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Coordinate teams of role-based agents — role definitions, task delegation, sequential and hierarchical workflows, and shared memory. Use when a job naturally splits into specialist roles collaborating.

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 streaming AI user interfaces with AI SDK patterns — message rendering, tool-call displays, loading states, and optimistic updates. Use when creating chat UIs that stream model output and show agent activity.

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。

AI agent guidance — agent architecture, tool design, planning loops, guardrails, evaluation, and production 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。

Run goal-driven autonomous agent loops — decomposing objectives, executing tool chains, self-critiquing, and iterating until done. Use when exploring what fully autonomous task execution looks like and its limits.

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。

Designing alert rules that fire when it matters — thresholds, burn rates, and routing — for any monitoring stack.

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。

Argo CD guidance — GitOps workflows, Application manifests, sync strategies, AppProjects, and multi-cluster ops.

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 agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.

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。

Writing abstracts that get papers read — structured content, the 5-sentence core, and journal-specific constraints.

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 memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.

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。

Idiomatic Astro: islands architecture, content collections, SSR/SSG choices, and performance-first builds. Use when writing, reviewing, or structuring Astro sites.

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。

Architect autonomous agent systems — perception/action loops, tool design, planning strategies, memory, and multi-agent orchestration patterns. Use when designing an agent from scratch or scaling a prototype 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。

Architect persistent, searchable memory for agents — episodic, semantic, and procedural stores with retrieval, consolidation, and forgetting policies. Use when designing how an agent remembers across sessions.

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。

Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.

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。

Master 1Password: vaults, Watchtower, passkeys, SSH agent, CLI, and family/team administration. Use when getting full value from 1Password personally or administering it for others.

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。

Application performance monitoring — transactions, profiling, and finding slow code — works with any APM platform.

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 with Aleph Alpha's Luminous models — European AI with emphasis on explainability and sovereignty.

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, score, and shortlist brand names through a guided interview. Use when the user says "name my brand", "help me name", "come up with a name", "naming ideas", "what should I call this", "brand name", "product name", "company name", "rename", "rebrand", or is stuck choosing between names. Self-contained and brand-agnostic: it does not read knowledge/, so it works for naming a brand-new venture, product, service, feature, or rebrand from scratch, before any brand context exists. Runs on Alexandra Watkins' SMILE and SCRATCH tests to generate and filter, Marty Neumeier's 7 criteria to score, and the Lexicon Branding method for sound symbolism and strategy. Asks the user questions before generating. For the positioning the name has to carry, see positioning-doc. For the message architecture built on top of it, see messaging-framework.

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 reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.

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**WORKFLOW SKILL — MANDATORY for ANY hook work.** Create, configure, edit, debug, or review lifecycle hooks for VS Code Copilot (default) and Claude Code. USE FOR: criar/configurar/editar hook, hook do Copilot/Claude/agente, lifecycle hook, PreToolUse, PostToolUse, SessionStart, SubagentStop, Stop hook, UserPromptSubmit, PreCompact, bloquear comando, validar tool call, injetar contexto, guard, .agent.md frontmatter hooks, hooks.json, .github/hooks, .claude/settings.json, hook Node.js, cross-platform, hook não dispara/silencioso/ignorado, permissionDecision, hookSpecificOutput, additionalContext. **DEFAULT = VS Code GitHub Copilot.** Switch to Claude Code only when user explicitly says "Claude" OR workspace has `.claude/`. NEVER assume Claude. DO NOT USE FOR: general coding, creating agents (use agent-creator), creating skills (use skill-creator).

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。

Evaluate page content for usefulness, E-E-A-T, readability, thinness, and AI citation readiness, plus last-mile draft cleanup (AI-typical phrasing and invisible Unicode watermark characters). Use for content-only analysis, not full-page technical checks.

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。

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".

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。

Turn one approved master still into a character's whole animated sprite set - one image-to-video clip per action (idle, walk, run, attack, jump, hurt, cast and so on), all from that still, generated through generate2dmedia route_media.py (the xAI API when a key is configured, else the local Grok CLI) - then gate each take, key it with a soft matte, register it to the master, pick loops or retime actions, finish (HD by default, pixel on request) and package fixed-canvas frames, alpha WebM, packed MP4 for iPhone and a PNG fallback with engine metadata and a JS runtime. Also processes a supplied clip. Use for character and creature animation, fluid motion and any video-to-sprite work. Not for FX, icon or prop sheets (generate2dsprite), maps or plate loops (generate2dmap), code-drawn animation (codeart2d, only on request) or calling a video API by itself (generate2dmedia).

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。

Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.

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Use when admins are reducing Chatter signal-to-noise across an org — covers org-level Chatter Email Settings, per-user Email Settings preferences, group-level email digest frequency (Daily / Weekly / Every post / Never), bell-icon Custom Notifications vs Chatter feed-post tradeoffs, automated-feed suppression on record types via Feed Tracking config, and the Chatter Settings org defaults that control whether new users start with everything-on. Triggers: 'chatter feed full of automated posts nobody reads', 'users complain about chatter email volume', 'turn off chatter notifications for a specific group', 'change default chatter digest frequency', 'stop process builder posts from filling the feed', 'NotificationFrequency invalid value', 'ChatterEmailsMD.settings will not deploy', 'custom notification recipient limit'. NOT for Custom Notifications API design (use apex/apex-custom-notifications-from-apex), NOT for Connect API / Chatter REST API integration patterns (use apex/apex-connect-api-chatter), NOT for Chatter group lifecycle, archiving, ownership and unlisted-group governance (use admin/chatter-group-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。