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概要と使いどころ
Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).
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。
Large-scale data migrations with validation, rollback, and zero-downtime strategies. Activate on: data migration, database migration, zero-downtime migration, dual-write, backfill, cutover, data validation, schema migration. NOT for: schema evolution in streams (use schema-evolution-manager), API versioning (use api-versioning-backward-compatibility).
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 applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.
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。
Validates agent outputs against expected schemas and quality criteria. Reports structural and content checks within the declared evidence scope. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).
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。
Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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。
Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "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。
Locate CCSPlayer_MovementServices::Pawn in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSPlayer_MovementServices::Pawn" (symbol CCSPlayer_MovementServices_Pawn). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CCSPlayer_MovementServices class layout via the schema/network system; the member must satisfy natural alignment. Cross-check with functions that dereference the member. Do not switch output schema: emit the structmember fields below even if the class turns out to be polymorphic - a func_*/vfunc_* payload for this symbol is rejected by canonical_symbol_yaml_bytes. Trigger: CCSPlayer_MovementServices_Pawn, CCSPlayer_MovementServices::Pawn
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。
Locate CCSBot::Profile in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CCSBot::Profile" (symbol CCSBot_Profile). This is a STRUCT MEMBER OFFSET (schema netvar), not a function. Resolve the CCSBot class layout via the schema/network system; the member must satisfy natural alignment. Cross-check with functions that dereference the member. Do not switch output schema: emit the structmember fields below even if the class turns out to be polymorphic - a func_*/vfunc_* payload for this symbol is rejected by canonical_symbol_yaml_bytes. Trigger: CCSBot_Profile, CCSBot::Profile
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 generate Convex database schemas with proper validation, indexes, and relationships. Use when creating schema.ts or modifying table definitions.
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。
编写Premake5的Lua函数，用于一次性自动配置静态库的头文件包含路径（包括第三方依赖）和链接设置，解决多路径包含问题。
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。
Draft professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composing professional messages.
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。
Manage received (inbound) emails and attachments via Resend API. Use when user asks about their emails, received messages, or email attachments.
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。
Maxim cinematic brand film — engineered for commitment and emotional brand alignment. Uses Hero's Journey + Peak-End Rule + Emotional Arc Theory to convert viewers into brand advocates. Use for brand films, anthem ads, founder stories, long-form brand content. Triggers on cinematic, film, brand film, anthem, dramatic, epic, Hollywood-style.
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。
AI video and image prompt engineering — video generation, image generation, product video, social media video, cinematic direction, animation, Seedance, Higgsfield, GPT-Image-2. Applies 15 Maxim-authored cinematic styles and the atomic prompt schema.
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。
Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.
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。
INVOKE THIS SKILL when creating, managing, or using annotation configs on Arize (categorical, continuous, freeform), or applying human annotations to project spans via the Python SDK. Configs are the label schema for human feedback on spans and other surfaces in the Arize UI. Triggers: annotation config, label schema, human feedback schema, bulk annotate spans, update_annotations.
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 sending transactional emails (welcome messages, order confirmations, password resets, receipts), notifications, or bulk emails via Resend 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。
Use when setting up an email inbox for an AI agent (Moltbot, Clawdbot, or similar) - configuring inbound email, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks.
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 a new feature needs a table, column or capability — evolving the schema so the new thing joins the permission, audit and RLS contract instead of sitting outside it. Covers the DDL/DML split, the resource/capability wiring, and the self-verifying migration.
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 FIRST when someone asks for a new feature or a change to an existing one — before any schema, code or test. Works out what is actually being asked for, whether to build it or integrate a third party (Trustpilot, Stripe, Calendly and the like), where the data lives, who may do what, and what happens when the vendor is down. Produces a decision, then hands to feature-schema.
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。
Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs.
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。
Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive 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。
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
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。
Implement a phishing report button in email clients with automated triage workflow that analyzes user-reported suspicious emails and provides feedback to reporters.
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。