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Found 329 Skills
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.
Django Unfold admin theme - build, configure, and enhance modern Django admin interfaces with Unfold. Use when working with: (1) Django admin UI customisation or theming, (2) Unfold ModelAdmin, inlines, actions, filters, widgets, or decorators, (3) Admin dashboard components and KPI cards, (4) Sidebar navigation, tabs, or conditional fields, (5) Any mention of 'unfold', 'django-unfold', or 'unfold admin'. Covers the full Unfold feature set: site configuration, actions system, display decorators, filter types, widget overrides, inline variants, dashboard components, datasets, sections, theming, and third-party integrations.
Unlock the surprising speed of SQLite in Flutter for building responsive UIs, showcasing its ability to handle large datasets with synchronous queries and optimized configurations.
Dune CLI for querying blockchain and on-chain data via DuneSQL, searching decoded contract tables, managing saved queries, and monitoring credit usage on Dune Analytics. Use when user asks about blockchain data, on-chain analytics, token transfers, DEX trades, smart contract events, wallet balances, Ethereum/EVM chain queries, DuneSQL, or says "query Dune", "search Dune datasets", or "run a Dune query".
Browser automation CLI with Nstbrowser integration for AI agents. Use when the user needs advanced browser fingerprinting, profile management, proxy configuration, batch operations on multiple browser profiles, or cursor-based pagination for large datasets. Triggers include requests to "use NST profile", "configure proxy for profile", "manage browser profiles", "batch update profiles", "start multiple browsers", "list profiles with pagination", or any task requiring Nstbrowser's anti-detection features.
Implements Syncfusion WPF DataPager (SfDataPager) for paginating large datasets in WPF applications. Use this when implementing pagination controls, page navigation, or splitting large data into manageable chunks. Supports configurable page sizes, navigation buttons, numeric page buttons, and works with DataGrid, ListBox, ListView, and ItemsControl.
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).
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.
Publication-quality bioinformatics figures - phylogenetic trees, genome browsers, iTOL datasets, and data presentation
Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns. Use when the user asks to benchmark prefix caching hit rate, caching efficiency, or repeated-prompt performance in vLLM.