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概要と使いどころ
Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.
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Generate visual hierarchy diagrams of agent system showing levels and delegation. Use for documentation or onboarding.
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。
Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems
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。
Check agent configuration coverage across hierarchy levels and phases. Use to ensure complete agent system coverage.
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 context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.
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。
Send and receive messages between AI agents using AI Maestro's messaging system. Use this skill when the user asks to "send a message", "check inbox", "read messages", "notify [agent]", "tell [agent]", or any inter-agent communication.
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Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.
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A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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.
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Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
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。
Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.
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Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.
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Add a new tool to the backend OpenAI function calling system. Use when user mentions "new tool", "add tool", "backend function", "agent capability", or wants to extend what the AI agent can do.
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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.
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Score your Claude Code environment on a fun 0-10 ladder (Tourist → Swarm) calibrated to the quickstart's 4 systems — Context, Skills, Integrations, Orchestration. Two front doors - an interactive quiz (zero setup, shareable) or a silent scan of your repo. Outputs a next-level roadmap plus a branded HTML dashboard or a paste-anywhere ASCII card. The default mastery skill of this quickstart. Triggers - "level", "what's my claude code level", "claude code level quiz", "assess my setup", "how good is my setup", "claude code maturity"
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Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.
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。
One line — used in CLAUDE.md system-reminder and the skill catalog.
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Organizational Development Manager (R31): guides the subject from an organizational change request (self/team/organization) to a concrete first step from SE/SM/PE. Step 0 — system type classifier (MIM.M.030, 7 questions). Step 3 — routing-table (MIM.M.031).
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Weekly review of IWE work culture (element 14 — System Evolution). Runs during Week Close.
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。
Generate a PR description following the repository's pull request template. Use when the user asks to create, draft, or generate a PR description for a pull request. The skill automatically locates the PR template in the target repository and fills it in based on the git diff.
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Review touched Incan source for Python-quality readability, idiomatic Incan structure, dogfooding integrity, public API polish, and source-level anti-patterns. Use for `.incn` stdlib, examples, language-surface implementations, or when the user asks whether Incan code reads like well-written Python rather than Rust-shaped scaffolding.
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Review touched code for maintainability smells such as duplication, awkward indirection, suspicious clones, dead code, poor naming, and Boy Scout cleanup opportunities. Use when a broad review needs a dedicated maintainability pass or when the user asks for code-smell review specifically.
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Orchestrate a broad Incan review using specialized reviewer roles, parallel slice reports, and a canonical merged review report. Use when the user asks for delegated review, multi-agent review, or when a broad dirty worktree should be reviewed by specialized passes rather than one generic reviewer.
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Review an Incan RFC document for formatting, structural, and content issues before GitHub submission. Use when the user asks to review an RFC, check an RFC for problems, prepare an RFC for submission, or says /review-rfc.
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。