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概要と使いどころ
Develop or review dygo PostgreSQL connectivity, schema planning, additive migration, prune, metadata persistence, Records, transactions, and schema snapshots. Use for framework database behavior and high-risk data lifecycle changes.
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。
Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.
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。
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
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-ready frontend interfaces with high visual quality. Use this skill whenever the user asks to build, design, or improve any web UI — including landing pages, dashboards, components, app screens, forms, or interactive widgets. Also trigger when the user says "make this look better", "redesign this", "add polish", "build a UI", "create a page", "style this", or references HTML/CSS/JS, React, Vue, Svelte, or any frontend framework. If the request involves any visible output rendered in a browser, use this skill — even if the user hasn't explicitly said "design." This skill produces intentional, memorable interfaces that avoid generic AI patterns.
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。
Identify high-quality sales leads by analyzing your business, defining ideal customer profile, and discovering target companies with personalized contact strategies.
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 consistent, high-quality internal communications (status reports, 3P updates, newsletters, FAQs, incident reports). Use company standards and formats for all internal messaging.
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 high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.
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 and analyze competitor ads from ad libraries (Facebook, LinkedIn, etc.) to understand messaging strategies, pain points highlighted, and creative approaches that resonate with audiences.
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 git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
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。
Collaborative writing partner that assists with research, outlining, drafting, citations, and iterative feedback. Helps writers create high-quality content while maintaining their unique voice and style.
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。
Detect cell-ordering / enchainement problems in Jupyter notebooks (canonical-order slippage, misplaced or forgotten cells). Arguments: [target] [--severity HIGH|MED|LOW] [--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。
Council — 5 independent AI perspectives + mutual critique + synthesis (adapted from karpathy/llm-council for Claude Code). For high-stakes decisions where the answer does NOT follow unambiguously from the data — go/no-go before real consequences, critiquing a methodology, choosing between competing approaches, pressure-testing a conclusion you're attached to, designing risk controls. NOT for log/data analysis (the council has no data access) and not for factual lookups. Triggers — "convene the council", "run it past the council", "go/no-go", "pressure-test this".
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。
Bootstrap self-improving Symphony + Linear orchestration for a software repository. Use when Codex needs to inspect a repo, review or generate repo-local guidance, shape Linear issues, render a Symphony workflow, launch a high-leverage first execution wave, or run an orchestrator-led review, recovery, and learnings loop that makes future runs better.
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。
Delegate implementation to Codex, grok, or cursor-agent, then review the diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants Codex, the grok 后端, or cursor-agent to write code, mentions handing off / delegating implementation to one of those backends, asks to work through a plan or spec unit-by-unit with an implementation backend doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer runs in the real environment behind backend-specific git and publication boundaries, must not be pointed at a full test suite by default, and its self-report is a claim rather than evidence. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the engineering-mode skill when it is installed.
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。
Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).
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。
Select complementary review lenses for a requested multi-perspective review or a high-risk GEODE change. Covers design, agent constraints, gateway operations, and CLI/tool behavior; not a mandatory four-agent gate for ordinary edits.
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。
欧盟 AI 法案知识引擎——以 70 份官方欧盟来源文件为基础的权威监管问答（包括 2026 年委员会关于 Art. 6 高风险分类的指南草稿——一般原则、附件 I、附件 III）。基于完整条例文本、委员会指南、EDPB/EDPS 意见、《实践守则》、协调标准、FRIA 指南、事件模板和行业特定指引，以条款级引用回答关于 AI 法案的任何问题。当用户询问"EU AI Act"、"AI Act regulation"、"Regulation 2024/1689"、"KI-Verordnung"、要求"explain an AI Act article"、"what does Article X say"、"AI Act requirements"、"AI Act penalties"、"AI Act timeline"、"GPAI obligations"、"high-risk AI"、"prohibited AI practices"、"AI Act and GDPR"、"fundamental rights impact assessment"、"AI Act standards"、"AI Act national implementation"、"AI literacy"、"serious incident reporting"、"Digital Omnibus"、"AI regulatory sandbox"，或任何关于欧盟人工智能监管的问题时，应使用本技能。德语术语同样触发："KI-Gesetz"、"KI-Verordnung"、"Hochrisiko-KI"、"KI-Kompetenz"、"Grundrechte-Folgenabschaetzung"、"GPAI-Verhaltenskodex"。对于产出分类决定而非知识答案的评估工作流，提议在会话内运行结构化风险等级分类。
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。
Patterns for vibe coding that produce production-quality output instead of garbage. Covers prompt engineering for AI coding, verification without reading every line, accept/reject/refine decision framework, architectural guardrails, context management (CLAUDE.md, .cursor/rules), TDD-driven vibe coding, screenshot-driven development, diff review discipline, and the anti-patterns that destroy quality. Activate on: "vibe coding", "AI coding quality", "Claude Code workflow", "AI-assisted development", "how to use AI for coding", "coding with AI", "agentic engineering workflow", "AI code quality", "vibe code without garbage", "production quality AI code", "CLAUDE.md setup", "cursorrules setup", "pre-commit hooks for AI", "TDD with AI", "test-driven vibe coding". NOT for: multi-person collaborative coding sessions (use cooperative-vibe-coding), building AI/LLM applications (use ai-engineer), prompt engineering for non-coding LLM use cases (use prompt-engineer).
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。
Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.
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 and implement systematic checklists that reduce errors by 30-50% in complex, high-stakes domains (medicine, aviation, construction, finance). NOT for simple tasks or when comprehensive instruction is needed.
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 roadside signs and large-format ads for businesses, especially automotive dealers. Use for pylon signs, monument signs, lot banners, highway billboards, digital roadside boards, service-lane signage, used-car event signs, lease or finance campaign concepts, and other driver-at-speed messaging where legibility, offer hierarchy, and dealer ad compliance matter. Activate on "road sign", "billboard", "dealership sign", "car dealer ad", "pylon sign", "monument sign", "service lane signage", "automotive billboard", "roadside ad", "dealer lease offer", or "drive-by signage". NOT for fabrication drawings, permitting or local sign-code review, web or social ad campaigns, or full brand identity 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。
Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.
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。