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概要と使いどころ
Go development intelligence. 12 domains, 10 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Go code. Domains: pattern, package, error, perf, test, concurrency, interface, arch, idiom, anti, tooling, module. Stacks: web-gin, web-echo, web-fiber, web-stdlib, cli, grpc, database, microservice, cloud-native, data-pipeline. Integrations: arch_system generator.
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。
Node.js & TypeScript development intelligence. 12 domains, 12 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Node code. Domains: pattern, package, error, perf, test, async, arch, idiom, anti, tooling, module, typescript. Stacks: web-express, web-fastify, web-hono, web-nestjs, web-nextjs, web-remix, orm-prisma, orm-drizzle, auth, database, realtime, graphql. Integrations: arch_system generator.
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。
Rust development intelligence. 14 domains, 11 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Rust code. Domains: pattern, crate, error, perf, test, concurrency, traits, arch, idiom, anti, tooling, module, unsafe. Stacks: web-axum, web-actix, web-rocket, web-warp, cli, grpc, database, async-runtime, auth, wasm, embedded. Integrations: arch_system generator.
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。
Ultimate UI/UX design intelligence: an Anti-Slop layer that kills AI tells (em-dash, AI purple glow, generic names, template layouts), tunable Design Dials (variance/motion/density) and Style Modes (functional, minimalist, brutalist, soft-premium, marketing), 161 industry color palettes in a shadcn-aligned semantic token schema, 48 UX Laws, 37 Design Tests, UX Heuristics (Nielsen + Krug), Harvester extraction, Figma & Google Stitch integration, MCP server, Component Generator, and BM25 search across 17 domains + 19 framework stacks. One command = complete design system. Use when designing or reviewing UI/UX, choosing colors/typography, fixing generic AI-looking designs, or building Web App / SaaS / Mobile screens.
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。
Ultimate Design System Intelligence. Manage, extract, replicate, and improve UI design systems. Extracts design tokens visually from existing sites (Harvester v5), manages multi-page tokens, and utilizes MCP servers like Google Stitch and Pencil.dev to generate complete, consistent design systems (Shadcn UI, Halo, Lunaris, Nitro). Use when the user asks to "extract design from a URL", "create a Shadcn design system", "analyze design tokens", "build a design system for", or "copy the UI style of".
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。
Streams audio from Twilio Media Streams over WebSocket to AssemblyAI real-time transcription, extracting speaker-diarized transcripts with word-level timestamps. Triggers entity detection and sentiment analysis via AssemblyAI LeMUR on completed call segments. Results are pushed to HubSpot or Salesforce contact records via their REST APIs.
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 ArgoCD application sync operations with intelligent drift detection and rollback strategies. Uses the ArgoCD gRPC API and ApplicationSet CRD for multi-cluster GitOps deployments.
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。
Summarizes audio content using AssemblyAI's LeMUR (Large Language Model for Audio Understanding) API. Chains the /v2/transcript endpoint with /lemur/v3/generate/summary for contextual audio intelligence.
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 sales enablement strategist for building high-performing sales teams. Use when designing sales training programs, onboarding and ramp plans, sales playbooks, coaching frameworks, certification programs, or competitive intelligence distribution. Covers content strategy, tool adoption, performance measurement, and continuous learning systems. Use for building sales academies, creating enablement content, and optimizing sales productivity.
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 suite for processing YouTube videos. Use this when the user needs to: (1) Extract transcripts, (2) Generate visual infographics, (3) Create audio summaries (TTS) and videos, or (4) Perform full 'kitchen sink' processing of YouTube 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。
Use when navigating or refactoring code with a language server - choosing between semantic (LSP), exact-text (rg), and fuzzy/semantic search; anchoring LSP calls by position; gating degraded results; and disclosing tool substitutions, in any 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。
Use when monitoring online forums (Reddit, HN, ProductHunt) for recurring user pain points, feature requests, and unmet needs. Invoke for market research, pain-point discovery, trend detection, and competitive intelligence. Designed for scheduled execution via CronCreate.
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。
Edit images with precision using ImageMagick 7, sips, transparent-background, and Pillow — with intelligent routing to AI semantic editing (Gemini/nano-banana-pro) for content-aware operations. Covers cropping, resizing, compositing, annotating, format conversion, color adjustment, background removal, batch processing, and montage creation. Triggers on image editing, crop, resize, composite, annotate, remove background, format conversion.
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。
Scaffold editorial documentation sites with the Vellum design system — OKLCH palette, Bodoni Moda / Source Serif 4 / Inconsolata typography, warm or dark space theme, 35 semantic components (state cards, parity matrices, entity dossiers, stat stacks, utterance blocks, editorial notes, entity flows, fact blocks, sub-navigation, source lists, debugger panels, ring visualizations, phase timelines, glossaries, subject-intro, stat-bar, hero-image with lightbox, gallery/masonry grid, audio player with Web Audio API). Static HTML, zero build step. Use when building competitive intelligence pages, personal archives, internal documentation, roadmaps, or multi-page static sites that need a designed, non-generic aesthetic.
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。
Writes and reviews Swift App Intents code that exposes app actions and data to Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence. Use when adding AppIntent, AppEntity, OpenIntent, AppShortcutsProvider, EntityQuery, Focus Filters, AssistantEntity/AssistantIntent schemas, on-screen awareness, LongRunningIntent/CancellableIntent, EntityCollection, SyncableEntity, AppIntentsTesting, or when wiring SwiftData/networked data into intents.
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 AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.
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。
Trademark filing procedures covering EUIPO (EU), USPTO (US), and WIPO Madrid Protocol (international). Walks through pre-filing conflict checks, Nice classification, descriptiveness assessment, mark type decisions, filing basis strategy, office-specific e-filing procedures, Madrid Protocol extension, post- filing monitoring, and open-source trademark policy drafting. Use after running screen-trademark to confirm the mark is clear, when ready to secure trademark rights in one or more jurisdictions.
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。
Apply collective intelligence coordination patterns — stigmergy, local rules, and quorum sensing — to organize distributed systems, teams, or workflows without centralized control. Covers signal design, agent autonomy boundaries, emergent behavior cultivation, and feedback loop tuning. Use when designing distributed systems without a coordination bottleneck, organizing teams that must self-coordinate, building event-driven architectures with shared state communication, or replacing fragile centralized orchestration with resilient emergent 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。
Use when studying Intel VT-x or AMD-V internals: VMCS and VMCB, EPT and NPT, VM exit handling, APIC virtualization, or building a minimal type-1 hypervisor. Not for running VMs: use qemu-kvm.
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。
Step-by-step tutorial for adding a new lightweight SYCL JIT kernel for Intel XPU to sgl-kernel-xpu's python/sgl_kernel/jit module (SYCL source + Python wrapper + tests + benchmark)
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。
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
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 for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.
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。
Top 5 local competitor analysis — menu, pricing, review, social, and SEO comparison with positioning gap identification
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。
On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".
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。