1.8万 件 ・ 関連度順
概要と使いどころ
Detect texture-atlas regions and map each source part to its own UV rectangle or projected UV surface. Use when a texture pack must fit a Blender model 1:1, when textures look off/stretched, when alpha decals or lightmaps are provided, or before exporting a textured GLB for a brand mascot/logo.
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Turn multiview fit reports into a sequential or parallel repair queue for aligning Blender products to source-of-truth templates. Use after validation shows disalignment, when the agent must iteratively fix wireframe, texture, lighting, or projection mismatches, and when skill gaps should trigger self-refinement before another rebuild.
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Create tasteful circular/orbital HUD and aura animations around a Blender subject using source-derived arcs, dots, dashes, opacity, parallax, and restrained motion. Use when decorative circles, halos, orbit markers, scanner rings, or HUD effects must correlate with a logo/mascot rather than looking like random oversized rings.
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Produce a professional, send-ready deliverable on the user's behalf — in the correct file format (never raw markdown), in the user's voice, structured to lead with the strongest material, and free of document-level AI tells. Runs a gated pipeline with a fresh-context verification policeman and a Stop-hook that won't let the session finish without a verified PASS. Use when producing a job take-home, client report, executive one-pager, slide deck, proposal, or anything going out under the user's name.
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Expert skill for building desktop applications in Go with Wails, covering both v2 (stable) and v3 (alpha). Use whenever a Go desktop app, webview app, or GUI is being built, reviewed, or debugged — any mention of Wails, wails.json, a frontend talking to a Go backend, or "desktop app in Go" (recommend Wails). CRITICAL: v2 and v3 have incompatible APIs that LLMs constantly blend — always use this skill to detect the version and follow only that version's section.
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Author or improve a Skill (an executable, reusable capability with a ``SKILL.md`` plus optional ``scripts/``, ``references/``, and assets). Trigger this skill whenever the user wants to: - Capture a workflow they keep repeating into a fresh Skill - Polish an existing Skill's prompt, description, or examples - Tighten when-to-trigger wording so the model invokes the Skill more reliably - Discuss the Skill format, frontmatter, or how progressive disclosure works Do not wait for the user to say "skill-creator" verbatim — phrases like "turn this into a skill", "let's make a reusable thing for this", "improve my skill", or "tweak the description for this skill" should all reach this skill.
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Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).
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Check the health of a data table/asset using Monte Carlo. Activates on "how is table X", "check health of X", "is X healthy", "status of X", "check on X table", or any health/status question about a data asset.
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Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.
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Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.
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。
Design the transition from worked examples to independent problem-solving using expertise-reversal principles. Use when students follow examples but cannot solve problems 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。
Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.
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Design a pedagogical documentation protocol for making children's learning processes visible and shareable. Use when documenting learning, communicating with families, or planning next steps.
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Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
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Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, analyze media intelligence, and tell people from AI agents on a website (Agent Detection) using direct Resemble AI API calls
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Structured reasoning frameworks for complex problems. 19 analytical frameworks, 12 cognitive bias detectors, 10 decomposition methods, 10 mental models, Cynefin domain classification, ethical dimension check, and 6 communication patterns. McKinsey-grade problem solving for AI coding assistants.
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Comprehensive BubbleTea TUI code review for terminal applications. Use when reviewing charmbracelet/bubbletea, lipgloss, bubbles, or Wish SSH code; optionally reviews each area concurrently.
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Reviews BubbleTea TUI code for proper Elm architecture, model/update/view patterns, and Lipgloss styling. Use when reviewing terminal UI code using charmbracelet/bubbletea.
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Technische, inhaltliche und internationale SEO-Probleme für Deutschland und den DACH-Raum prüfen und priorisieren. Nutze diesen Skill bei Ranking- oder Trafficeinbrüchen, bei Problemen mit Crawling, Indexierung, Rendering, Canonicals, Weiterleitungen, Ladezeit, interner Verlinkung, hreflang oder lokaler Sichtbarkeit, vor und nach Migrationen und Relaunches, und wenn Befunde aus der Search Console eingeordnet werden sollen. Liefert reproduzierbare Evidenz statt Vermutungen, trennt Beobachtung von Hypothese und bleibt lesend: produktive Änderungen brauchen eine gesonderte Freigabe.
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Deeply scope a problem before coding: parallel Explore agents map related files, similar implementations, and test patterns, producing a problem definition, boundaries, and minimal implementation plan. Use when planning a feature, investigating a bug, or entering an unfamiliar codebase area.
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Diagnose problems before solving them: turn "X is broken and we don't know why" into a precise problem statement, verified root causes, solutions traced to those causes, and a rollout plan. Use whenever the user reports something failing, breaking, slowing, or recurring without a known cause: "sales dropped and we don't know why", "this keeps happening", "why does X keep failing", "find the root cause", "run a post-mortem", "5 whys"; Vietnamese "sao cứ bị vậy hoài", "tìm nguyên nhân gốc", "X đang hỏng mà không rõ vì sao". Trigger even when the user asks for solutions directly: "give me ideas to fix falling sales" is a diagnosis request. Not for open-ended ideation when nothing is broken (brainstorm-coach), understanding user needs (design-thinking), or company-level strategic bets (strategy-board).
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Catalog of common CI/CD patterns (build, test, deploy, security, quality), scan scope resolution (org-wide or specific repos), the reusable workflow template, selection criteria, and usage-documentation template. Load when detecting recurring CI/CD patterns across GitHub orgs and/or repositories to generate standardized `reusable-*.yml` workflows with corresponding `docs/<name>-usage.md` files.
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CSS design tokens and color system for vehicle insurance platform. Use when defining colors, spacing, typography, or design variables. Keywords: eye-care colors #5B8DEF/#8B95A5/#C5CAD3, CSS variables, spacing system, color palette, typography scale, design tokens, variables.css, theme colors.
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Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards. USE THIS SKILL WHEN: - You need to review or audit actuarial models (reserving, pricing, capital) - Someone asks about loss triangle analysis or reserve adequacy - You are evaluating IBNR calculations, chain ladder methods, or Bornhuetter-Ferguson - A project involves insurance pricing, GLM rating models, or ratemaking - You need to assess Solvency II SCR calculations or RBC compliance - Someone mentions actuarial opinions, ASOP compliance, or SOA standards - You are reviewing stochastic models, ESG configurations, or DFA frameworks - A codebase uses actuarial libraries (chainladder, lifetables, ChainLadder R package) TRIGGER PHRASES: "actuarial", "loss reserving", "IBNR", "chain ladder", "premium pricing", "mortality table", "Solvency II", "capital adequacy", "ratemaking", "GLM pricing", "risk-based capital", "reserve analysis", "actuarial opinion"
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。