“code models”的搜索结果

Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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Audits business spreadsheet models (.xlsx, .xlsm, or Google Sheets exported to .xlsx) for structural integrity errors and explains each fix -- budgets, forecasts, pricing models, commission calculators, FP&A packs, ops trackers, not only banking models. A bundled openpyxl script finds hardcoded numbers inside formulas, typed values pasted over formulas, inconsistent formulas across a row or column,

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Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.

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Papers With Code CLI (`pwc`) for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, frameworks, and benchmark leaderboards, and submitting authenticated paper edits through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, `pwc`, or `pwc-cli`.

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Figma 转 Photoshop (PSD) (Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).) Master OS — automated mastery of Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「figma 转 psd」「figma to psd」「figma 转 photoshop」「figma 导出 psd」「设计稿转换」

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Odoo 18 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 18 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/list/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code. Includes CSS/SCSS asset authoring and review for Odoo addons.

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Odoo 19 development knowledge base with 18 specialized guides covering Actions (ir.actions.*, cron jobs, server actions), Controllers (HTTP routing, endpoints, auth types), Data files (XML/CSV records, shortcuts, noupdate), API Decorators (@api.depends, @api.constrains, @api.ondelete, @api.onchange, @api.model, @api.private), SQL Constraints (models.Constraint replacing _sql_constraints), Database Indexes (models.Index), Module development (manifest, wizards, reports), Field types (Char, Text, Monetary, relational fields), Manifest configuration (__manifest__.py, dependencies, asset bundles), Mixins (mail.thread, mail.activity.mixin, mail.alias.mixin, utm.mixin), ORM Model methods (search, CRUD, domain filters, recordsets, CamelCase model naming), Migration scripts (pre/post/end hooks, data migration), OWL frontend components (hooks, services, lifecycle), Performance optimization (N+1 prevention, batch ops, _read_group), QWeb Reports (PDF/HTML, paper formats, barcodes, t-out), Security/ACL (record rules, field permissions, privilege-based groups, @api.private), Testing (TransactionCase, HttpCase, mocking, query count assertions), Transactions (savepoints, UniqueViolation, serialization failures), Translations (i18n, PO files, translatable fields), XML Views (list/form/search, kanban card templates, xpath inheritance, QWeb templates). Use when writing, reviewing, or debugging any Odoo 19 Python or XML code, creating or modifying modules, fixing performance issues, or looking up Odoo 19 API patterns and best practices. Includes CSS/SCSS asset authoring and review for Odoo addons.

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Odoo 17 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 17 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/tree/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code. Includes CSS/SCSS asset authoring and review for Odoo addons.

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Odoo 16 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 16 or custom addons—even if the user only pastes a traceback, mentions addons/ or __manifest__.py, describes form/tree/kanban/XML errors, HTTP controllers, or business rules on models—including building features, fixing bugs, refactoring, or reviewing addon code. Includes CSS/SCSS asset authoring and review for Odoo addons.

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arkcli +code-example：为指定基础模型生成多语言（Python / Go / Java / Node / curl）调用示例代码并写入本地文件。数据源是火山方舟 OpenTOP OpenGetSampleCode。当用户需要拿某个基础模型的 SDK / curl 调用示例、保存为本地接入模板时使用。反触发：TTS/ASR/语音模型没有 arkcli 示例代码路径，不能靠补版本解决，只能转 models search 说明当前不支持。

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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"

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Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.

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Decompose dense codebase-wide, multi-document, PDF, and aggregation work even when the input fits the context window, following Recursive Language Models (Zhang, Kraska, Khattab, 2025). Use when the user asks to analyse all files, a whole repo, all docs, large PDFs, or to aggregate or multi-hop across scattered sources. Skip one file, one function, a single needle, or a one-page PDF conversion. Triggers: long context, context rot, large codebase, many files, all files, big document, multi-document, PDF, aggregate, summarize everything, codebase-wide, multi-hop, recursive, sub-agents, map-reduce.

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Use when keeping imp's docs and config examples coherent after a change - updating docs/internals/ARCHITECTURE.md / README / docs/GOAL.md / docs/MODELS.md / docs/ROADMAP.md / imp.conf.example / CHANGELOG / MISSION_JOURNAL, or "is this doc stale", "document this change", "the example config is out of date", "the README says X but the code does Y", "add a roadmap ledger row". Do NOT use for layer/frontmatter/lint questions (docs-layers), structural code audits (codebase-audit), measuring perf or refreshing the perf baseline (benchmark-cuda), or the agent's private memory (MEMORY.md).

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为 msModelSlim 创建基础 Transformers 模型适配器（Model Adapter）。 包含创建适配器、实现必需接口与注册安装流程。 适用：Decoder-only LLM、理解类 VLM（仅 LLM/text 部分）。 多模态生成（DiT/扩散，含双/多专家）见下方「多模态生成扩展」节，按需加载。 不适用：Encoder-only、非 Transformers 架构。

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Integrate ManagedCode.Orleans.Graph into an Orleans-based .NET application for graph-oriented relationships, edge management, and traversal logic on top of Orleans grains. Use when the application models graph structures in a distributed Orleans system.

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Build with the Falcon open models — UAE's TII models with strong multilingual and efficient architectures.

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Build with DeepSeek's models — efficient open-weight reasoning models and low-cost API inference.

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Infectious disease modeling with SIR/SEIR models — R0, fitting, forecasting, and intervention scenarios.

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Build with Zhipu AI's ChatGLM models — bilingual conversational models with strong Chinese dialogue.

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Build with Baichuan's open models — bilingual Chinese-English models for general and specialized tasks.

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Build with BAAI's Aquila models — open bilingual models from Beijing's academy with research transparency.

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Build with AI21 Labs' models — Jurassic for generation and strong task-specific models for enterprise use.

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