「foundation」の検索結果

Apple FoundationModels 框架，用于设备端大语言模型（LLM）—— iOS 26+ 中的文本生成、配合 @Generable 的引导式生成、工具调用以及快照流式传输。

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Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an `/interview-me` spec, delegates the data-management plan to `/data-management-plan` and the facilities statement to `/capture-environment`, and emits a funder-requirements checklist. Use when user says "draft a grant", "write a proposal", "NSF proposal", "NIH aims", "ERC application", "foundation grant", "specific aims", or "scaffold a grant proposal". NOT a submission tool — produces a draft the user uploads to the sponsor's portal themselves.

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Foundational Python best practices, idioms, and code quality fundamentals

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Design Thinking coaching foundation knowledge: coach identity and philosophy, quality and fidelity constraints, method sequencing, coaching state schema, and the canonical deck workflow

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Use when the user mentions Foundation Models review, on-device AI audit, LanguageModelSession issues, or @Generable/guardrail problems — including missing availability checks, manual JSON parsing, and guardrail handling.

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AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.

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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

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Foundation framework skills — AttributedString patterns for rich text formatting, alignment, selection, and SwiftUI integration. Use when working with styled text or AttributedString APIs.

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Foundational platform engineering knowledge from key references -- Continuous Delivery, SRE, Accelerate, Team Topologies, Chaos Engineering, and Secure Delivery. Load when contextual grounding in platform engineering theory is needed.

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Maintain or assess Workflow Foundation-based solutions on .NET Framework, especially where long-lived process logic or legacy designer artifacts still matter. USE FOR: WF activities and workflows; designer-backed process logic; long-lived workflow state and persistence; keep, isolate, or replace decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Project memory workflow for init/upgrade, profile-aware setup, end session, normal/full close, file organization, document archiving, language switching, versioned update logs, rule review, status, wiki sync, and context recovery. Use for /project-butler, setup/初始化, foundation setup, profile setup, end session/收工, normal close, full close, foundation repair, organize files/整理文件, change language/切换语言, continue/接着上次, continue full context/全面回顾, review claude, sync wiki, status. Maintains project memory files. Runs version freshness check before trigger routing.

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Design architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.

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options foundations, Greeks analysis, volatility surface, put call parity, options pricing, delta gamma theta vega rho, Black Scholes, options theory, derivatives basics, options mechanics

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Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

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Applies the Neoplatonic and mathematical reasoning of Hypatia of Alexandria. Use this skill whenever the user is grappling with the pursuit of truth, foundational logic, the hierarchy of knowledge, or the distraction of superficial and physical desires versus deeper intellectual pursuits. Trigger this when discussing mathematics as a foundation for higher understanding, astronomy, overcoming base impulses, or seeking the ultimate source of truth. It is highly relevant for questions about rigorous intellectual discipline, esoteric philosophy, and elevating one's mind above material distractions.

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Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.

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Foundations of breath practice and seated meditation across traditions — diaphragmatic mechanics, pranayama families (ujjayi, nadi shodhana, bhramari, kapalabhati, bhastrika, sitali, sheetkari), buffer-safe breath-retention posture, shikantaza and koan traditions in Sōtō and Rinzai Zen, secular clinical mindfulness as MBSR translates it, and engaged-mindfulness pedagogy as Plum Village teaches it. Use when setting up a breath practice, designing a meditation sit, debugging a stalled practice, or deciding whether a given technique is safe for a given person.

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.

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Consolidated Symfony conventions (Symfony 6.4 / 7.x): attribute routing, controllers as services, autowiring/DI, Form types, Validation constraints, Voters for authorization, the Serializer/DTO contract, and Messenger. Apply when writing or reviewing Symfony backend code. Activated automatically by symfony-plugin/stack.md as a convention skill for the development phase. Apply when: writing or reviewing Symfony controllers, services, forms, validators, voters, and serialization. Do NOT use this skill for: - PHP language idioms (strict_types, enums, readonly) — see php-foundation:php-conventions. - Doctrine queries / entities — see symfony-plugin:doctrine-patterns. - Testing — injected by the QA phase (WebTestCase/KernelTestCase) + php-foundation:php-testing.

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Spring Boot coding conventions: REST controller design, service layer, dependency injection, configuration, DTO patterns, exception handling, and Bean Validation. Apply on any Spring Boot project. Works alongside spring-boot-plugin:spring-data-jpa for persistence and java-foundation:java-conventions for domain objects. Use this skill to: - Structure REST endpoints with @RestController, proper HTTP semantics, and DTO mapping. - Enforce constructor injection and @ConfigurationProperties over @Value on fields. - Write @Service classes with transactional boundaries at the right layer. - Handle errors consistently with @RestControllerAdvice and ProblemDetail (RFC 9457). Do NOT use this skill for: - JPA entity and repository patterns — see spring-boot-plugin:spring-data-jpa. - Spring Security configuration — see the security phase injection in stack.md. - Build tool configuration — see java-foundation:build-tooling.

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Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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