Results for “filter”

Answers plain-word questions from a repo's CSV, JSON, Parquet and YAML files, or a .duckdb file, with DuckDB, and shows the SQL it ran. Read-only. Trigger to query, count, join, filter or summarise data files, including analytics CSVs exported from a video platform; to build a Parquet or DuckDB cache; to check no .duckdb, .wal or .parquet cache is committed or stale; when a query hits a locked database file; or say duckrunner (ask, cache, check, refresh). Needs the user-installed duckdb Python package; never a database server. DuckDB docs, S3 and MotherDuck are the duckdb-skills plugin's; outside research researchscribe's.

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Generates DDD Specification for PHP 8.5. Creates reusable business rule objects for validation, filtering, and querying with composite pattern support. Includes unit tests.

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Ably real-time messaging patterns, WebSocket channel management, message validation and processing, staleness filtering, error recovery strategies, collaborative editing with drag-and-drop, optimistic updates for voting, real-time board collaboration, and Ably integration best practices for ree-board project

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Implement REST APIs in ABP Framework with AppServices, DTOs, pagination, filtering, and authorization. Use when building API endpoints for ABP applications.

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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

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Use this skill when building or debugging table usage with nuxt-ui-tools as a package consumer. Covers defineTableSchema, useTable, DataList, granular composition, columns, filters, sorting, offset or cursor loading, layouts, summary rows on phones, first-use empty states, actions, selection, accessible row names, context, and URL state with concrete examples.

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Complete color manipulation and green screen effects system. PROACTIVELY activate for: (1) Green screen/chromakey removal, (2) Color grading with LUTs, (3) Color correction (levels, curves, white balance), (4) Colorkey/color removal effects, (5) Hue/saturation manipulation, (6) Color space conversions (BT.709, BT.2020, HDR), (7) Color isolation effects, (8) Teal and orange look, (9) Vintage/film looks, (10) Color keying for transparency. Provides: chromakey and colorkey filters, LUT application (lut3d), curves and levels adjustment, color balance, selective color manipulation, color space handling, HDR tone mapping, professional color grading chains.

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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

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自动从知乎热榜选题、调用 AI 生成原创回答并保存为草稿，支持自定义话题关键词、写作风格和批量生成。 Draft Zhihu answers automatically: picks hot questions, generates original answers via AI, and saves them as drafts — with custom topic filters, writing style, and batch support. Never publishes.

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Evaluate and prioritize opportunities through a structured filtering process. Use for resource allocation, strategic decision support, and project management.

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Query Google Analytics 4 (GA4) data via the Analytics Data API. Use when you need to pull website analytics like top pages, traffic sources, user counts, sessions, conversions, or any GA4 metrics/dimensions. Supports custom date ranges and filtering.

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Guide a startup to set a single quantified traction goal and define the critical path of milestones to reach it. Use whenever a founder needs to prioritize activities, set growth goals, define milestones, decide what NOT to work on, plan quarterly/yearly execution, cascade goals to teams, escape the 'too many things to do' trap, or apply a binary on-path/off-path filter to proposed work. Activates on phrases like 'traction goal', 'critical path', 'what should we focus on', 'too many priorities', 'prioritization', 'milestones', 'quarterly planning', 'yearly goals', 'OKRs', 'where should I spend my time', 'what NOT to do', 'company planning', 'goal setting', 'DuckDuckGo', 'roadmap'.

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Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.

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Use when running integration tests in the VS Code repo. Covers scripts/test-integration.sh (macOS/Linux) and scripts/test-integration.bat (Windows), their supported arguments for filtering, and the difference between node.js integration tests and extension host tests.

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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.

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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

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Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.

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Airtable REST API via curl. Records CRUD, filters, upserts.

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Expert web researcher using advanced search techniques and synthesis. Masters search operators, result filtering, and multi-source verification. Handles competitive analysis and fact-checking. Use PROACTIVELY for deep research, information gathering, or trend analysis.

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Builds ASP.NET Core Minimal APIs -- route groups, filters, TypedResults, OpenAPI.

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Create a clean PR branch by filtering out .planning/ commits — ready for code review

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