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Use for hands-on Pulumi CLI work: running deployments, fixing broken stacks, and managing infrastructure state. Handles: recovering from stuck or interrupted `pulumi up` with pending operations, cleaning orphaned resources from state after out-of-band cloud deletions, protecting critical resources from accidental `pulumi destroy`, moving resources between stacks without recreating them, targeting specific resources during deployment, migrating between backends (local file to Pulumi Cloud, S3), stack lifecycle management, state export/import/repair, CI/CD pipeline setup, and importing existing cloud resources. Use this skill — not the language-specific Pulumi skills — whenever the user's question is about operating, troubleshooting, or recovering Pulumi infrastructure rather than writing program code.

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 or update the feature specification from a natural language feature description.

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。

Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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。

Program language models instead of prompting them — DSPy signatures, modules, and optimizers that tune prompts automatically.

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。

Align language models with human preferences using Direct Preference Optimization — no reward model, no RL loop.

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。

Domain-Driven Design: bounded contexts, ubiquitous language, aggregates, and tactical patterns. Use when modeling complex domains or structuring domain logic.

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 multimodal AI apps on Clarifai — model marketplace, pipelines, and computer vision plus language workflows.

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。

Elicit step-by-step reasoning from language models to boost accuracy on math, logic, and multi-step problems.

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 complete brand identity systems with strategy, visual language, voice, and usage guidelines.

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 legal information systems that explain rights to the public, evaluating plain-language accuracy, jurisdiction-specific content correctness, reading level appropriateness targeting 5th-8th grade, multilingual support, WCAG accessibility 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。

Quality gate for product specifications before stakeholder review. Use this skill when: - A spec draft is ready and you want to validate it before sending to Engineering, Design, and QA - You want to check a spec against your template's completeness rules - You need to catch vague language, missing sections, or gaps before review - You want a pass/fail scorecard with specific fix suggestions

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 or refine the Ideal Customer Profile (ICP) and personas using the Jobs-to-be-Done framework (Clayton Christensen / Bob Moesta). Use when the user asks for ICP research, persona development, "who are our customers", buyer personas, audience research, "build our ICP", target customer definition, or wants to update persona docs. JTBD interview questions, three job dimensions (functional/emotional/social), and the Four Forces of Progress are wired into every persona. Writes to knowledge/icp/personas.md so all other skills get smarter. For the individual buyer persona rather than the segment, see customer-persona. For sourcing real customer language, see customer-research.

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 raw customer material into evidence-backed personas and verbatim voice-of-customer language. Two modes - analyse sources the user already has (call transcripts, support tickets, survey responses, sales notes, reviews), or source new material from public places customers actually talk. Produces quoted pains, objections, and switch triggers that other skills consume, never invented ones. Use when the user says "customer research", "voice of customer", "what do our customers actually say", "we need real quotes", "analyse these call transcripts", "read our support tickets", "build a persona from data", "find what customers complain about", or when a persona is marked provisional. For structuring the finished persona into the knowledge base, see brand-context. For the JTBD persona template itself, see customer-persona.

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。

Parse natural-language quantities, units, dates, and ranges ("5'11\"", "1.5 cups", "72 in to cm", "three days ago", "between 5 and 10 kg", "it's hot") into canonical, validated values with issue codes and spans — and humanize them back. Use when a form input, LLM tool schema, MCP handler, or import pipeline takes free-text measurements/units/dates. Covers Standard Schema fields (quantityField/dateField/lingoObject) for the AI SDK and MCP, the headless <lingo-input>, unit conversion, and format/humanize round-trips. Zero-dependency TypeScript, deterministic.

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。

Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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。

Stress-test a Yuzu plan against the repo's domain language, code, CONTEXT.md, and ADRs while updating docs as decisions crystallize. Use when the user says `/grill-with-docs`, asks to grill a design with docs, wants decision interrogation, or needs ADR-adjacent planning.

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。

Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using natural language.

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。

Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization 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。

Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.

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。

Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress FILEPATH or "compress memory file"

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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 designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).

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。

Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.

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 configuring org-wide platform settings: MFA enforcement, My Domain setup and deployment, session timeout and security settings, password policies, trusted IP ranges (Network Access), and CSP Trusted Sites. Owns these settings as deployable Settings metadata (Security.settings, MyDomain.settings, Company.settings, Language.settings) and the order in which a new org is stood up. Trigger keywords: 'MFA setup', 'My Domain', 'session settings', 'password policy', 'trusted IP ranges', 'CSP trusted sites', 'Security.settings', 'SecuritySettings metadata', 'MyDomainSettings', 'settings-meta.xml', 'enhanced domains', 'deploy org settings', 'new org setup order', 'sessionTimeout enum', 'lockSessionsToIp', 'enableAdminLoginAsAnyUser'. NOT for user-level login restrictions (use user-management), NOT for permission model design (use permission-sets-vs-profiles), NOT for security audit and hardening review (use security/org-hardening-and-baseline-config).

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。