SwiftUI patterns, navigation, gestures, layout, animation, Liquid Glass, WebKit, performance, and UIKit interop
9 skills
Science-based learning exercises for deliberate skill development during AI-assisted coding
Plugin learning-opportunities--learning-opportunities from marketplace claude-skills-mirror in andrew-d/claude-skills. Commands read from the repo's manifest — 1 month ago.
learning-opportunitiesFacilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated go
SwiftUI patterns, navigation, gestures, layout, animation, Liquid Glass, WebKit, performance, and UIKit interop
9 skills
Core Swift language features, concurrency, testing, SwiftData, Charts, and Codable
6 skills
Modern Python best practices. Use when creating new Python projects, and writing Python scripts, or migrating existing projects from legacy tools.
1 skill · hooks
Automatically nudges Claude to offer learning exercises after git commits. Requires the learning-opportunities plugin.
hooks
Generates a repo-specific orientation.md for use with the learning-opportunities skill
1 skill
Clarify ambiguous requirements by asking questions before implementing. Only when invoked explicitly.
1 skill
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Cross-session memory and token savings for coding agents.
6 skills · 1 agent · 1 MCP server · hooks
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
26 skills · 4 commands · 3 agents · 1 MCP server
Claude Code native multi-agent orchestration with intelligent model routing across agents and skills. Zero learning curve. Maximum power.
85 skills · 20 commands · 19 agents · 1 MCP server · hooks
Turn any learning intent into a personalized Coursera experience. Asks three quick questions (topic, familiarity, preferred format), searches Coursera's catalog, and delivers the right next step — a course, hands-on project, short video, or live roleplay — then maps a path forward. Requires the Coursera connector for catalog tools.
1 skill
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