Loading...
Loading...
Found 255 Skills
TanStack Table v8 headless data tables for React. Covers column definitions, sorting, filtering (fuzzy/faceted), server-side pagination with TanStack Query, infinite scroll, virtualization (TanStack Virtual), column/row pinning, row expanding/grouping, column resizing, and reusable Shadcn-styled components. Prevents 15 documented errors including infinite re-renders, React Compiler incompatibility, and server-side state mismatches. Use when building data tables, fixing table performance, implementing server-side pagination, adding filtering/sorting, or debugging table state issues.
Backlog Management. Users can submit ideas or pain points at any time, and the AI is responsible for following up, organizing, merging, and archiving them into the backlog file. When users are preparing to launch a new version, it assists in filtering from the backlog. Driven by pain points, no advance scheduling is done.
JSON querying, filtering, and transformation with jq command-line tool. Use when working with JSON data, parsing JSON files, filtering JSON arrays/objects, or transforming JSON structures.
Search the web using Brave Search API for fast, privacy-respecting results with localization, freshness filtering, and extra snippets. Use when you need web search results with country/language targeting or time-based filtering. Requires BRAVE_API_KEY. Keywords: brave, web search, localized search, privacy search, freshness filtering.
Guide for Workleap's logging library (@workleap/logging) that provides structured, composable logging for frontend TypeScript applications. Use this skill when: (1) Setting up logging in a Workleap frontend application (2) Creating or configuring loggers (BrowserConsoleLogger, CompositeLogger) (3) Understanding log levels (debug, information, warning, error, critical) (4) Building complex log entries with chained segments (withText, withObject, withError) (5) Using logging scopes to group related log entries (6) Styling log output in browser console (7) Composing multiple loggers to send logs to different destinations (8) Filtering logs by severity level (9) Integrating logging with LogRocket or other telemetry tools (10) Reviewing logging-related changes in pull requests (11) Questions about logging best practices specific to wl-logging
Repository packaging for AI/LLM analysis. Capabilities: pack repos into single files, generate AI-friendly context, codebase snapshots, security audit prep, filter/exclude patterns, token counting, multiple output formats. Actions: pack, generate, export, analyze repositories for LLMs. Keywords: Repomix, repository packaging, LLM context, AI analysis, codebase snapshot, Claude context, ChatGPT context, Gemini context, code packaging, token count, file filtering, security audit, third-party library analysis, context window, single file output. Use when: packaging codebases for AI, generating LLM context, creating codebase snapshots, analyzing third-party libraries, preparing security audits, feeding repos to Claude/ChatGPT/Gemini.
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, capability discovery, tool filtering, programmatic execution, or encountering context bloat, server configuration, tool execution errors.
Transform song lyrics into vivid visual scene descriptions and image generation prompts — filtering for concrete imagery and rendering each distinct scene as a numbered canvas.
Create production-quality Django REST Framework APIs using Clean Architecture and SOLID principles. Covers layered architecture (views, use cases, services, models), query optimization (N+1 prevention), pagination/filtering, JWT authentication, permissions, and production deployment. Use when building new Django APIs, implementing domain-driven design, optimizing queries, or configuring authentication. Applies Python 3.12+ and Django 5+ patterns.
Write SQL, TypeScript, and dynamic table transforms for Goldsky Turbo pipelines. Use this skill for: decoding EVM event logs with _gs_log_decode (requires ABI) or transaction inputs with _gs_tx_decode, filtering and casting blockchain data in SQL, combining multiple decoded event types into one table with UNION ALL, writing TypeScript/WASM transforms using the invoke(data) function signature, setting up dynamic lookup tables to filter transfers by a wallet list you update at runtime (dynamic_table_check), chaining SQL and TypeScript steps together, or debugging null values in decoded fields. For full pipeline YAML structure, use /turbo-pipelines instead. For building an entire pipeline end-to-end, use /turbo-builder instead.
OpenTelemetry Transformation Language (OTTL) expert. Use when writing or debugging OTTL expressions for any OpenTelemetry Collector component that supports OTTL (processors, connectors, receivers, exporters). Triggers on tasks involving telemetry transformation, filtering, attribute manipulation, data redaction, sampling policies, routing, or Collector configuration. Covers syntax, contexts, functions, error handling, and performance.