「schema」の検索結果

软件开发团队架构师（高见远）。基于 PRD 设计系统架构 + 任务分解。擅长技术选型、系统架构设计、API 设计、DB Schema 设计、任务拆解与依赖分析。 触发词：由 software-team-lead 主理人调度执行架构设计任务时激活。不直接面向用户。

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MVP 开发专家团架构师（高见远）。擅长技术选型对比矩阵、系统架构设计、RESTful API 设计、DB Schema 设计、技术可行性验证。 触发词：由 mvp-dev-team-lead 主理人调度执行 Phase 1 调研或 Phase 2 设计细化任务时激活。不直接面向用户。

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SQL查询架构师（秦逸 Quinn）。将自然语言转换为优化SQL，支持多表关联、窗口函数、CTE；自动探索数据库Schema，执行查询并返回结构化结果与数据画像。 触发词：由 copilot-team-lead 主理人调度执行，不直接面向用户。当主理人下发 Phase 1 数据采集任务时激活。也可响应纯SQL查询类直接请求。

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Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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Generative Engine Optimization plus agent readiness. Use this skill whenever the user asks about GEO, AEO, LLMO, optimizing content for AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Copilot), getting cited by LLMs, brand mentions in AI responses, llms.txt, robots.txt for AI crawlers, Schema.org JSON-LD, FAST framework, agent readiness, MCP server discovery, Web Bot Auth, OAuth for agents, API Catalog, agentic commerce (x402, ACP, UCP), Markdown content negotiation, or auditing a site or page for AI visibility. Also trigger when the user says "AI SEO", asks how to "rank in ChatGPT", "show up in Perplexity", "appear in AI Overviews", or wants a site to "speak to AI agents". Trigger even when the request is implicit, such as "review this article so AI search picks it up" or "make our docs agent-friendly".

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Super Base Context — project doc loader & synchronizer. Triggers even without explicit "load docs"/"sync docs." READ MODE — load architecture/modules/coding/decision docs when work references project state: code work (write/modify/remove); design (features/APIs/schemas/architecture); review (modules, past solutions); tech selection; ADR research; migration/refactor planning; performance/security analysis; tests; code reviews; any spec/plan/design/ADR referencing project. WRITE MODE — NET DELTA vs. last doc sync: additions/modifications/REMOVALS/deprecations of previously-synced code or decisions (docs revert: delete BCU file/ADR), AND rollbacks. User signals: sync docs/done/ready to commit/pre-commit/record decision. Auto-fires before final task-completion reply. WRITE MODE does NOT fire on: brainstorming, generic/read-only Q&A, in-session try-and-undo with no net change, already-synced.

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Manage application configuration—schemas, validation, .env.example, placeholder detection, fail-fast startup. Use when adding env vars or centralizing config.

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Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - → 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.

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Design agent tools and CLI surfaces—schemas, naming, errors, idempotency, and discoverability for LLM callers. Use when defining tools for agents, SDKs, or AI-native CLIs.

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Design component architecture, REST API contracts, and data models (Pydantic schemas) before implementation. Use when planning how to build a feature, defining endpoints, or modeling data. Trigger on "design the architecture", "design the API", "data model", "how should I structure this feature".

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Run SQL queries against psql, BigQuery, or MySQL from the terminal, including natural-language-to-SQL and schema exploration. Use when analyzing data, inspecting DB state, or debugging tables. Trigger on "query the database", "SQL", "show me data from", "explore table".

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Design data models with Pydantic schemas, comprehensive validation rules, type annotations, and relationship mappings.

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Generates machine_readable execution logic for Dutch law YAML files: writes the machine_readable sections, validates against the schema, and (when a shell is available) runs the BDD suites until the model is correct. Use this skill proactively when: editing or creating machine_readable sections in law YAML files, working with corpus regulation files, or when user mentions 'generate', 'machine_readable', or wants to make a law executable. Activate automatically when user discusses law YAML files that need executable logic.

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Voert de iteratieve desk-review-&-completion-cyclus uit op een regelrecht-corpus — plannen, harvesten/migreren/uitbreiden, multi-agent valideren, bevindingen 4-weg classificeren (modellering-fout / wetgevings-fout / engine-limitatie / acceptabele untranslatable), synthese + heroverweging, documenteren (wetgevings-/stelselfouten-analyse, fixes-plan, engine-limitaties, diagrammen, corpus-status), verifiëren tegen externe bronnen (resolutie-tracker + bronnen-dossier), en een eindrapport per cyclus. Gebruik dit bij corpus-review op correctheid/coverage zonder live expert-sessie, het opstellen van een wetgevings-notitie-bron, schema-migratie, het harvesten van nieuwe wetten, of het aansturen van een autonome completion-loop. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Voor live-validatie met experts: zie regelrecht-audit-products.

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API security automation skill for REST, GraphQL, gRPC, and WebSocket APIs. Covers OpenAPI/AsyncAPI ingestion, authenticated fuzzing, OWASP API Top 10 (BOLA, BFLA, mass assignment, SSRF), schema diffing, GraphQL introspection abuse, JWT and OAuth misuse, rate-limit and replay testing. Use to automate API assessments with safe, scoped, evidence-backed findings.

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Exploit exposed GraphQL introspection endpoints to map the entire API schema. This skill details how to extract available queries, mutations, types, and fields, which significantly aids in identifying hidden endpoints, Broken Object Level Authorization (BOLA/IDOR), and mass assignment vulnerabilities.

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Covers object-level authorization bypass in GraphQL APIs where introspection reveals hidden fields or mutations that accept arbitrary user/resource IDs without ownership checks. Trigger on keywords like "GraphQL", "query", "mutation", "introspection", "resolver", "node ID", "relay", "object type", "schema", "batching", or "alias". Applies to dual-stack REST+GraphQL apps, Relay-style global IDs, and unauthenticated resolvers.

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GraphQL and hidden parameter testing playbook. Use when exploring introspection, batching, undocumented fields, hidden parameters, schema abuse, and GraphQL authorization gaps.

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Identify and exploit GraphQL API vulnerabilities by leveraging Introspection queries to dump the entire database schema, performing query batching to bypass rate limits (brute forcing), and extracting deeply nested unauthorized data via graph relationship abuse.

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Deep API security assessment beyond surface scanning. Covers the full OWASP API Security Top 10 (2023): Broken Object Level Authorization (BOLA / IDOR), Broken Authentication, Broken Object Property Level Authorization (mass assignment + excessive data exposure), Unrestricted Resource Consumption, Broken Function Level Authorization (BFLA / vertical privilege escalation), Unrestricted Access to Sensitive Business Flows, Server-Side Request Forgery via API parameters, Security Misconfiguration, Improper Inventory Management (shadow/zombie/deprecated endpoints, v1/v2 drift), and Unsafe Consumption of third-party APIs. Works across REST, GraphQL, gRPC, SOAP, and MCP servers. Discovers APIs from OpenAPI/Swagger specs, GraphQL introspection, gRPC reflection, .well-known endpoints, JS bundles, and traffic capture. Uses kiterunner, ffuf, schemathesis, restler-fuzzer, openapi-fuzzer, graphql-cop, clairvoyance, batchql, inql, jwt_tool, postman, mitmproxy, and manual http(action="request", ...) payloads. Every technique includes actual payloads, commands, and verification logic. Chains from /pentester or /codebase when API endpoints are discovered, chains into /web-exploit when classic injection points are found in API parameters, chains into /post-exploit when RCE is achieved, and chains into /ai-redteam when an LLM/AI endpoint is discovered (chat APIs, completion endpoints, RAG search, agentic tool-use, MCP servers).

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API reconnaissance and documentation review playbook. Use when discovering endpoints, schemas, versions, OpenAPI specs, hidden docs, and surface area for API testing.

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Build a full feature in this Flutter repository that includes backend or storage data flow: read API schema, create DTOs, map DTOs to entities, add Riverpod use cases, connect feature state, and render UI data. Use when a task goes beyond a screen and needs real data integration.

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Use when building Django REST APIs with django-ninja - Pydantic schemas, routers, CRUD endpoints, authentication, pagination, file uploads, async views, OpenAPI docs, or migrating from DRF

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。