「surface」の検索結果

Create and edit 3D meshes in Blender — primitives, hard-surface modeling, mesh operators, modifier stacks (Bevel, Subdivision, Boolean, Mirror, Array, Solidify), bmesh-level edits, retopology basics. Use whenever the user asks to "make/model/create/build a 3D object", "shape/sculpt this", "add a cube/sphere/cylinder/etc.", "extrude/inset/bevel this face", "add a modifier", or any geometry-creation request that isn't a wireframe trace. Make sure to use this skill even if the user does not say "model" — also covers "make a sword", "build a chair", "add a door", "carve out a hole". Pairs with blender-materials for look-dev and blender-pro-workflow for full pipelines.

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。

Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.

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。

When a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

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。

Surface beliefs, assumptions, stories, and values shaping a system. Use when deeper mental models need examining with care and evidence.

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。

Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.

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。

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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。

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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。

Summarize sprint status and surface risks. Use when the user says "check sprint status" or "show sprint status"

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。

Trigger.dev client/frontend surface: subscribe to runs in realtime (runs.subscribeToRun and the @trigger.dev/react-hooks hook useRealtimeRun), consume metadata and AI/text streams in React (useRealtimeStream), trigger tasks from the browser (useTaskTrigger, useRealtimeTaskTrigger), and mint scoped frontend credentials with auth.createPublicToken / auth.createTriggerPublicToken. Load when wiring a frontend (React/Next.js/Remix) or backend-for-frontend to show live run progress, status badges, token streams, trigger buttons, or wait-token approval UIs. NOT for writing the backend task itself (streams.define / metadata.set is trigger-authoring-tasks territory); this is the consumer side.

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Independent review loop run before delivery in code work. An independent reviewer (Codex, or fresh Codex-style subagents) reads the whole PR against the approved outcome; the lead fixes in-scope functional findings and re-reviews until none remain. Its differentiator is independence and exhaustiveness — a reviewer that fails differently from the authors, run to clean — distinct from the recursive-refinement ladder. Invoked by the team lead from the unified pre-ship gate (code-mode and /swarm:refine) after the ship steps.

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。

Kolu's project-wide default for filesystem monitoring is `@parcel/watcher`. Reach for this skill when adding or modifying any code that watches files or directories — recursive subtree watching, single-file observation, fs.watch alternatives, chokidar replacement, inotify/FSEvents/watchman backend selection, ignore globs, watcher debouncing, or refcounted shared subscriptions. Covers backend dispatch, the watchman invocation path, ignore handling, post-install reconciliation, and the failure modes Kolu's logger surfaces.

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Drive CI with `odu` — one shared service, reached from a terminal (`odu surface`), from an agent (`odu mcp`), or from a browser. Trigger when the user asks to "run CI", "run the pipeline", "re-run a check", to run named lanes or recipes (e.g. "run fmt and nix", "just the e2e lane", bare selectors like `fmt`/`nix`/`e2e`), or names a recipe by `<recipe>@<platform>`. This skill — not a repo's local `just ci` / `just <recipe>` — is how an odu-run request is served, whichever face you are on.

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Verify Claude, Codex, and Grok availability plus Director guidance, relay, agents, and skills. Audit optional hooks only when selected. Use after installation or when a native surface misbehaves.

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。

React and Next.js frontend development standards for Retail-Agentic-Commerce. Use when creating or modifying UI pages, components, hooks, styling, tests, or Apps SDK widget browser behavior in src/ui or related frontend surfaces.

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Preflight that resolves which private key(s) to use for the warp-deploy chain across every protocol in the route (EVM / Sealevel / Cosmos / Starknet / Tron). For each protocol the user supplies either a specific key spec (GCP secret name, env var, or keystore path) or a candidate list of GCP secret names for the agent to surface and the user to pick. The agent never enumerates GCP secrets on its own and never reads secret values without an explicit user-approved pick. Persists the resolution to `~/.hyperlane/key-contexts/ticket-id.yaml` for downstream warp-deploy skills to auto-load.

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Maintain the durable, per-ticket run log that warp deploy/update skills append to at every milestone — a Linear-document-by-title primary with single-writer discipline and a local-file fallback, a machine-parseable per-chain row plus prose entry shape, and a surface-the-URL-as-proof hard gate. Referenced by any warp deploy/update skill that must leave a retrospective trail across worker restarts.

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Drafts due diligence checklists for U.S. corporate transactions (M&A, asset purchases, investments, JVs, restructurings). Covers corporate structure, financials, tax, contracts, IP, litigation, employment, benefits, insurance, and environmental compliance. Surfaces change-of-control triggers, consent requirements, and red flags. Use when preparing for deal closing, board presentations, or investor disclosure.

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Expert guide for modern WebGPU and WebGL 3D graphics in the browser using Three.js (WebGPURenderer + TSL), Babylon.js, PlayCanvas, React Three Fiber (R3F), and 3Dviz spatial reasoning — covers object craft across 3 scales, surface continuity at joins, physical interaction envelopes, grounded scientific visualization, KTX2/Meshopt optimization, and 3D visual anti-slop / Panduan ahli grafis 3D web, WebGPU modern, dan penalaran spasial 3Dviz.

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。

Shadows and layering that lift elements above the surface. Use when designing cards, dropdowns, modals, tooltips or any floating element.

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。

Applying motion study and time analysis to household work. Based directly on Lillian Gilbreth's *The Home-Maker and Her Job* and *Cheaper by the Dozen*, this skill covers the therblig-motion catalog, task decomposition, batch processing, parallelism, routine chart design, and the ergonomics of work surfaces. Use when diagnosing why a household is running out of time, designing a weekly routine, teaching task sharing to children, or reducing the friction of any repeated household task.

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Analyzing patterns of continuity and change over time in historical contexts. Covers periodization (how and why we divide history into eras), identifying turning points, measuring rates of change, and recognizing long-term patterns and trends. Use when analyzing temporal structure in history, debating period boundaries, or assessing whether a moment represents genuine transformation or surface-level disruption.

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Study of human activities, spatial patterns, and social processes on Earth's surface. Covers population and migration, cultural diffusion and landscapes, urbanization and city systems, economic geography and development, political geography and borders, and social/identity geographies. Use when reasoning about why people live where they do, how cultures spread, how cities grow, how economies are spatially organized, or how power operates through space.

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Audit a skill by running a paired probe — the same task once with the skill loaded and once without — segment both traces into goal-directed phases, align phases, and emit a SIP report (surface anchoring, template copy, excess planning, task recovery, off-task artifact). Use whenever a skill is created, modified, or proposed for retirement. Pass-rate is BLIND to most skill effects: CTA (arxiv 2605.11946v1) shows a single skill can produce 522 measurable behavioural changes across 49 tasks while pass-rate moves only +0.3%. Triggers: "audit this skill", "is this skill helping", "retire skill", "before shipping skill", "behavioural impact of skill X", or any skill review event.

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Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.

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。