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概要と使いどころ
Integration of advanced IoT protocols for Zephyr RTOS. Covers OpenThread mesh networking, Matter-over-Thread device development, Golioth Cloud SDK patterns, and LoRaWAN basics. Trigger when building smart home devices, wide-area sensor networks, or cloud-integrated hardware fleets.
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。
Classic image manipulation with Python Pillow - resize, crop, composite, format conversion, watermarks, brightness/contrast adjustments, and web optimization. Use this skill when post-processing AI-generated images, preparing images for web delivery, batch processing image directories, creating responsive image variants, or performing any deterministic pixel-level image operation. Works standalone or alongside bria-ai for post-processing generated images.
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。
Schmidhuber's compression progress as intrinsic curiosity reward for learning 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。
CatSharp Scale Galois Connections between agent-o-rama and Plurigrid ACT via Mazzola's categorical music theory
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。
3 parallel agents explore codebase improvements via GF(3) balanced prime geodesics
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。
CatColab Decapodes - Discrete Exterior Calculus for PDE modeling on meshes via Decapodes.jl integration. Model physics equations compositionally with automatic code 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。
Category Theory Tripartite: SICP generic dispatch, CTP Racket categories, CatColab double theories across 3 parallel worldlines with GF(3) conservation
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。
Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.
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 Artificial Life skill combining ALIFE2025 proceedings, classic texts (Axelrod, Epstein-Axtell), ALIEN simulation, Lenia, NCA, swarm intelligence, and evolutionary computation. 337 pages extracted, 80+ papers, 153 figures.
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。
Illustrative Claude Code skill configuration. Use when learning the basic SKILL.md shape; follow the canonical runtime reference for the current field inventory.
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。
Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.
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 SAM Stage 5 Execution has completed and task results need independent verification against acceptance criteria. Dispatches a separate reviewer agent to fact-check implementation outputs and returns COMPLETE or NEEDS_WORK with specific findings and remediation 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。
This skill should be used when analyzing Claude Code session transcripts, reviewing agent performance, finding anti-patterns or tool misuse, mining workflow patterns, running kaizen analysis, debugging agent behavior, or performing session forensics.
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。
Manage Alibaba Cloud DNS (Alidns) using the @alicloud/alidns20150109 TypeScript SDK. Use when working with DNS resolution on Alibaba Cloud, including domain management, DNS record CRUD (A, AAAA, CNAME, MX, TXT, SRV, CAA, etc.), DNS load balancing (DNSSLB), custom resolution lines, DNSSEC, domain groups, batch operations, Cloud GTM (Global Traffic Manager), DNS GTM, GTM Classic, recursive DNS, DNS cache, Public DNS (PDNS), DNS over HTTPS (DoH), ISP cache flush, domain statistics, and resource tagging. Covers all 234 APIs of the Alidns 20150109 version.
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。
Decompose work into stages with physical artifacts and acceptance checklists. Gap detector. Use when opening a WP ≥3h (open-loop/problem-framing) or on explicit request to break into stages. Executes R29 Decomposer role.
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 verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via one-level metric decomposition (Z = P × Q). Triggers: "verify X", "is this true", "find the original source", "where is this number from", "two sources disagree", "is it true X never did Y". Covers five scenarios: (1) basic truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.
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 AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
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。
Rebuild or update the course overview video (about 98 seconds) for this course in videos/overview. Use when running /overview-video, or when asked to refresh the course overview video, add a chapter or feature to it, retake its dark-mode app screenshots, change its copy or timing, add music, or render a new MP4.
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-stack video production from script to rendered video. Combines script generation (optimized for TikTok/YouTube/Reels) with AI-powered video rendering using Remotion, AI voiceovers (Qwen3-TTS/ElevenLabs), music generation (ACE-Step), and browser-based demo recording. Multi-session project tracking with automatic intent reconciliation. Use when "create video", "produce video", "demo video", "product video", or any request to generate AND render video content.
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 white-labeled, client-facing intelligence dashboard — a live, branded reporting surface an agency stands up per client instead of sending static reports. Covers brand auto-extraction from the client's URL, a three-tier build (Basic/Standard/Advanced), an onboarding feature wizard, a 10-page inventory, public-access tradeoffs, and the retention plays that make the dashboard sticky. Use when "client dashboard", "client intelligence dashboard", "white label dashboard", "client portal", "branded dashboard for my client", "agency dashboard", "build my client a dashboard", "give the client a live view instead of a report", or any request to stand up a durable, client-facing reporting surface.
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 a supplied video into a shot-by-shot generation specification, displayed evidence frames, and paired Chinese-English prompt packages. Use for requests such as 视频反推提示词, 视频转提示词, 从视频生成提示词, 视频逆向提示词, 逐镜头反推, 分镜拆解并生成提示词, 反推视频 prompt, or video-to-prompt; also use for Veo, Sora, Kling, Wan, generic prompt adaptation, and bounded generate-compare-optimize loops. Do not use for ordinary video summaries or basic transcription.
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。
Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Active Directory, Kerberos, LDAP, OAuth, enterprise messaging, Windows host forensics, credential material, and lateral-movement challenges. Use when the user asks to trace tickets or tokens, inspect mailbox rules, analyze Windows host evidence, understand an AD trust path, or explain a lateral-movement chain across sandbox-linked nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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 audio content using Chromaprint/AcoustID fingerprinting, Shazam API recognition, and ACRCloud monitoring. Matches music, speech, and ambient audio against fingerprint databases.
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。