데이터·AI 스킬

Draft and explain persistent goal-loop prompts for long-running agent work with clear stop conditions.

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Use when designing, querying, or troubleshooting the Financial Services Cloud data model — including managed-package (FinServ__ namespace) and Core FSC (standard objects, no namespace) object structures, household relationship modeling, financial account ownership, and the FSC rollup framework. Trigger keywords: FSC data model, FinancialAccount, FinancialHolding, FinancialAccountRole, FinancialAccountParty, FinServ namespace, household rollup, AssetsAndLiabilities, FinancialGoal, LifeEvent, AccountContactRelation FSC. NOT for setting up financial accounts and roles in Setup — use admin/financial-account-setup. NOT for household membership and rollup scheduling — use admin/household-model-configuration.

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Enterprise BPMN 2.0 diagram generator — converts natural language process descriptions into OMG-compliant BPMN 2.0 XML files and SVG previews via a 4-phase pipeline: Intent Extraction (LLM → JSON Logic-Core) → Validation (deadlock detection, structural soundness) → ElkJS Auto-Layout → BPMN XML + SVG output. Supports: multi-pool collaborations, message flows, boundary events (timer/error/signal), loop/multi-instance markers, data objects, all gateway types with correct gatewayDirection, and all BPMN 2.0 task types. Use this skill whenever the user wants to create, generate, or model a BPMN diagram, process flow, or workflow — even if they say "draw a process", "model this workflow", "make a BPMN for...", "create a Prozessmodell", "visualize this process", or describe a business process in natural language. Also use for editing or extending existing BPMN Logic-Core JSON.

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Build production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

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The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you to remember to remember, this one just pays attention.

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查询A股行情、历史K线与复权、财务指标、公司新闻公告、概念及入选依据、行业分类与成分、解禁、回购、股东增减持、涨停炸板详情、龙虎榜席位、板块热力图、指数、宏观及A+H上市信息。用于股票数据查询、原始数据测试和数据源异常排查。

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Search the web, read source pages, and retrieve structured records with Firecrawl. Use for current information, finding sources, reading a supplied URL, or looking up data-provider capabilities.

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Patterns for data loading, exploration, and statistical analysis

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Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines

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积木报表生成器 — 自然语言描述报表需求或提供截图，自动生成积木报表（支持数据报表、打印报表、分组报表、循环报表、数据填报等全类型）。Use when user says "积木报表", "jmreport", "Excel报表", "数据填报", "可视化报表", "打印报表", "分组报表", "循环报表", "按照截图生成报表", "创建积木报表", "做一个可视化报表", "积木设计器", "create jimureport", "visual report". Also triggers when user describes report requirements involving Excel-like layouts, data binding with

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Use when user asks to create/design a big screen (大屏), full-screen data visualization, or says "创建大屏", "生成大屏", "新建大屏", "设计大屏", "做一个大屏", "BI大屏", "数据大屏", "可视化大屏", "监控大屏", "create big screen", "design big screen", "BI visualization big screen". Also triggers when user describes big screen requirements like "做一个销售数据大屏" or mentions full-screen display like "展厅展示", "监控室大屏". Make sure to use this skill for big screens (大屏) — NOT dashboards (仪表盘/看板), which use a completely different layout and styling system.

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JeecgBoot 系统主数据查询与管理。Use when user asks to query/create/manage system master data, or says "查询角色", "查询用户", "查询部门", "查询字典", "创建字典", "创建角色", "查岗位", "查职务", "查租户", "查数据源", "查定时任务", "系统主数据", "query roles", "query users", "query depts", "create dict", "create role", "master data". Also triggers when other skills (desform, bpmn, codegen) need to look up or create roles, users, departments, dictionaries, or positions as dependencies — follows "先查后建" (query-first-create-later) principle.

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Use when user asks to create/edit/query Online reports, SQL reports, data reports, or says "创建报表", "生成报表", "新建报表", "查询报表", "online报表", "SQL报表", "数据报表", "统计报表", "create report", "generate report", "data report". Also triggers when user describes report requirements like "做一个销售统计报表", mentions JeecgBoot cgreport/online report, or says "查看现有报表" / "列出所有报表". This skill handles Online 报表 (SQL-driven data display/reports), not Online forms (cgform) or designer forms (desform).

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JeecgBoot Online表单（cgform）全生命周期管理——通过API自动创建/编辑数据库表和表单配置， 支持单表、主子表、树表，26种控件类型，以及JS/Java/SQL增强、权限配置、数据CRUD、积木报表集成。 只要用户意图涉及「Online表单」就必须使用本技能，包括但不限于： 创建或配置数据库表（"建一张请假表"、"创建online表"、"做一个带下拉选择的表"、"低代码表单"、"在线表单"、"配置表"）、 修改已有Online表字段（"加个字段"、"改字段类型"、"加子表"、"删除字段"）、 配置表单增强（"JS增强"、"自定义按钮"、"表单联动"、"Java增强"、"SQL增强"）、 配置权限（"字段权限"、"按钮权限"、"数据权限"、"授权给角色"）、 管理表单数据（"插入数据"、"查询记录"、"导出CSV"、"造测试数据"）、 以及关联积木报表（"给这个表加报表"、"集成打印"）。 即使用户只描述了业务需求而没说"online"（如"做一个员工信息管理功能，包含姓名、部门下拉、入职日期"）， 只要涉及元数据驱动的表单配置，也应触发本技能。 注意：不要与「设计器表单」(desform)混淆——desform是拖拽式表单设计器，用skill jeecg-desform处理； 也不要与「Online报表」(cgreport)或「Online图表」(onlchart)混淆——它们是SQL驱动的只读展示。

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Use when user asks to create/edit Online graph charts, data visualization, or says "创建图表", "生成图表", "新建图表", "做一个图表", "online图表", "数据图表", "柱状图", "折线图", "饼图", "统计图", "可视化", "chart", "graph", "create chart", "generate chart", "bar chart", "line chart", "pie chart". Also triggers when user describes chart requirements like "做一个销售柱状图" or mentions data visualization like "用图表展示男女比例".

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JeecgBoot AI 编排流程（AIFlow）全生命周期管理——通过自然语言描述需求，自动创建、编辑、查询、删除、调试、发布 AI 编排流程。 只要用户意图涉及「AI编排」「AIFlow」就必须使用本技能，包括但不限于： 创建 AI 编排流程（"做一个AI流程"、"创建aiflow"、"新建编排"、"做一个知识库问答流程"、"创建一个大模型对话流程"）， 修改已有流程（"给流程加个节点"、"改一下LLM的提示词"、"修改流程"）， 查询流程（"查看流程列表"、"有哪些AI流程"）， 删除流程（"删除流程"、"移除XX流程"）， 调试运行流程（"调试流程"、"运行流程"、"测试流程"）， 发布管理（"发布流程"、"取消发布"）， 复制流程（"复制流程"、"克隆流程"）。 关键词触发：aiflow、ai-flow、AI编排、AI流程、编排流程、大模型流程、知识库流程、LLM流程。 注意：本技能仅处理 AI 编排流程（AIFlow），不处理 BPMN 工作流（使用 jeecg-bpmn）、 不处理简流（使用 jeecg-lowcode-miniflow）。

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

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Automatically translate and sync App Store metadata (description, keywords, what's new, subtitle) to multiple languages using LLM translation and asc CLI. Use when asked to localize an app's App Store listing, translate app descriptions, or add new languages to App Store Connect.

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Resize and validate App Store screenshots with current asc screenshot-size data and macOS sips. Use when preparing or fixing screenshots for App Store Connect submission.

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TypeScript-first schema validation and type inference. Use for validating API requests/responses, form data, env vars, configs, defining type-safe schemas with runtime validation, transforming data, generating JSON Schema for OpenAPI/AI, or encountering missing validation errors, type inference issues, validation error handling problems. Zero dependencies, compact core (~5kb gzipped; zod/mini ~1.9kb).

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TanStack Query v5 (React Query) server state management. Use for data fetching, caching, mutations, or encountering v4 migration, stale data, invalidation errors.

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