「schema」の検索結果

サイトが検索で見つかりにくい原因を、検索エンジンの巡回・登録、表示速度、ページ内容などから調べ、根拠と優先順位を添えた改善案にまとめるスキル。

• 重要ページが検索に出ない原因調査
• タイトル・説明文・見出しの点検
• サイト移行後の技術的なSEO確認

LLMの応答をJSONや指定形式に整え、推論サーバーを構築するスキル。共通プロンプトの計算を再利用し、ツール呼び出しや複数ターンの会話を扱います。

• 指定形式のJSONで情報を抽出したいとき
• ツールを呼び出すエージェント開発
• 会話で共通プロンプトを再利用したいとき

Agent PlatformでGemini APIを使う企業向けアプリの実装を案内します。Google Gen AI SDKによる認証から、文章・画像・音声の処理や一括実行まで扱います。

• 文書やメディアを理解する機能の追加
• 指定した形式のJSON生成
• 音声・映像を使うリアルタイム対話

Use when defining the shape of cognee's knowledge graph with graph_model= — writing DataPoint node classes, choosing identity and index fields so nodes merge and are searchable, declaring typed Edge fields and FromIdentity references, building a model from a JSON schema, or debugging duplicated nodes, missing edges, or InvalidReferenceTypeError.

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 dealing with cognee database migrations — understanding when they run automatically, checking or repairing migration state with cognee-cli upgrade/downgrade/stamp/current, a write blocked by a failed migration, authoring a new Alembic (relational schema) revision or a graph/vector data migration, or moving data between systems (relational DB import, memory export/import).

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。

Migrate a brain from gbrain-base (or any pack) to gbrain-base-v2's 14-canonical-type taxonomy via gbrain onboard --check + the unify-types Minion handler. Collapses 94 noisy types to 15 canonical with subtypes, alias rows, and link rows. Triggers when an agent notices pack_upgrade_available, type_proliferation, or asks "what is the canonical taxonomy / how do I clean up my page types".

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。

End-to-end discipline for turning any large data source (audio libraries, email takeouts, document corpora, chat exports, API dumps) into brain pages at scale. The lifecycle spine: SCHEMA → ACCESS → TRIAL → EVALUATE → IMPROVE → CODIFY → TEST → SKILLIFY → BULK → MONITOR. State is tracked in a durable JSON manifest (see MANIFEST-PATTERN.md) so any crash, session boundary, or subagent fan-out resumes from ground truth instead of memory.

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。

Everything In Its Right Place. The universal post-work organizer. After any significant work session, EIIRP runs a 7-phase audit: (1) inventory every output, (2) walk taxonomy to decide where each lands, (3) check schema-pack consistency against the brain's actual shape, (4) file enriched brain pages, (5) audit the skill graph for DRY+MECE, (6) verify resolvability, (7) report. Named after the Radiohead song. Nothing produced during significant work lives only in chat — knowledge becomes permanent, patterns become reusable. Also carries the always-on auto-fire gate: when >=500 words of structured analysis on a user-shared document is about to be delivered, file the brain page first, then deliver the analysis with the link in that same reply.

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。

Discover and invoke OpenCLI capabilities for websites and Chrome. Find commands, inspect schemas, interpret results and choose browser or adapter tools.

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。

Audits and fixes both halves of being found — classic search and AI answers. Search side: rankings, site structure, page speed, titles and descriptions, and content that answers what customers search for. AI side: whether assistant crawlers can reach the site, pages render without JavaScript, an llms.txt file, schema.org data, and presence in the directories assistants read. For online stores it also runs a bulk catalog cleanup, rewriting product titles, descriptions, alt text, and SEO fields in approved batches. Works on any public website with no connector. Use this whenever the owner talks about being found online, including "nobody finds us on Google," "how do I show up in ChatGPT," "make my website AI friendly," "SEO audit," "GEO," "AEO," "AI search visibility," "my competitor ranks above me," "fix my product listings," or "I asked an AI about my industry and we weren't in the answer."

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 or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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 after a Codex or Claude Code feature has been implemented in Qwen Code to run the selected reference agent and Qwen Code under the same scenario, capture HTTP and terminal traces, compare request bodies, tool/function schemas, outputs, and iterate until the reproduced behavior is close enough.

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 reproducing an existing Codex or Claude Code feature in Qwen Code or another agent CLI by choosing a reference agent, capturing HTTP request bodies, prompts, tool/function schemas, terminal output, and then implementing the matching behavior in the target repo.

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。

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.

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 complete, production-ready SaaS project boilerplate including authentication, database schemas, billing integration, API routes, and a working dashboard using Next.js 14+ App Router, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe. Use when the user wants to create a new SaaS app, start a subscription-based web project, scaffold a Next.js application, or mentions terms like starter template, boilerplate, new project, or wiring up auth and payments.

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 and ship production-ready MCP (Model Context Protocol) servers from OpenAPI contracts instead of hand-written tool wrappers. Python and TypeScript support, schema validation, safe evolution. Use when exposing an existing API as an MCP server, building tool integrations for Claude or Codex or Cursor, or scaffolding an MCP project from scratch.

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 to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy.

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 to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.

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。

/cs:onboard — Founder interview that populates ~/.claude/company-context.md using the canonical 7-dimension cs-onboard schema. The first command to run when starting with c-level-agents. Use when setting up the virtual C-suite for a new company, or when advisors lack company context — e.g. before a first /cs:boardroom or after a fundraise changes the numbers.

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。

Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).

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。

Localization rules and audit workflow for cmux UI strings, settings rows, menus, shortcuts, schema/config text, docs, command/help text, alerts, tooltips, and web messages. Use whenever changing user-facing text.

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。

Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions, schemas, output size, errors, async flows) without the maintainer manually driving an MCP client. Use after adding or changing MCP tools, or when asked to check that the MCP "works" or "is ergonomic".

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。

Assists in provisioning instances and databases, designing performant schemas, and querying data in Cloud Spanner on Google Cloud. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.

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。