データ・AIのスキル

How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

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Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

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。

Secure skill template with prompt injection defenses. Copy this template when creating new skills.

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。

Refine a draft prompt before sending to a coding agent: decomposes goal/context/constraints, flags ambiguity, emits a model-agnostic rewrite. NOT for prompts from scratch (mk:brainstorming).

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 publish-grade SVG+PNG technical diagrams (architecture, data flow, sequence, ER, state-machine, etc.) in 8 styles. Writes to tasks/visuals/. Needs rsvg-convert for PNG; else SVG-only.

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。

JIRA time tracking via jira-as: log work, worklogs, estimates, reports, bulk-log. Edit/delete confirms first (data loss). NOT sprint capacity (mk:jira-agile).

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 current library, framework, and API docs via Context7, Context Hub, and web fallback. Use whenever docs are needed for a library, SDK, or internal spec instead of stale training data.

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。

Secure skill template with prompt injection defenses. Copy this template when creating new skills.

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, reviewing, or refactoring Vue 3 code — components, composables, reactivity, Pinia state, Pinia Colada data-fetching, file-based routing, and forms. Targets Composition API + <script setup> + TypeScript. Auto-activates on .vue files. For deep best-practices review/recommendations or the full ordered workflow (built-in components, animations, performance pass), use mk:vue-best-practices.

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 publish-grade SVG+PNG technical diagrams — architecture, data flow, flowchart, sequence, agent-memory, class, ER, state-machine, comparison, timeline, and more — across 8 visual styles. Writes output to tasks/visuals/ or an active plan's visuals/ directory. Requires rsvg-convert (librsvg) for PNG export; degrades gracefully to SVG-only when absent.

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 plan directory (plan.md + phase-*.md) needs a visual review. By DEFAULT this drives the STRUCTURED visual-plan artifact (visual-plan/plan.json) via the mewkit visual-plan CLI — generate if absent, validate, open the local studio (view/edit), and export plan.html from the approved artifact. Pass --static for the legacy prompt-only single-file plan.html template render. Triggers on "render this plan", "visualize the plan", "open the plan studio", "make a shareable plan page", "plan.html". This is the canonical owner of plan-as-visual-review. NOT for plan critique or scope review (see mk:plan-ceo-review / mk:validate-plan), NOT for generic code/diagram/diff visuals (see mk:preview), NOT for live media generation (see mk:multimodal).

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 refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).

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。

JIRA time tracking via the jira-as wrapper: log work, list/update/delete worklogs, set estimates, generate reports, bulk-log. Triggers: 'log 2h on KEY', 'show worklog for KEY', 'time report for sprint', 'bulk-log 30m to JQL'. Worklog edit/delete loses data — agent confirms. NOT for sprint capacity (mk:jira-agile).

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 up-to-date library, framework, and project documentation using scripts + MCP tools (Context7, Context Hub) with intelligent fallback. Use this skill whenever the user or agent needs documentation for any library, framework, API, SDK, or internal project spec. Triggers on "docs for [X]", "how does [library] work", "find documentation", "API reference for", "look up [feature] in [library]", "latest docs", "what's the API for", "find our [internal spec]", or any request that requires current, accurate documentation rather than relying on training data. Always prefer this skill over raw WebSearch for documentation retrieval — it returns structured, context-efficient results.

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Use when transforming existing code into agent-consumable surfaces — CLI (npm-publishable), MCP server (stdio/SSE/Streamable HTTP), and a companion skill. Triggers on 'agentize', 'henshin', 'expose as MCP', 'wrap as CLI', 'publish to npm', 'make LLM-accessible', 'turn into agent tool', 'expose feature as tool'. Runs when the user has existing code and wants to ship it to agents. NOT for building new code from scratch (see mk:bootstrap); NOT for reviewing or verifying existing code (see mk:review).

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 the user asks about website analytics, traffic, visitors, page views, referrers, conversions, goals, or mentions "fusionaly". Answers from a self-hosted Fusionaly instance through its MCP tools (or its SQL API), in plain English with numbers.

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Use when user asks about website analytics, traffic, visitors, page views, referrers, or mentions "fusionaly". Queries Fusionaly analytics via SQL API.

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。

Zod schema validation and React Hook Form integration for TypeScript frontends. This skill should be used when creating forms, defining API schemas, validating form data, implementing type-safe validation, using ZodResolver with React Hook Form, or migrating legacy forms to the Zod + React Hook Form pattern.

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Django Redis caching with django-cacheops. This skill should be used when implementing caching, adding cache invalidation, optimizing API performance, modifying models that affect cached data, or debugging cache-related issues in the Django backend.

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Semble (MinishLab) MCP wrapper — semantic code search via embeddings (98% token reduction vs grep+read, NDCG@10=0.854)

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。