497 件 ・ 関連度順
概要と使いどころ
Builds HTTP APIs and web services in Rust with Axum, the Tokio team's web framework on top of Tokio, Hyper and Tower. Use when a user asks to create a Rust REST API, add routes, extractors, JSON handlers, shared state, middleware, error handling, WebSockets, graceful shutdown or tests with Axum, or to migrate older Axum code (0.6/0.7 path syntax) to 0.8.
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Actix Web is a powerful, high-performance web framework for Rust. It provides async handlers, type-safe extractors, middleware, and integrates with the Rust ecosystem for building fast, reliable, and memory-safe web services.
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Mastery of the new frontier: AI-generated video that rivals—and reimagines—traditional production. This skill covers Veo3, Runway Gen-3, Sora, Kling, Pika, Luma Dream Machine, and the rapidly evolving landscape of neural video synthesis. We're not just using AI to replace traditional video—we're unlocking visuals that were previously impossible, unaffordable, or unimaginable. Flying through abstract data visualizations, impossible camera moves, photorealistic product renders from text, and consistent character performances without actors or sets. The practitioners of this skill understand that AI video is not about cost savings— it's about creative expansion. The ceiling has been removed. What would you create if production cost was zero and physics was optional? Use when "AI video, generate video, Veo3, Veo 3, Runway, Sora, Kling, Pika, Luma, text to video, image to video, video generation, AI footage, neural video, synthetic video, impossible shot, ai-video, veo3, runway, sora, kling, pika, generation, text-to-video, neural, synthesis" mentioned.
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Detects and redirects scope creep during implementation — unrequested features, drive-by refactors, premature abstraction, and over-engineering. Use while implementing, especially when a change is growing beyond the original request.
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。
Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Brand Reputation Monitoring workflow skill. Use this skill when the user needs Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Brand Reputation Monitoring workflow skill. Use this skill when the user needs Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Lead Generation workflow skill. Use this skill when the user needs Scrape leads from multiple platforms using Apify Actors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Suggest small refactors that improve readability without changing behavior. Use when a junior developer asks to clean up code.
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。
Delegate coding, code review, analysis, and research jobs to Google Antigravity CLI (agy) sub-agents that run alongside your own work. Use this whenever the user asks to spin off, offload, or delegate a task to Antigravity, agy, Gemini, or an "external agent"; wants a second opinion or independent review from a different model; wants intensive repo work (audits, large refactors, research sweeps) run in the background while you keep working; or says things like "have Antigravity do it", "spin up a sub-agent for this", or "get more done in parallel". Also use it proactively when a task is a good fit for parallel delegation and the user has expressed a preference for using Antigravity workers.
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 whenever you are reasoning about nutritional epidemiology, dietary guidelines, chronic disease prevention, or public health policy. It channels the thinking of Meir J. Stampfer, epidemiologist and nutrition expert at Harvard University. Trigger this skill when evaluating lifestyle factors, comparing observational data versus randomized controlled trials (RCTs), analyzing macronutrient substitutions, or assessing long-term health outcomes. Stampfer's approach is essential when users ask about "good" vs. "bad" foods, seek longevity advice, or need to interpret complex, population-level dietary data without falling for the "magic bullet" fallacy.
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Applies the nutritional epidemiology and public health frameworks of Frank B. Hu (nutrition epidemiologist, Harvard University). Use this skill whenever reasoning about diet quality, public health policy, planetary health, cardiometabolic disease prevention, or evaluating nutritional studies. Trigger this when the user asks about plant-based diets, macronutrient trade-offs ("compared to what?"), the impact of the food environment, or lifestyle factors for longevity. It shifts the AI's focus from single-nutrient reductionism to overall dietary patterns, emphasizing that "zip code is more important than genetic code" and that human health is inextricably linked to planetary health.
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Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the discrepancy between observational and randomized data, or how to future-proof a clinical registry.
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。
Applies the epidemiological reasoning and population-health frameworks of Albert Hofman (Harvard epidemiologist, Rotterdam Study). Trigger this skill whenever you are analyzing public health strategies, preventive medicine, cohort study design, cardiovascular or neurodegenerative disease risks, or healthy aging. Use it when evaluating whether to use population-wide interventions versus individual screening, assessing risk factors in elderly populations, or tracing adult chronic diseases back to early-life or fetal origins.
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Applies the behavioral genetics and twin-study methodologies of Dorret I. Boomsma (behavior geneticist, Vrije Universiteit Amsterdam). Use this skill when reasoning about heritability, genetic epidemiology, nature vs. nurture debates, psychiatric genetics, or longitudinal phenotyping. Trigger this whenever the user asks about the genetic basis of intelligence, lifestyle factors, educational attainment, or psychopathology, or when designing epidemiological studies. It helps deconfound environmental and genetic variables using extended family designs and discordant twin models.
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Structured planning mode for AI agents working on coding and development tasks. Produces a comprehensive plan BEFORE any code is written or changed, including task decomposition, architecture decisions, risk assessment, and approval gates. Use this skill whenever the user says "/plan", "plan mode", "plan first", "think before coding", "make a plan", or asks the agent to plan before executing. Also trigger when the user describes a complex multi-step development task and would clearly benefit from structured planning before implementation — especially for tasks involving new features, refactors, migrations, infrastructure changes, or multi-file edits. When in doubt, plan. A few minutes of planning prevents hours of rework.
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Orchestrate a phased WinForms-to-Avalonia migration: behavior contracts, UI-independent application logic, platform adapters, MVVM shell, parity/cutover, evolution, and hardening. Use when the request explicitly involves moving a WinForms or .NET Framework desktop UI to Avalonia, or continuing that migration. Do not trigger for unrelated migrations, rewrites, ViewModel refactors, settings work, or command-line work without WinForms-to-Avalonia context. Load this orchestrator before one matching phase skill.
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Data-model rules that make a schema importable from day one, so the migration-import-engineer is never blocked on missing columns. Every SMB Product-Builder product must let a customer bring their data from an incumbent (ServiceTitan/Toast/Mindbody/Shopify) — that requires provenance (source_ref) and rollback (import_batch_id) on importable entities, and modelling real-world actors as entities rather than inline fields. Applied by architect when writing the data model in ARCH-{slug}.md, and checked by migration-import-engineer. One cheap rule set prevents the migration↔architecture seam gap from recurring across all 40 products.
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Designs, builds, or refactors user-facing web or application interfaces with deliberate visual direction, typography, color and spacing tokens, content hierarchy, responsive behavior, accessible interaction states, and real rendered-surface verification. Use for pages, components, dashboards, landing pages, design systems, and frontend polish. Not for backend logic, API-only work, or visual claims that cannot be rendered or inspected.
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Implements behavior changes with a test-first red-green-refactor loop: write one meaningful failing test, watch it fail for the intended reason, make the smallest change, then refactor while green. Use for new behavior, bug fixes, refactors with preserved contracts, and regression tests. Not required for disposable prototypes, generated files, or documentation-only edits.
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Refactors code while preserving behavior. Use when reducing technical debt, planning codemods, applying strangler migrations, or tightening CI guardrails around risky changes.
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Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels.
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Use for every task that plans, writes, edits, debugs, tests, refactors, reviews, or maintains code. Apply to professional repositories, scripts, apps, services, prototypes, beginner work, vibe coding, and bounded code changes inside large systems. Choose the fewest clear lines, files, paths, states, and abstractions that correctly implement the requested behavior. Do not invent fallbacks, compatibility paths, defensive layers, or future scale without evidence. Do not use for ordinary conversation, emotional support, prose, translation, summarization, social-media content, or general research or technical discussion that is not part of planning, changing, or reviewing code. Never weaken real security, integrity, concurrency, compatibility, privacy, accessibility, legal, or regulatory requirements.
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。