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概要と使いどころ
Chinese journal matcher — performs L3 6-dimensional matching (keywords, cited direction, trend, solicitation, institution, cross-discipline), calculates conditional acceptance probability with review cycle estimation, and outputs tiered submission strategy (aspiration-stable-safety) using 4-layer weighted ranking.
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。
International journal scout — searches candidate SCI/SSCI/EI/Scopus journals via Wanfang foreign journal API, performs L1 deterministic rejection screening, and L2 citation fingerprint analysis.
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。
International journal matcher — performs L3 6-dimensional matching for SCI/SSCI/EI/Scopus journals, computes conditional acceptance probability with review cycle estimation, applies 4-layer weighted ranking, and outputs tiered submission strategy including CAS warning screening and Chinese scholar friendliness analysis.
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 Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.
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 the user asks to create, edit, rebuild, or refine editable diagrams in `.drawio` / diagrams.net format, especially for academic figures such as system architecture diagrams, technical roadmaps, workflow diagrams, method pipelines, module relationship diagrams, and experiment flowcharts. Use this skill when the user wants publication-ready outputs with both an editable source file and a PNG export, or when an existing figure image needs to be rebuilt into a clean editable academic-style diagram.
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。
Display the current VCSDD pipeline status for the active (or specified) feature. Shows phase, mode, sprint count, open findings, pending proofs, and the traceability summary.
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 this skill when applying VCSDD to Python projects. Provides hypothesis property testing patterns, mutmut mutation testing, and pytest integration for the VCSDD 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。
Use this skill when committing VCSDD pipeline artifacts to git. Provides conventional commit message format, phase tag patterns, and atomic staging strategies.
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。
Initialize a VCSDD feature pipeline. Creates the .vcsdd/features/<name>/ directory tree, sets mode (strict|lean), and activates the feature. Must be run before any other VCSDD command.
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 this skill when applying VCSDD to TypeScript/JavaScript projects. Provides fast-check property testing patterns, Stryker mutation testing, and vitest/jest integration for the VCSDD 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。
Run an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.
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。
Cut a hyalo release end-to-end — verify/bump the workspace version, rotate the changelog with hyalo itself, sync the winget fork, publish the GitHub release with curated + auto-generated notes, and watch the pipeline. Use this skill whenever the user wants to release a new hyalo version, cut/tag/publish vX.Y.Z, says /release, or says things like "ship 0.19.0", "cut the release", or "publish the release".
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 rating a source with the NATO Admiralty Scale, the user asks "is this reliable?" / "rate this source", or the tradecraft pipeline calls for source assessment before publishing. Reliability A-F, credibility 1-6.
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 the user asks "what TLP should this be?", applying TLP markings to a finished product, or the tradecraft pipeline calls for TLP determination before sharing. Covers TLP v2.0 (CLEAR / GREEN / AMBER / AMBER+STRICT / RED).
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 the user asks about initial access brokers (IABs), the access-listing market, ransomware-feeding-IAB pipelines, specific broker handles, or how access is priced and packaged. Self-updating knowledge cell.
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 assigning a confidence level to an analytical judgment, the user asks "how confident are we?" / "what is the confidence on X?", or the orchestrator's tradecraft pipeline calls for a confidence level before publishing. Provides three named levels (High, Moderate, Low) with 0-100 score ranges aligned to STIX, and the rule that confidence cannot exceed what the weakest load-bearing claim supports.
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 phrasing a forward-looking statement, the user asks "how likely is X?" / "what's the likelihood?", or the tradecraft pipeline applies a probability yardstick to a finished product. Standardised likelihood language across all products.
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。
Indicator pivoting methodology — how to use one known indicator to discover related infrastructure across the IOC graph. Decision tree by indicator type with concrete `/lookup-*` commands per pivot, a worked multi-hop example, pivot-quality scoring, and routing into the rigor pipeline. Use when the user asks "what else is connected to this IP / domain / hash / cert / actor?", needs to expand a single seed IOC into a campaign cluster, or wants the canonical reference for graph-walking IOCs.
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。
Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native 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。
Game art principles. Visual style selection, asset pipeline, animation workflow.
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 modern full-stack web applications with Next.js (App Router, Server Components, RSC, PPR, SSR, SSG, ISR), Turborepo (monorepo management, task pipelines, remote caching, parallel execution), and RemixIcon (3100+ SVG icons in outlined/filled styles). Use when creating React applications, implementing server-side rendering, setting up monorepos with multiple packages, optimizing build performance and caching strategies, adding icon libraries, managing shared dependencies, or working with TypeScript full-stack projects.
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。
Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.
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 robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend 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。
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project 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。