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Found 200 Skills
Use when cognee is a Python AI memory engine that transforms documents into knowledge graphs with vector and graph storage for semantic search and reasoning. Use this skill when writing code that calls cognee's Python API (add, cognify, search, memify, config, datasets, prune, session) or integrating cognee-mcp. Covers the full public API, SearchType modes, DataPoint custom models, pipeline tasks, and configuration for LLM/embedding/vector/graph providers. Do NOT use for general knowledge graph theory or unrelated Python libraries.
Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely.
Work with raster and imagery data including ImageryLayer, ImageryTileLayer, multidimensional data, pixel filtering, and raster analysis. Use for satellite imagery, elevation data, and scientific raster datasets.
Implement Syncfusion WPF TreeMap (SfTreeMap) control for hierarchical data visualization using nested rectangles. Use this when visualizing large datasets with hierarchical structure, creating heat maps, or displaying proportional data. This skill covers TreeMap configuration, layout algorithms, color mapping, data binding, and interactive features for stock analysis, data categorization, and hierarchical visualization scenarios.
Comprehensive guide for implementing Syncfusion WPF Range Selector (SfDateTimeRangeNavigator) for time-bound data visualization with interactive scrolling, zooming, and range selection. Use this when working with range selectors, date-time range navigation, or time-bound data visualization. This skill covers interactive data range selection, chart range zooming, and dashboard time navigation features for large time-based datasets in WPF applications.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Lovrabet development workflow CLI — Manage datasets, SQL queries, BFF scripts and code generation via the rabetbase command. Trigger words: dataset, data table, custom SQL, sql.execute, bff.execute, get_dataset_detail, validate_sql_content, save_or_update_custom_sql, @lovrabet/sdk, lovrabet development, rabetbase, filter, codegen.
Use this skill when the user needs to look up or verify Goldsky blockchain dataset names, chain prefixes, dataset types, or versions. Triggers on questions like 'what\'s the dataset name for X?', 'what prefix does Goldsky use for chain Y?', 'what version should I use for Z?', or 'what datasets are available for Solana/Stellar/Arbitrum/etc?'. Also use for chain-specific dataset questions (e.g., polygon vs matic prefix, stellarnet balance datasets, solana token transfer dataset names). Do NOT trigger for questions about CLI commands, pipeline setup, or general Goldsky architecture unless the core question is about finding the right dataset name or chain prefix.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Implements Syncfusion WinUI Cartesian Charts (SfCartesianChart) for data visualization in WinUI applications. Use this when working with column, line, bar, area, or financial charts (OHLC, Candle). This skill covers axis configuration, legends, tooltips, zooming/panning, data labels, and high-performance fast series for large datasets.