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概要と使いどころ
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。
Posts content and articles to X (Twitter). Supports regular posts with images/videos and X Articles (long-form Markdown). Uses real Chrome with CDP to bypass anti-automation. Use when user asks to "post to X", "tweet", "publish to Twitter", or "share on X".
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 knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping
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 SEO + GEO (Generative Engine Optimization) system for SaaS companies wanting to rank on both Google and AI search engines (ChatGPT, Perplexity, Gemini, Claude). Use this skill whenever the user asks about SEO strategy, keyword research, content planning, writing SEO-optimized articles, auditing their search performance, creating a content calendar, analyzing competitors, or optimizing for AI search visibility. Trigger on: "seo", "keyword research", "content calendar", "rank on google", "search traffic", "write an article", "blog post", "serp", "backlinks", "competitor analysis", "content cluster", "seo audit", "geo optimization", "ai search", "search console", "organic traffic", "content strategy", "publish article", "seo setup", "ranking", "impressions", "ctr", "meta description", "schema markup", "faq schema". Also trigger when a user wants to set up their SaaS blog SEO from scratch, analyze their current rankings, or create a publishing workflow.
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。
Quantitative trading expertise for DeFi and crypto derivatives. Use when building trading strategies, signals, risk management. Triggers on signal, backtest, alpha, sharpe, volatility, correlation, position size, risk.
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 a startup to set a single quantified traction goal and define the critical path of milestones to reach it. Use whenever a founder needs to prioritize activities, set growth goals, define milestones, decide what NOT to work on, plan quarterly/yearly execution, cascade goals to teams, escape the 'too many things to do' trap, or apply a binary on-path/off-path filter to proposed work. Activates on phrases like 'traction goal', 'critical path', 'what should we focus on', 'too many priorities', 'prioritization', 'milestones', 'quarterly planning', 'yearly goals', 'OKRs', 'where should I spend my time', 'what NOT to do', 'company planning', 'goal setting', 'DuckDuckGo', 'roadmap'.
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。
以命理场景为主的八字排盘、黄历查询与大运/流年/流月/流日/流时区间查询技能。用于用户请求“算八字”“四柱排盘”“阳历/农历转八字”“查黄历/宜忌”“查未来10年流年”“查下个月流日/流时”等场景；关键词包括：八字、四柱、命理、大运、流年、流月、流日、流时、时辰、阳历转八字、农历转八字、黄历、宜忌、干支日期。真太阳时换算属于辅助能力，仅在需要校时定盘时使用。 / Bazi-first skill for charting, Chinese almanac lookup, and range queries of decade/annual/monthly/daily/hourly luck cycles. Use for requests like “calculate my Bazi”, “convert solar/lunar datetime to Bazi”, “check huangli”, “query next 10 years”, and “check next month daily/hourly cycles”. Keywords focus on Bazi/Four Pillars and fortune-cycle analysis; true-solar-time conversion is an auxiliary step only when time correction is 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。
Maxim Design System Intelligence — token architecture (primitive→semantic→component), component library governance, CSS variable systems, Tailwind integration, design-to-code handoff, Storybook, Figma token export. Activate for design system creation, token audits, component specs, dark mode systems, brand-to-token sync. Powered by Maxim behavioral science + Atomic Design + TOGAF governance.
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。
Audit context files (CLAUDE.md, .claude/ tree, READMEs, docs) for broken references and verifiably stale content. Runs a deterministic offline link/path checker first, then a bounded semantic review where every finding must cite evidence. Reports findings; fixes only on request. Use when the user says "docs check", "/docs-checker", "check the docs for stale content", "audit CLAUDE.md", or when a scheduled cloud routine fires.
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。
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptools_scm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptools_scm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
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。
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
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。
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
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。
Guidelines for fixing unhandled errors from the VS Code error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.
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。
Formats commit messages using the Conventional Commits specification. Use when creating commits, writing commit messages, or when the user mentions commits, git commits, or commit messages. Ensures commits follow the standard format for automated tooling, changelog generation, and semantic versioning.
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。
CI/CD pipeline patterns with GitHub Actions, release automation, and testing strategies. Use for: github actions, workflow, CI, CD, pipeline, deploy, release, semantic release, changesets, goreleaser, matrix, cache, secrets, environment, artifact, reusable workflow, composite action.
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。
Enterprise-grade security vetting protocol for AI agent skills. Automated threat detection, quantified risk scoring, and zero-trust code 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。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language 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。
Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.
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。
Humanize text: strip AI-isms and add real voice.
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。
Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The 'don't do the work yourself' rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
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。
Systematically audit and remediate accessibility issues in UI, focusing on keyboard navigation, screen reader support, color contrast, semantic HTML, ARIA usage, and motion sensitivity. Use when the user wants to improve accessibility, make a component accessible, fix WCAG violations, add keyboard support, or ensure screen reader compatibility.
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。