Results for “guide”

An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Guides the practical path from 'we have no design system' or 'we have a mess' to a real, functional design system. Component inventory, design token extraction, headless UI adoption (Radix, React Aria, Headless UI), migration order of operations, and the assessment-to-rollout pipeline. Activate on 'design system from scratch', 'bootstrap design system', 'no design system', 'design system migration', 'headless UI adoption', 'component audit', 'design token extraction', 'design system strategy'. NOT for building specific components once a system exists (use design-system-creator), not for generating token files/configs (use design-system-generator).

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Detects and fixes color contrast violations using WCAG 2.1 guidelines and perceptual analysis. Expert in contrast ratio calculation, color blindness simulation, and providing accessible alternatives. Activate on "check contrast", "color accessibility", "WCAG audit", "readability check", "contrast ratio", "hard to read", "can't see text". NOT for general color theory (use color-theory-palette-harmony-expert), brand color selection (use web-design-expert), or non-visual accessibility (use ux-friction-analyzer).

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Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs.

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End-to-end Coval accent testing workflow. Creates one persona per accent (each using a distinct accent voice and mirroring your Standard Customer behavior), launches one run per accent against the same voice agent + test set + metrics, polls for completion, builds a per-persona comparison table from the results, and creates the saved multi-run report (grouped by Persona) via the public API. Use when a user wants to follow the Testing Across Accents cookbook (https://docs.coval.dev/guides/testing-across-accents) without doing each step by hand.

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End-to-end Coval audio-quality testing workflow. Launches one run per audio-robustness scenario against the same voice agent + test set + metrics, polls for completion, and produces the multi-run report URL grouped by Persona. Use when a user wants to follow the Testing Across Audio Qualities cookbook (https://docs.coval.dev/guides/testing-across-audio-qualities) without doing each step by hand.

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Set up and connect a new Coval agent for evaluation. Guides through agent type selection, endpoint configuration, system prompt, and default resource attachment. Use when user says "set up an agent", "connect my agent", "create an agent", "add an agent", or "configure agent endpoint".

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Design and create a simulation persona for testing an AI agent. Guides through use case selection, voice and language configuration, behavior prompt crafting, and interruption calibration. Use when user says "create a persona", "design a persona", "set up a test persona", "configure simulation persona", or "build a caller profile".

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End-to-end Coval adversarial / red-team testing workflow. Builds one adversarial test set (12 core attack vectors plus legitimate controls, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".

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Operating guide for the durable Autonomous Work Loop task fleet. Use when adding work to, coordinating, or working an Autonomous Work Loop campaign, or when calling the work_loop MCP tool.

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The gate and path for any question an agent wants Arman to decide. Use when about to ask him anything, ending a turn on a question for him, writing 'needs Arman's decision', marking work blocked on him, or on /ask-arman. NOT for a step only he can perform (use a guided session).

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The interview primitive for putting open decisions to an owner. Use on 'grill me', 'stress-test this', or 'what do you need from me before you build this', and whenever a plan, scope, spec, vision, or question ledger needs owner rulings. NOT for steps only a human can perform (use a guided session).

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Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, node materials, compute shaders, post-processing, and WGSL integration. Use when working with Three.js WebGPU, TSL shaders, or GPU compute pipelines.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Build a LiveAvatar integration end-to-end — assesses the user's existing stack, recommends the optimal path, and guides implementation. Use when: (1) Building a new LiveAvatar integration, (2) Adding a real-time avatar to an app or site, (3) Connecting LiveAvatar to an existing AI pipeline, (4) User mentions LiveAvatar, real-time avatar, interactive avatar, conversational avatar, or lip-sync avatar, (5) Deciding between Embed, FULL Mode, and LITE Mode, (6) Migrating from HeyGen Interactive Avatar to LiveAvatar.

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Guide the user through starting a new web application project — elicit requirements (MoSCoW scoping), create architecture and requirements documents, break work into tasks, and register them as a GitHub Project kanban board. Use when the user says "新しいWebアプリを作りたい", "Webアプリの立ち上げを手伝って", "start a new web app project", "プロジェクトの要件を整理したい", or "GitHub Projectにタスクを登録したい".

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Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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実際の本番アプリケーションに基づくプロジェクト固有のスキルテンプレートの例。

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Guides a first-time user through onboarding and picks the right workflow. Use when the user is new, just cloned or installed the studio, has no idea or engine yet, asks where to begin, or wants to bring in an existing product.

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Guides the user through finding and choosing a product concept. Use when the user wants product ideas, a new concept, a SaaS application jam idea, or to explore other directions, including dropping the current idea and starting over.

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Researches a question to something usable — a verdict with a flip condition, a versioned playbook or a graded report — every claim tagged by evidence. Trigger on which X should I pick (even from pasted pages), is Y worth it, compare A vs B, go/no-go, research this, deep research, fact-check a doc, write or merge a reference guide or playbook, grade a research report, read a bot-blocking source (Reddit, YouTube, X), or map an unfamiliar repo before contributing (entry points, flows, where to change X); or say researchscribe (verdict, playbook, research, verify, sources, map, refresh). Code review is engineering tooling's; fiction canon is lorescribe's; messages commscribe's; prompt model picks promptwright's; skill niches skillwright's; config rigwright's; brands brandscribe's.

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Directs game audio and judges it by numbers — sound palettes, SFX and music briefs, Godot import settings, licence-checked generator briefs, and measured file checks (loudness, peaks, clipping, silence, loop seams). Trigger for game sound design or an audio style guide; an SFX or music list; a loop that clicks; sounds too loud or uneven; what LUFS a game should hit, or normalizing to it; checking WAV or Ogg files before import; or say soundsmith (direct, brief, audit, test). Direction, not DSP; generating a sound starts here. Running ComfyUI is comfyrunner's; audio code godotsmith's; transcription whisperrunner's; canon lorescribe's; pixel art pixelsmith's.

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Directs pixel art that must read at several zoom scales at once — per-scale palette and silhouette rules, terrain-versus-unit contrast, a one-scene look test, and briefs for an artist or image generator. Trigger when a game renders one world through zoom bands or levels of detail, a pixelated 3D or 2.5D band with billboards included; when a sprite, unit or icon vanishes against terrain zoomed out; for a cross-band look test or pixel-art style guide; or say pixelsmith (test, brief, audit, 3d). Works from a described scene. Drawing is an art tool's job; rendering and LOD code the game's engineering; a brand palette brandscribe's; game audio soundsmith's.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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