754 件 ・ 関連度順
概要と使いどころ
Bootstraps a new project end to end: research, design, plan, scaffold, implement, docs. Explicit invocation only. NOT for autonomous builds (mk:autobuild); NOT for existing-repo work (mk:cook).
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。
Auto-invoked 7-phase workflow for complex-feature intent. Includes fasttrack mode for pre-approved specs. NOT for explicit user-invoked single tasks (see mk:cook); NOT for green-field autonomous builds (see mk:autobuild); NOT for simple bug fixes (see mk:fix).
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。
Session-persistent browser automation via Playwright CLI — form filling, screenshots, data extraction, multi-step flows. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information across multiple requests. NOT for AI-driven long-autonomous flows (see mk:agent-browser); NOT for generating .spec.ts E2E code (see mk:qa-manual).
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。
Spec-driven manual QA testing and Playwright E2E code generation. Orchestrates browser skills to navigate apps like a human tester, producing structured test reports or production-ready .spec.ts files. Always prompts user for credentials — never guesses or stores auth. Use for QA testing, E2E generation, login flow testing, or exploratory testing from a URL. NOT for AI-autonomous flows (see mk:agent-browser).
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 creating a new project from scratch — orchestrates the full journey from idea to running code. Research → design → plan → scaffold → implement → docs. Explicit invocation only — never auto-activates. CLI = project infrastructure (.claude/), bootstrap = application code + full pipeline. NOT for autonomous multi-hour builds of specified products (see mk:autobuild); NOT for single-task feature work on existing projects (see mk:cook).
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。
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
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 drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
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。
Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded 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。
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).
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。
BDI (Beliefs-Desires-Intentions) agency framework for designing autonomous agents with mental state architectures
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。
Logic-based agent programming language implementing BDI architecture for practical autonomous agent 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。
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。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.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。
Identifies patients with adrenal tumors on CT who should undergo dexamethasone suppression test to evaluate for autonomous cortisol co-secretion. Triggers include when a clinician finds an adrenal tumor on CT and asks 'Should I test for cortisol co-secretion?' or is planning AVS for a tumor >3.0 cm.
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。
Recommends performing a dexamethasone suppression test to evaluate for autonomous cortisol secretion in patients with primary aldosteronism and adrenal adenoma. Triggered when a clinician considers whether to perform a dexamethasone suppression test in a patient with PA and adrenal adenoma.
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。
Autonomously navigate websites and extract structured data across pages. Use when the task requires navigation or no suitable ready-made workflow or data provider covers it.
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。
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
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。
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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。
Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.
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。
仕様書/実装計画書をもとに、計画を見出し単位のステップへ分解し、各ステップで「テスト作成→レビュー→収束ならコミット」「実装→レビュー→収束ならコミット」(必要なら「ドキュメント修正→レビュー→収束ならコミット」)を自律反復する。ユーザーから自律進行(止まらず最後まで進める・ステップを自動で回す等)の明示的な指示があったときだけ使う。単に計画に沿って実装してほしいだけの依頼(自律進行の指示を伴わない)では使わない。レビューは codex-review-loop、コミットは commit に委譲し、仕様整合(参照の記法・用語規約)はエージェントが直接確認する。