'foundation' 검색 결과

Apply when integrating a new requirement into an existing design. Redesign as if the requirement had been a foundational assumption from day one, instead of bolting it on.

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Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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AWS Bedrock — fully managed foundation models on AWS infrastructure. Use when deploying AI in AWS-native environments, needing enterprise compliance (SOC2, HIPAA), running Claude, Llama, Titan, or Mistral on AWS, leveraging Knowledge Bases for RAG, or applying Guardrails for content safety.

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Builds Agent2Agent (A2A) servers and clients, the open protocol (originally from Google, now under the Linux Foundation) that lets AI agents from different frameworks call each other. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.

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The foundational theory of interactive experience design - loops, motivation, feel, and the art of meaningful playUse when "game design, core loop, game feel, player motivation, game mechanics, meaningful choice, progression system, game economy, game balance, playtesting, GDD, game document, fun factor, engagement, flow state, risk reward, player agency, juice, game polish, 8 kinds of fun, bartle types, MDA framework, game-design, player-experience, core-loop, motivation, game-feel, MDA, playtesting, GDD, systems-thinking, player-psychology, engagement, flow-state" mentioned.

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World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.

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The ultimate brand asset: a consistent, explorable universe generated by AI. This skill covers creating coherent brand worlds, consistent characters, architectural spaces, and visual universes that serve as the foundation for all marketing content. Traditional brand building creates guidelines. AI world building creates explorable realities. Characters with consistent personalities across thousands of images. Environments that feel like real places. Visual languages so coherent that any new asset feels like it belongs. This is where AI generation meets universe design. The practitioners of this skill don't just create images—they create the DNA that generates infinite consistent images. They build the source code for visual worlds. Use when "world building, brand world, universe, consistent character, character design, visual language, brand universe, style bible, world bible, coherent assets, character sheet, environment design, world-building, universe, character-design, consistency, visual-language, brand-world, style-guide" mentioned.

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Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

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arkcli 自定义模型仓库管理：从 TOS 导入自定义模型、查询/筛选自定义模型、查看详情、改名、删除、查询可用量化模式、量化已就绪的模型。任何提到自定义模型 ID（`cm-*`）的管理、部署准备，或要求用 `cm-*` 直接对话/推理/试效果的边界判断，都必须使用本 skill。注意：查询火山**公共基础模型**（doubao 等 foundation models）走 arkcli-models；本 skill 只管账号下的自定义模型仓库。

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This skill should be used when the user asks to "make this animation feel natural", "fix motion that feels stiff/floaty/cheap", "my animation looks robotic", "choose an easing curve", "how do I use the Graph Editor", "add overshoot or bounce", "Easy Ease isn't enough", "make snappy motion", "pick a duration for this transition", "stagger a list animation", "sync animation to a beat", or "review motion for good timing". It is the tech-agnostic foundation for deciding how something should move.

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Coordinate the Next.js foundation and ownership.

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Applies the foundationalist reasoning and methodological doubt of René Descartes (17th-century French rationalist philosopher). Use this skill whenever the user is questioning fundamental assumptions, seeking absolute certainty in a decision, dismantling flawed systems, or trying to rebuild a project from first principles. Trigger this skill for topics involving epistemology, rationalism, radical doubt, systemic reconstruction, or separating objective truth from sensory illusion. Reach for this whenever a user needs to cut through probabilities and consensus to find an indubitable 'Archimedean point' to build upon.

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Use this skill whenever you need to reason like John D. Rockefeller, founder of Standard Oil and American oil industrialist. Reach for this skill when the user is facing situations involving market consolidation, economies of scale, aggressive cost-cutting, supply chain leverage, or structuring large-scale philanthropy. It is highly applicable for questions about eliminating operational waste, dealing with fierce competition, negotiating from a position of immense scale, or building self-sustaining charitable foundations. Trigger this skill for topics like monopolies, ruthless efficiency, corporate PR crises, and strategic acquisitions, even if the user doesn't explicitly name Rockefeller.

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Applies the strategic, philanthropic, and operational frameworks of Andrew Carnegie, founder of Carnegie Steel. Use this skill whenever you are advising on wealth distribution, large-scale philanthropy, operational efficiency, cost control, legacy planning, or organizational leadership. Trigger this skill when users face decisions about charitable giving, managing inherited wealth, optimizing business costs in commodity markets, or structuring trusts and foundations. Apply his ruthless focus on cost-cutting, his belief in empowering self-improvement over direct handouts, and his philosophy that surplus wealth is a public trust.

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Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S. Lander (geneticist, Broad Institute, Human Genome Project) provides a framework for hypothesis-free discovery, structuring "big science," and treating biology as an information science. Reach for this when the user is discussing open science, collaborative ecosystems, CRISPR/gene editing ethics, managing massive datasets, or setting realistic timelines for technological breakthroughs. Apply his principles to avoid the "lone genius" myth, prevent overpromising, and build foundational infrastructure that serves the broader community.

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Suede-owned operations-architecture discipline that fixes the shape of a system before anyone builds it: entity schema first, exactly one write path per entity, every unit of work routed to a deterministic automation, an AI agent, or a human decision, every existing tool marked absorb, keep, or kill, and the build sequenced into phases whose completion is proved by a command. Use when scoping an automation or agent build, deciding whether a workflow needs a model at all, consolidating a sprawling tool stack, designing the data model under a workflow, choosing a migration path off an old system, or ordering a build so nothing lands on a moving foundation. NOT FOR: executing a planned multi-file repo change (use suede-graph-flo-xr); coordinating parallel builders with file ownership (use suede-agent-teams); lead lifecycle, scoring, and CRM routing rules (use suede-revops); proving agent output quality against an eval set (use suede-ai-eval); reviewing the finished diff (use suede-code-review).

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Use when the mobile repository has no theme tokens or component library yet (a new app, an "Apply design system" task, or a task that asks for one), before building any screen - writes the approved design system's generated files from get_design_system (files true) and builds the theme from them when there is one.

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Use when the repository has no design system yet (no tokens, no component library - a new site/app, an "Apply design system" task, or a task that asks for one), before building any page section - writes the approved design system's generated files from get_design_system (files true) and builds the tokens on them when there is one.

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Design local AI engines for iOS. Use when wiring Apple Foundation Models, local classifiers, extraction, summarization, grounded answers, and cloud fallbacks.

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Designs consensus, quorums, leases, idempotency, and CRDTs. Use when facing split brain, fencing tokens, exactly-once claims, duplicate retries, stale reads, or leader flapping.

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Team-theory primitives for cooperative agents: what each subagent sees, when to communicate, aggregation, Shapley credit, coalition topology. Use when organizing subagents.

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16 evidence-graded behavioral-economics primitives. Use when asking will this nudge work, is a pricing decoy or dark pattern lawful, or what effect size to expect.

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Tests whether data supports a claim: A/B significance, peeking, sample size, eval error bars, multiple comparisons. Use when deciding if results are significant.

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PID/MPC/Kalman/anti-windup control primitives for feedback loops. Use when an autoscaler flaps, a controller overshoots after saturation, or budget pacing overspends at peak.

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