences.
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。
An infographic skill for AI agents — works inside Claude Code, Codex, Hermes, OpenClaw, Cursor, and any agent host that loads skills. Create professional infographics as self-contained HTML files with PNG exports. Use when the user asks to create, design, generate, build, or make an infographic, data visualization, visual explainer, chart poster, cheatsheet, or pocket guide. Trigger phrases include "make this visual", "turn this into a graphic", "visualize this data", "infographic for X", "cheatsheet for X", or when the user provides structured data (tokenomics allocations, ecosystem/partner lists, timelines, comparisons, step-by-step processes) AND asks for a visual output. Covers tokenomics, ecosystem overviews, game overviews, cheatsheets, airdrop guides, comparisons, and process flows. Do NOT trigger for plain text answers, markdown tables, bulleted summaries, questions asking for advice or explanations about a topic (even when the topic is tokenomics, ecosystem, vesting, etc.), inline chat diagrams, or data processing with no visual output requested. The signal is "asks for a visual", not "mentions a keyword".
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。
Outlines: structured JSON/regex/Pydantic LLM 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。
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
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。
Refines ideas iteratively. Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.
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 safe, structured wellness intake that escalates red flags to professionals.
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。
Extract text, tables, and structured data from documents using prebuilt and custom models.
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。
Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON 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。
Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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。
Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.
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。
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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。
Transform rough ideas into solid designs through structured questioning
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。
Normalize and screen scattered foreign-trade leads into a conservative, structured lead pool that can flow into customer-intel research. Use when an operator has company names, websites, emails, or contact clues from search results and needs a standardized output with missing-field warnings, review flags, next-step suggestions, and customer-intel-ready JSON.
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。
Find the first batch of foreign-trade prospect companies from public web, LinkedIn, and user-authorized data sources such as customs exports, trade show lists, association directories, CRM/Excel files, or historical customer data, then output a conservative, structured candidate list ready for lead screening.
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。
Discover prospect companies from a structured OpenClaw search brief, including public search and user-authorized data sources, then output a conservative candidate list ready for lead screening.
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 planning or upgrading an internal skill/workflow for business operations, especially when turning a messy idea into a structured skill plan, migration plan, acceptance checklist, or packaging decision. Particularly useful for Obsidian knowledge base consolidation, workspace migration, content library cleanup, and integrating multiple workbenches into one operating system.
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。
OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.
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。
Generate conservative, editable English outreach drafts for foreign-trade sales from structured lead inputs and customer-intel signals. Use when an operator needs a first-touch or follow-up email draft that calls confirmed recent/customer-market signals from the customer-intel Skill, with subject options, review notes, and explicit reminders not to present unconfirmed facts as facts.
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。
OpenClaw-native SIEGER-aligned customer intelligence. Use a structured evidence bundle to produce a bilingual report with claim-level evidence, decision gates, industry-specific analysis, and proposed sales angles that require explicit approval before email drafting.
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。
Turns a plan phase into a PhaseSpec v1 and dispatches it to a specialist subagent via the Agent tool. Subagent reads files itself, returns a structured result, the result is logged into progress.md. Markdown is the shared state; no in-memory shared store across agents.
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 for search engine visibility and ranking. Use when asked to "improve SEO", "optimize for search", "fix meta tags", "add structured data", "sitemap optimization", or "search engine optimization".
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。