「engineer」の検索結果

Integrate LLM API RAG fine-tuning prompt engineering พร้อมคุม token cost และกัน prompt injection

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。

Engineering operating model for teams where AI agents generate a large share of implementation output.

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。

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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 change end-to-end from a freeform prompt - settle the requirements, design the engineering approach and get it approved, implement with TDD, run the format/compile/credo/coveralls gates without shortcuts, self-review the diff like a senior engineer, QA the finished change in the running app, record a demo video when it is visible there, then commit and open the PR. Use when the user wants to "complete an issue", "work the ticket", or hands over a description of something to build.

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, audit, harden, review, or upgrade MCP servers and MCP Apps with revision-aware protocol profiles, explicit HTTP/deployment boundaries, capability-centered validation, bilingual reporting, and reproducible evidence. Use for MCP server architecture, JSON-RPC or Streamable HTTP bugs, security hardening, protocol-version migration, MCP Apps widget boundaries, review reconciliation, test planning, and public engineering evidence.

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 the systems of a game from a locked concept — core loops, meta loops, player verbs, progression, content systems, level structure, and narrative integration. Triggers on "game design doc", "GDD", "system spec", "core loop design", "progression design", "level design", "MDA", or "design the systems". Produces a design doc plus per-system specs that game-balancer numbers, iap-manager stocks, and godot-engineer builds. Stops at design — no number tuning, pricing, or engine code. For balance numbers see game-balancer; for the engine implementation see godot-engineer.

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 building games or interactive software in Phaser 3 with TypeScript — scenes, gameplay code, input, Arcade physics, animations, audio, tweens, asset preloading, save systems, performance work, or scaffolding with Vite. Triggers on "Phaser", "Phaser 3", "Phaser.Scene", "Phaser.Game", "Arcade physics", "Matter physics", "tilemap", "Tiled", "GameObject", "Container", "Group", "Vite + Phaser", or any `.ts`/`.js` file in a Phaser project. For game design and balance see game-systems-designer, game-balancer; for game intake see game-design-shaper. For Godot/C# see godot-engineer.

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 building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".

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。

Structures a vague engineering request into a well-scoped task brief before any implementation begins. Use when the user has an engineering goal but the ask is missing which repos are in scope, what "done" means, constraints, or open questions. Triggers on "shape this", "scope this out", "frame this work", "write a brief for", "I want to build" (with unclear scope), or the /shape slash command. Produces a filled task template (multi-repo feature, single-repo change, investigation, or bugfix). Not for already-scoped work — go straight to execution. If the domain is unclear, ask one qualifying question before routing — for marketing intake see marketing-shaper, game-design intake see game-design-shaper.

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 writing, reviewing, or deploying Solidity smart contracts — token contracts, signature-gated claim systems, merkle-gated staking vaults, allocation modules, role-based proxies, or factory patterns. Triggers on edits to `*.sol` files, `hardhat.config.*`, `foundry.toml`, deploy scripts, or mentions of "smart contract", "Solidity", "Web3", "EVM", "Hardhat", "Foundry", "ERC20/721/1155", "merkle", "staking", or "on-chain". For security audits and adversarial review see security-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。

Complete construction estimating and cost management system. Use when preparing project estimates, bid proposals, cost breakdowns, value engineering, change order management, or construction budget tracking. Covers residential, commercial, and infrastructure projects. Trigger on 'estimate', 'construction cost', 'bid', 'takeoff', 'cost breakdown', 'change order', 'value engineering', 'construction budget', 'unit pricing', 'RSMeans'.

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。

You are the Accessibility Engineering Engine — a complete WCAG compliance, inclusive design, and digital accessibility system. You help teams build products that work for everyone, pass audits, and...

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。

Understanding this is key to prompt engineering: you

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 recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent work.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to cursor-implementation-loop, this plugin's kernel skill, with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to cursor-implementation-loop. Requires this plugin's cursor-implementation-loop skill; they ship together.

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。

Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.

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。

Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).

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。

Prepare a traceable local Fusion engineering review draft with source identities, artifacts and unresolved assurance gates. Not for sending, publication or release approval.

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。

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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。

Expert DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline, infrastructure as code, container orchestration. NOT for: application code (use language skills), database schema (use data-pipeline-engineer), API design (use api-architect).

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。

Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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 audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).

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 and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

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。