「engine」の検索結果

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。

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。

Optimize for search engine visibility and ranking. Use when asked to "improve SEO", "optimize for search", "fix meta tags", "add structured data", "sitemap optimization", or "search engine 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。

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。

Deep knowledge about deploying applications to Google Cloud Platform (Cloud Run, App Engine, Compute Engine).

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。

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。

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。

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。

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。

Analyzes ticket complexity and maps dependencies against an existing codebase before sprint planning. Triggers on 'how complex is this ticket', 'what should we work on first', 'can we fit this in the sprint', 'tech review before planning', 'plan the sprint'. NOT for writing implementation plans (see mk:plan-creator); NOT for scope/ambition review (see mk:plan-ceo-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。

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。

Plan and run safe reverse engineering, decompilation, disassembly, instrumentation, de-obfuscation, and anti-analysis countermeasures for binaries, firmware, mobile apps, WebAssembly, JavaScript bundles, and .NET assemblies. Use when the user asks for reversing strategy, decompiler selection, binary analysis, malware-style triage, unpacking, deobfuscation, defeating anti-debug or anti-VM checks, Wakaru, Ghidra, IDA, RetDec, radare2, Rizin, Frida, angr, YARA, or command scripts that capture analysis stats.

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。

Coordinate complex engineering work across bounded subagents while protecting the main context, preventing edit conflicts, and measuring whether delegation was worthwhile. Use when the user asks to delegate, parallelize, orchestrate, use subagents, fan out a review, or handle a large task with independent workstreams; do not use for small, tightly coupled, or inherently sequential tasks.

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。

Maintain a review-first Markdown knowledge base for Codex practices with source provenance, engineering capture, citation-aware queries, explicit archive and promotion, and deterministic linting. Use when asked to capture a durable engineering lesson, ingest Codex research, query or archive what the repository knows, check wiki health, reconcile conflicting guidance, or promote verified knowledge.

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。

Reverse engineer and security-test Telegram bots — API analysis, callback interception, exploit discovery, and vulnerability documentation

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。

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python 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。

Optimize for search engine visibility and ranking. Use when asked to "improve SEO", "optimize for search", "fix meta tags", "add structured data", "sitemap optimization", or "search engine 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。

Shape or update a rolling-wave engineering project before slice preparation. Use when the user wants to start a rolling-wave project, define the finish line, clarify success criteria, create or update docs/rolling-wave/{project}/project.md, sketch a broad roadmap, or interrogate project-level direction without implementation details.

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。

Shared support references for the rolling-wave engineering skill set. Use only when a rolling-wave workflow skill explicitly points here, or when maintaining the shape-project, prepare-next-slice, implement-slice, finalize-slice, and complete-slice 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-level transportation engineering covering highway design, traffic flow, intersection design, pavement design, traffic impact studies, and public transit.

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-level nanomaterials engineering covering synthesis, characterization, properties at nanoscale, nanocomposites, safety, and industrial applications of nanomaterials.

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。