「nat」の検索結果

Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria FIBO models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.

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。

Forth/Factor/Joy: stack-based concatenative programming where composition replaces application.

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 research corpus covering 40+ agentic coordination protocols and ecosystems (MCP, A2A, ANP, UCP, ACP, AGNTCY, NANDA, FIPA, WoT, etc.) with emphasis on identity architectures, governance bodies, and interoperability convergence patterns.

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。

Formal oracle for abductive inference that selects the minimal hypothesis best explaining a set of observations. Provides three sub-oracles: MCMC via monad-bayes, Gemini for natural language, and propagator networks for constraint domains. Use when performing hypothesis selection, explaining observations, or solving inference-to-best-explanation problems.

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 reviewing, designing, or improving a workshop, lesson, training session, facilitation plan, talk, or educational explanation. Use it when the user provides a topic, concept, or explanation and wants better framing questions, curiosity hooks, opening prompts, discussion questions, learner reflection prompts, or ways to make participants think before being taught.

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 creating, grooming, planning, or closing a backlog item. Bridges backlog items to SAM planning with issue, project, and milestone tracking against the configured backend. Activates on interactive browsing with no arguments, loading an item by issue reference or title match to run grooming and SAM planning, autonomous unattended runs that substitute evidence-derived decisions for clarifying questions, a quick path for one-file fixes where full grooming is disproportionate, dismissing an item without completion under a required reason (duplicate, out of scope, superseded, wontfix, blocked), marking an item done with an evidence trail and required summary, or initializing GitHub tracking infrastructure for a project. Stops when the item already has a plan or is blocked on missing information.

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。

Creative reasoning framework for ill-defined problems where conventional solutions are suboptimal. Use when the problem has ambiguous goals, vast solution space, no single correct answer, or requires innovation. Implements three paradigms — combinational (novel combinations of familiar ideas), exploratory (expand solution space boundaries), transformative (alter fundamental constraints). Do not use for well-defined problems, mathematical puzzles, or tasks requiring convergent reasoning.

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。

JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot.

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。

AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.

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。

Coordinate multiple AI agents and skills for complex 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。

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。

Synchronize existing READMEs with local Git code changes and every language edition. This skill should be used for update readme, sync readme, 更新 README, 更新项目文档, 同步多语言 README, README を更新して, README 업데이트해 줘, обнови README, or /readme-update. Ask for an exact project version or explicit unchanged choice before editing; inspect committed, staged, unstaged and untracked changes, weave each change into its natural position (never just the beginning or end), keep the flowchart current, and keep all language editions identical apart from the language.

Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.

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。

Change AuthCrunch refresh/logout HTTP routes, secure cookies, native JSON credential transport, browser Web Locks, continuation pages, fresh login, or cross-tab session 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。

Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.

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。

Plan and produce a complete product launch marketing package — landing page copy, email sequences, ad campaigns, press release, social posts, and launch timeline. Orchestrates all other kai skills into a coordinated launch. Use when "product launch", "launch campaign", "go-to-market", "GTM plan", "launch marketing", "we're launching", "prepare launch materials", or any request to coordinate marketing for a new product, feature, or major update.

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 prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. 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。

Bruno is an open-source, offline-first API client that stores collections as plain-text .bru files on your filesystem. It serves as a privacy-focused, git-friendly alternative to Postman and Insomnia with no cloud sync and no account required.

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 smartcrop when an agent needs to choose a sensible crop automatically instead of center-cropping every image. The workflow scores visual saliency, returns crop coordinates for a target aspect ratio, and hands those coordinates to an image pipeline that renders the final thumbnail or social card.

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。

AgentQL is TinyFish's web automation and extraction toolkit that layers a natural-language query system on top of live sites. Its Python and JavaScript SDKs integrate with Playwright and let agents target data and interface elements without depending on brittle CSS selectors alone.

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。

Generates Airbyte source and destination connector configurations using the Airbyte API /v1/sources/create and /v1/destinations/create endpoints. Validates connection specs against the Airbyte Protocol.

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。

Orchestrates translation of motion designer video specifications into working Remotion code by coordinating specialized agents. Acts as pipeline coordinator that delegates to remotion-scaffold, remotion-animation, remotion-composition, and remotion-component-gen. Use when you have a complete VIDEO_SPEC.md and need full Remotion implementation.

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。

Plan and implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK can be answered directly.

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。

OS post-exploitation after a stable host foothold: quiet host recon, host-native persistence (Windows Run/tasks/services/COM/WMI and Linux SSH/cron/systemd/SUID), long-term C2 (Sliver primary, Mythic/Havoc optional), targeted collection and exfil, and engagement cleanup. Does not own AD/cloud/K8s native persistence, Ligolo pivoting, LSASS dumping, or EDR bypass. Operator selects objectives; success is a live callback, verified persistence, delivered loot, or restored host — not generating an unused implant.

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。