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Found 1,011 Skills
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
Lightweight scanner tracking Smart Money market concentration across all Hyperliquid assets. Flags assets accelerating up the ranks before they become crowded top-3 plays. IMMEDIATE_MOVER signal fires on 10+ rank jumps with quality filters (erratic history, velocity gate, trader count floor, max leverage check). One API call per scan, runs every 60 seconds. Use when detecting SM rotations, finding emerging opportunities early, or monitoring rank acceleration patterns.
Use after solution concepts exist to surface and prioritize assumptions behind outcomes, opportunities, or solution ideas and design experiments to test them.
When the user wants to create, optimize, or audit a showcase or gallery page for user-generated content. Also use when the user mentions "showcase," "gallery," "user work," "UGC," "creator showcase," "examples," or "made with [product]."
You are an Instagram marketing virtuoso with an artistic eye and deep understanding of visual storytelling. You live and breathe Instagram culture, staying ahead of algorithm changes, format innova...
Converge the raw answers of real fieldwork —clusters with their noise and their outliers, jobs to be done in three dimensions with two verbatims each, the customer profile, opportunities scored against the business outcome— and write the Insight Brief as dated research of the loaded product node. Manually triggered, fifth station of the discovery pipeline: the first one that runs after real fieldwork.
A skill for writing natural and valuable comments on Reddit communities. Includes the complete workflow from subreddit exploration, comment writing, review, posting, to tracking.
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
Active coordinator for building AI-powered side-gigs in 2025. Use when users want to build micro-niche products, validate business ideas, create MVPs, or launch profitable side businesses. This skill orchestrates sub-agents to execute market research, product design, business validation, and launch planning. Triggers include "help me build a side hustle," "validate my business idea," "find market opportunities," "build an AI product," or "launch a side-gig."
Build customer journey maps and service blueprints that visualize the end-to-end user experience including touchpoints, emotions, friction, and underlying systems. Use this skill whenever the user wants to map a customer journey, build a service blueprint, identify friction across an experience, align teams on the user experience, or visualize touchpoints and pain points. Triggers on customer journey, journey map, service blueprint, user journey, experience map, touchpoint analysis, friction map, journey audit, end-to-end experience, customer experience map. Also triggers when the team has departmental views of users but no shared map of what the experience actually feels like.
Use when you need systematic extraction of pain points, feature gaps, switching triggers, and opportunities from reviews (B2B review sites, app stores, forums, communities, issue trackers). Includes bias hygiene, taxonomy building, triangulation, and turning insights into experiments.
Implements WPF data validation using ValidationRule, IDataErrorInfo, and INotifyDataErrorInfo. Use when building forms, validating user input, or displaying validation errors in UI.