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Found 455 Skills
Unified intelligent query interface for the CDM DuckDB database. Use this skill when the user wants to query the linkml-coral CDM database. Automatically chooses between fast SQL translation and schema-aware intelligent queries based on complexity. Supports natural language questions, schema exploration, and data analysis.
Collaborative design exploration that refines ideas into validated specs through iterative questioning. Use before any creative work including creating features, building components, adding functionality, or modifying behavior.
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Specialized feature development agents. Use for deep codebase exploration and architecture design during feature development.
Token-efficient codebase exploration using RepoPrompt CLI. Use when user says "use rp to..." or "use repoprompt to..." followed by explore, find, understand, search, or similar actions.
Multi-repository codebase exploration for library internals, architecture understanding, and implementation comparisons.
Teaches learners to extract transferable design lessons from real-world codebases through critical evaluation and systematic exploration. Use when a learner wants to study existing code to learn patterns, architecture, or design decisions—not just understand what it does. Guides through navigation, pattern recognition, critical evaluation (deliberate choice vs. compromise), and lesson extraction. Triggers on phrases like "learn from this codebase", "study how X is implemented", "understand design patterns in Y", or when a learner wants to improve by reading real code.
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.
Best practices for doing quick exploratory data analysis with minimal code and a Pandas .plot like API using HoloViews hvPlot.
Enterprise LLM Fine-Tuning with LoRA, QLoRA, and PEFT techniques
LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.