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概要と使いどころ
Testing and debugging for Zephyr RTOS. Covers the Ztest framework for unit and integration tests, the Twister test runner for automated HIL/SIM validation, and advanced tracing/debugging techniques (SystemView, Thread Analyzer, Stack analysis). Trigger when writing unit tests, setting up CI/CD pipelines, or analyzing system performance and latency.
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 system management for Zephyr RTOS. Covers West workspace initialization, manifest management, Sysbuild multi-image builds, Kconfig symbols, and CMake integration. Trigger when setting up workspaces, configuring builds, or troubleshooting build-time errors.
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 name: cognitive-surrogate description: Layer 6 Barton Cognitive Surrogate System - build, train, and validate psychological models that predict and generate authentic responses matching a subject's cognitive patterns version: 1.0.0 trit: 0 # Ergodic/coordinator role in GF(3) triadic system ```
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。
Bridge layer connecting AlgebraicJulia/Catlab.jl to plurigrid/asi. Wires ACSets (attributed C-sets), wiring diagrams, decorated cospans, and the AlgebraicJulia ecosystem (AlgebraicDynamics, AlgebraicPetri, AlgebraicRewriting, Decapodes) into the ASI skill graph. ACSets generalize relational databases with categorical semantics; every diagram, network, and model in the ecosystem is an ACSet.
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。
Chemical reaction network modeling with Catalyst.jl. Autopoietic systems, unit-aware ODEs, and interacting subsystems.
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。
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
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 aptos-ecosystem
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
Research community usage patterns, real-world gotchas, and client compatibility for a specific known library, tool, or protocol feature. Use when a technical-researcher orchestrator needs community-sourced evidence about a named library or feature — bug reports, workarounds, compatibility gaps, and patterns from issue trackers and discussions. Distinct from the broad ecosystem-researcher agent — this skill targets a KNOWN entity and mines what real users have actually experienced, not what exists in a domain.
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 implement agentic AI workflows and patterns. Covers ReAct, planning agents, tool use, memory systems, and multi-agent orchestration. Use when building autonomous AI agents, implementing complex task automation, or designing intelligent workflow 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。
Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent 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。
Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools, disallowedTools, permissionMode), MCP integration, system prompts (CLAUDE.md, appendSystemPrompt, outputStyle), cost tracking, todo tracking, structured outputs, hosting patterns, plugins, and SDK branding guidelines. Assists with building custom agents, configuring SDK options, and troubleshooting SDK issues. Delegates 100% to docs-management skill for official 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。
AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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。
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration. Use when: the task directly matches context manager responsibilities within plugin agent-orchestration. Do not use when: a more specific framework or task-focused skill is clearly a better match.
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。
Discovers all Claude Code agents in the system including built-in, plugin, project, and user-level agents. Use when you need to find which agents are available, understand the agent ecosystem, or prepare agents for Actoris registration.
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 error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.
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 provider to agentconfig.org's comparison system. Use when integrating a new coding assistant (e.g., Cursor, Claude Desktop, GitHub Copilot alternative) with proper type system updates, implementation data, UI components, tests, and 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。
Test an uncertain Anabasis v2 path at the smallest real layer that can decide it, before a paid round is asked to: one fact from a log or a dry run, a walk of one value across the layers a change touches, a replay of every recorded log through this tree's readers, a whole round rehearsed on a seeded copy of a campaign with a script in each model seat (`bun run runs seed`, `bun run runs rehearse`), or one live seat from a seed. Use when the operator asks whether a change will work in the real system, to simulate or rehearse a round, to stand again in a position a past run stood in, what a campaign of a given size can read, or what a kill or a stop leaves behind. Not for reading a finished run (whole-run-investigation) or starting a paid one (run-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。
Review touched subsystems for layering, crate boundaries, single-source-of-truth rules, registry-driven behavior, and compiler/runtime separation. Use when a broad review needs a dedicated architecture pass or when the user asks for architectural review specifically.
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。
Write a single piece of marketing content using the marketing harness frameworks and quality gates. Supports blog posts, LinkedIn articles, emails, cold outreach, press releases, ad copy, and TikTok scripts. Automatically loads the right framework, skill contract, and persona. Use when "write a blog post", "draft an email", "LinkedIn article", "cold outreach", "write ad copy", "press release", "TikTok script", or any single content creation request. For building a complete email system, use /kai-email-system instead.
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 an exhaustive first-growth-hire distribution operating system across B2B and B2C channels: LinkedIn, events, AI outbound, AEO, blogs, long-form writing, YouTube, webinars, X, influencers, AI UGC, organic TikTok, paid social, sponsorships, partnerships, lifecycle, referral, and community. Use when "growth hacker", "first growth hire", "distribution hire", "cover every channel", "channel hacking", "0 to ARR growth system", "growth operator", "growth hacker OS", or any request to fan out channel operators and plug the result into Kai workflows.
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 a complete cold email outreach system — ICP definition, sequence architecture, personalization strategy, and batch-produced email sequences with CAN-SPAM/deliverability compliance. Use when "cold outreach", "cold email sequence", "outbound campaign", "prospecting emails", "sales outreach", "build outreach sequence", or any request to create systematic cold email campaigns for lead generation.
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 system that makes HTML output look like a professional consulting deliverable instead of default AI styling. Use WHENEVER generating any HTML document — report, analysis memo, proposal, research summary, dashboard, artifact — and when the user says 「HTMLで資料」「HTMLでまとめて」「レポートにして」「コンサル風に」「資料っぽく」「デザインがダサい」 or asks to restyle existing AI-generated HTML. Replaces boxes, borders, gradients, emoji and rainbow colors with whitespace-driven, typography-first, print-ready (A4) design.