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Found 331 Skills
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Super Ralph Wiggum - autonomous iteration loops with templates, PRD support, progress tracking, and browser testing. This skill should be used when running Claude Code in autonomous loops for test coverage improvement, PRD-based feature development, documentation generation, dataset creation, lint fixing, code cleanup, or framework migrations. Combines the plugin's in-session loop mechanism with specialized templates and best practices from Geoffrey Huntley, Ryan Carson, and AI Hero.
Master Node.js streams for memory-efficient processing of large datasets, real-time data handling, and building data pipelines
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.
Use when the user needs Excel file manipulation — reading, writing, formulas, charts, conditional formatting, data validation, pivot tables, or large file handling. Trigger conditions: create Excel reports programmatically, read spreadsheet data, add formulas or charts, apply conditional formatting, perform data validation, generate pivot tables, handle CSV import/export, process large datasets in Excel format.
Explore and query data on S3, Cloudflare R2, GCS, MinIO, or any S3-compatible storage. Use when the user mentions an s3://, r2://, gs://, or gcs:// URL, asks "what's in this bucket", wants to list remote files, preview remote Parquet/CSV/JSON, or query data on object storage without downloading it. Also triggers when the user wants to know the size, schema, or row count of remote datasets.
Research TikTok Creative Center or ad-library style datasets for winning ad patterns, regions, objectives, hook language, and creative signals without mixing paid ads with organic creator discovery.
Build creator lead lists for TikTok, Instagram, and X by turning normalized platform datasets into outreach-ready leads with contact signals, shortlist logic, and draft outreach messages. Use this when the user wants creator discovery, contact extraction, shortlist building, or outreach prep.
Use Parallel's parallel-cli to do live web search, URL extraction (clean markdown), deep research reports, bulk data enrichment (CSV/JSON), FindAll entity discovery, and web monitoring. Use when the user asks to look something up online, needs current sources/citations, provides URLs to read or summarise, requests deep/exhaustive research, wants to enrich a dataset with web-sourced fields, wants a list of entities (companies/people/places), or wants to monitor the web for changes over time.
Create or update a Coral source spec YAML for a custom HTTP API or local dataset. Use when authoring a standalone source for `coral source add --file`, or when adapting that spec into a bundled source in the Coral repo.