orchestrate
claudecodexRun a multi-session job as its orchestrator: plan, brief worker sessions, monitor, verify their claims, and loop the human in only for decisions.
2 skills
An end-of-session learning ritual that converts finished work into durable understanding — by making you recall and reconstruct it before filling any gaps, instead of letting the AI's fluency stand in for your own.
Plugin debrief from marketplace tstanmay13-skills in tstanmay13/claude-skills. Commands read from the repo's manifest — 21 days ago.
debriefDebrief a finished work session into actual learning instead of just output. Make the user recall and reconstruct what happened in their own words before you fill any gaps, catch the fluency illusion (feeling you learned something you only watched get done), and distill one or two durable, transfera
Run a multi-session job as its orchestrator: plan, brief worker sessions, monitor, verify their claims, and loop the human in only for decisions.
2 skills
A conversational lens that keeps the agent in product-space — describing what the human on the other side of the screen experiences — until you explicitly ask to cross into code-space.
1 skill
Fable 5's working discipline, distilled — outcome-first reporting, act-don't-ask autonomy, faithful verification. Load at session start on a smaller model.
1 skill
Interactive learning mode that requests meaningful code contributions at decision points (mimics the unshipped Learning 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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