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概要と使いどころ
Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
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 growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.
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 realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
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。
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
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 SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
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。
Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.
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。
Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.
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。
Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, batch processing, or scheduled tasks. Always consult this skill when the user asks to create, build, or design an n8n workflow, automate a process, or connect services — even if they don't explicitly mention 'patterns'. Covers webhook, API, database, AI, batch processing, and scheduled automation architectures. Also use when optimizing a slow workflow or speeding up large-item-count processing (node count, batchSize, all-items vs per-item).
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。
Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a scheduled or unattended workflow, or any path where a silent failure would drop user-visible work — and whenever the user mentions error handling, onError, continueErrorOutput, error branches/outputs, retries, retryOnFail, Respond to Webhook status codes, 4xx/5xx, Error Trigger, or "my workflow fails silently". Covers per-node error outputs and wiring, retry/self-healing, error-trigger workflows, and 4xx/5xx response shapes.
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 building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.
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 n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
日本語の概要は準備中です。原文の説明を表示しています。
Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs, waitForSubWorkflow, mode each vs all, or exposing a workflow as an agent tool. Covers typed sub-workflow inputs, all-vs-each execution, verb-first naming for discovery, stateless vs stateful design, and splitting by input shape.
日本語の概要は準備中です。原文の説明を表示しています。
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.
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。
Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, set up, provision, or deploy n8n on their own server/VPS/box (Hetzner, DigitalOcean, AWS EC2, bare metal, etc.) — in either single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance. This is for SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect the domain/SSH/timezone inputs, generate fresh secrets on the box, and bring the stack up with TLS. Trigger on "deploy n8n", "self-host n8n", "install n8n on my server", "n8n docker compose", "n8n queue mode / workers / scaling", "n8n reverse proxy / SSL", "back up / update my n8n", or "we don't want to give every user the OAuth client secret" / "enable the Sign in with Google button" (credential overwrites).
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。
CoinGecko API documentation - cryptocurrency market data API, price feeds, market cap, volume, historical data. Use when integrating CoinGecko API, building crypto price trackers, or accessing cryptocurrency market 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。
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
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。
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
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 building or editing any visual surface in apps/web — a page, screen, list, card, badge, avatar, modal, form, empty state, toast, tooltip, table, or panel — and you need to know WHICH component to compose and which primitives are banned. Load kortix-brand first for the values (color, spacing, type, radius, motion budget); this file assumes those and covers components, layout shells, and reference implementations. Source of truth: src/components/ui, the live /design-system page, and the reference implementations listed inside.
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 engineering principles for making interfaces feel polished. Use when building UI components, reviewing frontend code, implementing animations, hover states, shadows, borders, typography, micro-interactions, enter/exit animations, or any visual detail work. Triggers on UI polish, design details, "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, image outlines, box shadows.
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 for distinctive production-grade websites, landing pages, and interactive web experiences with strong design and QA discipline.
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。
Scaffolding that helps a writer build a story they already want to tell — fills in structure, hidden wound, theme, and prose from the writer's own material across seven narrative layers, deriving the spine from their notes as proposals they ratify. Bans generic AI patterns; scales short story to multi-book series. USE WHEN write story, fiction, novel, chapter, story bible, character arc, plot outline, creative writing, worldbuilding, draft story, help me write my story, develop my novel, layer my story, build out my book, I have notes for a story. NOT FOR narrative summaries of real content.
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。
Hardens LifeOS tests via property/mutation testing. USE WHEN harden, hardening, property test, property based testing, PBT, fast-check, mutation test, mutation testing, Stryker, CRAP score, CRAP analysis, DRY scan, jscpd, acceptance test mutation, strengthen tests, sharpen ISCs, find bugs example tests miss, universal quantified claim, shrink counterexample, what bugs am I missing, test the tests, test of the test. NOT FOR writing new feature tests (use bun test directly), grading agent output quality (use Evals), UI verification with real Chrome (use Interceptor), finding security vulnerabilities, or building new functionality.
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。
Generic civic intelligence aggregator for any US city — daily local digest of construction permits, crime, new businesses, public officials, legislation, elections, arrests, and local news, keyed off principal's Hometown. Writes JSON consumed by Pulse LOCAL tab. Crime delegates to a dedicated crime-stats skill. Workflows: DailyBrief, Construction, Crime, Business, Officials, Legislation, Elections, Arrests, News. USE WHEN local news, hometown news, council meeting, building permits, mayor, ballot measures, ordinance, recent arrests, civic intel, local digest. NOT FOR national news or arbitrary-city crime.
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。
Install and onboard a user into LifeOS — the Life Operating System (current state → ideal state via TELOS + the Algorithm). The agentic installer detects your OS + harness, wires hooks with permission, scaffolds your USER tree, pulls in sources you provide, and runs the TELOS / current→ideal interview that seeds your Pulse dashboard. USE WHEN install LifeOS, set up LifeOS, lifeos setup, lifeos-setup, lifeos interview, onboard me, run the interview, integrate LifeOS into my harness, update LifeOS, uninstall LifeOS, first-time setup, lifeos doctor, check my install, what capabilities are broken. NOT FOR building or cutting a LifeOS release (private release tooling), editing TELOS after onboarding (use Telos / Interview), or LifeOS system maintenance (use the private maintenance 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。