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Explain how email and Google Calendar work for OpenClaw agents, including the distinction between agent email and owner email, how gog and the Google Workspace skill are used, where related credentials/config live, and how to troubleshoot common setup or access issues. Use when someone asks how an OpenClaw agent accesses Gmail/Google Workspace/Calendar, where credentials are stored, how to use the agent inbox, how to give the agent write access to the owner's calendar, or how to guide another agent/user through the email or calendar setup model.

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Schedule meetings, parse meeting notes, extract action items. Three distinct intents covered. Invoke when user mentions a specific meeting (past or future) by name or context. NOT for general calendar checks (use direct calendar.py), NOT for time-zone math. Triggers: "schedule with X", "minutes of the X meeting", "action items from yesterday's meeting", "prep me for meeting with X".

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 working with Convex — convex backend platform management covering project inventory, table schemas, function deployments, scheduled job status, index configuration, environment variable auditing, and usage metrics. Use for comprehensive Convex project health and resource optimization.

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 working with Aws Sam — aWS Serverless Application Model (SAM) management. Covers template validation, local testing, build and packaging, deployment orchestration, stack inspection, log tailing, and API Gateway debugging. Use when building serverless applications with SAM, testing locally, deploying Lambda functions, or troubleshooting API Gateway endpoints.

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 working with Aws Lambda Deep — deep AWS Lambda analysis covering function inventory, cold start profiling, memory optimization, concurrency patterns, layer dependency auditing, dead letter queue health, event source mappings, and cost estimation. Goes beyond basic metrics to identify optimization opportunities.

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 working with Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.

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。

REQUIRED helper functions (never raw curl), project_id patterns, suggestion block syntax with offset notation

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。

REQUIRED --repo flag for all commands, helper functions for code review comments, suggestion block syntax rules

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 working with Aws Cloudfront Deep — advanced AWS CloudFront management covering distribution lifecycle, behavior configuration, Lambda@Edge functions, origin groups for failover, real-time logs, field-level encryption, and cache policy optimization. Use for deep CloudFront troubleshooting, performance tuning, advanced origin configuration, or Lambda@Edge debugging.

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。

REQUIRED helper functions (never raw curl), workspace/repo_slug patterns, suggestion block syntax for code 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。

Analyze Compute Engine VM, Cloud SQL, Persistent Disk, and serverless (Cloud Functions/Cloud Run) utilization to identify right-sizing opportunities. Uses Cloud Monitoring metrics with anti-hallucination rules for E2 shared-core instances, sole-tenant nodes, preemptible/spot VMs, SUD eligibility, peak vs average analysis, CUD-aware savings, and estimated monthly savings calculations.

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 working with Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.

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。

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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。

Distinction between classical and modern physics 2.

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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.

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 genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

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 Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown 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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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。

Configure Netlify projects via netlify.toml and the _headers/_redirects files — covering build settings and deploy contexts alongside environment variables/scopes and the Secrets Controller plus redirect/rewrite/proxy and custom-header rules. Use when setting a build command or publish directory, adding redirect or rewrite or proxy rules, configuring custom headers or basic auth, setting or scoping environment variables and secrets, wiring up a monorepo or SPA fallback, or skipping unnecessary builds. Reach for this whenever you touch netlify.toml or ask "why is my env var undefined in a function" or "how do I redirect this path".

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, deploy, and secure Model Context Protocol (MCP) servers on Netlify. Use whenever the task involves creating an MCP server, exposing an app or API to AI agents as MCP tools, letting Claude / Cursor / Claude Code call a custom remote server, or adding MCP tools to an existing Netlify site. Covers the MCP SDK + Streamable HTTP transport on a Netlify Function, authentication (single shared secret vs per-user API keys with Netlify Identity), read/write safety, file uploads, and connecting clients. Use even when the user just says "MCP", "tool server for an agent", or "let an AI use my 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。

Deploy and configure web frameworks on Netlify — build settings and SSR/edge adapters plus local platform emulation and env vars. Use when setting up or fixing a framework deploy (Next.js / Astro / Nuxt / SvelteKit / Remix / React Router / TanStack Start / SolidStart / Gatsby / Angular / Vite / Express / Hydrogen / Hugo / Eleventy / Vue / React), adding SSR or edge functions or middleware wired to Netlify context, fixing SPA redirect and catch-all rules, setting a build command or publish directory, or debugging "why isn't my env var updating" and framework build 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。

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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 this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.

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 this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API 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。