Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect
Agent skill for hierarchical-coordinator - invoke with $agent-hierarchical-coordinator
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Agent skill for workflow-automation - invoke with $agent-workflow-automation
Agent skill for github-modes - invoke with $agent-github-modes
Agent skill for authentication - invoke with $agent-authentication
Agent skill for performance-monitor - invoke with $agent-performance-monitor
Agent skill for security-manager - invoke with $agent-security-manager
Agent skill for coordinator-swarm-init - invoke with $agent-coordinator-swarm-init
Agent skill for pseudocode - invoke with $agent-pseudocode
Agent skill for agentic-payments - invoke with $agent-agentic-payments
Agent skill for production-validator - invoke with $agent-production-validator
Agent skill for base-template-generator - invoke with $agent-base-template-generator
Agent skill for orchestrator-task - invoke with $agent-orchestrator-task
Agent skill for challenges - invoke with $agent-challenges
Overview of Experimental MVCC feature - snapshot isolation, versioning, limitations
General Correctness rules, Rust patterns, comments, avoiding over-engineering. When writing code always take these into account
Implements Manus-style file-based planning to organize and track progress on complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring >5 tool calls. Supports automatic session recovery after /clear.
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.