868 件 ・ 関連度順
概要と使いどころ
Complete Claude Code productivity system — project setup, prompting patterns, sub-agent orchestration, context management, debugging, refactoring, TDD, and shipping 10X faster. Zero scripts 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。
Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
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, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
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 in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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 deploy copilot-style assistants on Azure with secure model access, tool integration, and observability. Use this skill when users ask to build assistants, agents, or copilot workflows in Azure. Covers orchestration, retrieval, safety guardrails, and production deployment.
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 agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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。
Harness Engineering 設計模式 — 基於 Claude Code 原始碼逆向分析的 12 條可遷移原則。Use when: 設計 agent 系統架構、實作 tool orchestration、設計 context 管理策略、建構 agent loop。
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。
Prove — or honestly fail to prove — a specific technical claim: "the fix works", "it's deployed", "the service is running", "the site is done", "there are no vulnerabilities". Atomizes the claim, picks the shortest decisive signal on the evidence ladder, and returns a verdict from a fixed vocabulary (pass / partial / fail / skip) with an explicit coverage boundary. NOT code review, NOT a fix, NOT orchestration — only "what is proven, and what we merely hope". Triggers — "prove it works", "is it really done?", "are you sure?", "we deployed — is it live?", "verify that", "did we actually check?".
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。
Bootstrap self-improving Symphony + Linear orchestration for a software repository. Use when Codex needs to inspect a repo, review or generate repo-local guidance, shape Linear issues, render a Symphony workflow, launch a high-leverage first execution wave, or run an orchestrator-led review, recovery, and learnings loop that makes future runs better.
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。
Runs the full implement-and-commit loop over a task list — selects the next incomplete sub-task, implements it, runs linters and tests, marks it done, commits, and repeats until the list is finished. Use when asked to work through a feature's task list at .ai/[feature]/tasks.md unattended, or to keep going until every sub-task is complete. For a single sub-task with no commit, use task-implementation; to stop for approval between each one, use iterative-development.
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。
Implements one numbered sub-task from a task list at .ai/[feature]/tasks.md, running linters and tests, without staging, committing, or marking the task complete. Use when asked to carry out a specific sub-task by number, or as the inner step of a larger loop that handles commits separately. For the full loop including commits, use task-orchestration.
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。
Implements sub-tasks from a task list at .ai/[feature]/tasks.md strictly one at a time, stopping after each to ask the user before starting the next, and keeping the task list and Relevant Files section current. Use when the user wants to review or approve each sub-task before work continues, rather than letting the agent run the list unattended with task-orchestration.
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。
16-phase feature orchestration workflow. Runs in the main thread as Kernel Mode. Coordinates the Lead→Developer→Reviewer→TestLead→Tester assembly line.
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。
Resume an interrupted orchestration run across Claude Code sessions using MANIFEST.yaml
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。
Domain-aware complexity routing: scans the task for domain keywords, matches domain-complexity.csv, and returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.
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。
Single-task pipeline: plan → test → build → review (ship separately). Use for a feature or scoped fix. NOT for green-field builds (mk:autobuild) or auto-orchestration (mk:workflow-orchestrator).
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。
Manages git worktrees for parallel agent isolation. Creates isolated worktrees for parallel agents on parallel/{name}-{timestamp} branches, merges results with conflict detection, prunes stale metadata, and reports health status. NOT for user-initiated feature branches — use git worktree directly for that.
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。
Domain-aware complexity routing. Scans task description for domain keywords, matches against domain-complexity.csv, returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.
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。
Detects task agent and complexity tier at Phase 0 orient. Invoked first in every workflow. Provider adapters select any model/runtime policy. NOT for runtime agent routing inside a single skill (see orchestration-rules.md).
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。
Orchestrates single-task implementation pipeline: plan → test → build → review. Shipping is an explicit follow-up. Use for feature work, plan execution, or fixes scoped to a single task. NOT for green-field product builds (see mk:autobuild); NOT for auto-invoked workflow orchestration (see mk:workflow-orchestrator).
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 pipeline architecture patterns for ETL/ELT design, orchestration, and data quality frameworks
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。
YAML-based DAG workflow engine with topological execution and failure strategies
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 in Git workflows, conventions, and patterns for modern development teams and AI agents. Activate on: branching strategy, commit conventions, git hooks, pre-commit, husky, lint-staged, trunk-based development, git worktree, monorepo git, PR review workflow, conventional commits, git workflow. NOT for: CI/CD pipelines (use github-actions-pipeline-builder), repository hosting (use devops-automator), multi-agent orchestration (use multi-agent-coordination).
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。