Automation
Task automation and scripting
project-bootstrapper
by mhattingpete
Sets up new projects or improves existing projects with development best practices, tooling, documentation, and workflow automation. Use when user wants to start a new project, improve project structure, add development tooling, or establish professional workflows.
bash-script-helper
by jeremylongshore
Configure with bash script helper operations. Auto-activating skill for DevOps Basics. Triggers on: bash script helper, bash script helper Part of the DevOps Basics skill category. Use when working with bash script helper functionality. Trigger with phrases like "bash script helper", "bash helper", "bash".
git-workflow-manager
by jeremylongshore
Manage git workflow manager operations. Auto-activating skill for DevOps Basics. Triggers on: git workflow manager, git workflow manager Part of the DevOps Basics skill category. Use when working with git workflow manager functionality. Trigger with phrases like "git workflow manager", "git manager", "git".
n8n-trigger-testing-strategies
by proffesor-for-testing
"Webhook testing, schedule validation, event-driven triggers, and polling mechanism testing for n8n workflows. Use when testing how workflows are triggered."
context-driven-testing
by proffesor-for-testing
"Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs."
build-tui-view
by hatchet-dev
Provides instructions for building Hatchet TUI views in the Hatchet CLI.
qcsd-ideation-swarm
by proffesor-for-testing
"QCSD Ideation phase swarm for Quality Criteria sessions using HTSM v6.3, Risk Storming, and Testability analysis before development begins. Uses 5-tier browser cascade: Vibium → agent-browser → Playwright+Stealth → WebFetch → WebSearch-fallback."
context-packager
by nimrodfisher
Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks.
lmms-eval-guide
by EvolvingLMMs-Lab
Guides AI coding agents through the lmms-eval codebase - a unified evaluation framework for Large Multimodal Models (LMMs). Use when integrating new models, adding evaluation tasks/benchmarks, using the HTTP eval server, or navigating the evaluation pipeline architecture.
Add Parallel AI Integration
by qwibitai
Restart: launchctl kickstart -k gui/$(id -u)/com.nanoclaw (macOS) or systemctl --user restart nanoclaw (Linux)
qcsd-refinement-swarm
by proffesor-for-testing
"QCSD Refinement phase swarm for Sprint Refinement sessions using SFDIPOT product factors, BDD scenario generation, and requirements validation."
cicd-pipeline-qe-orchestrator
by proffesor-for-testing
"Orchestrate quality engineering across CI/CD pipeline phases. Use when designing test strategies, planning quality gates, or implementing shift-left/shift-right testing."
n8n-workflow-patterns
by czlonkowski
Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.
knowledge-distillation
by davila7
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
huggingface-accelerate
by davila7
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
pytorch-lightning
by davila7
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
crewai-multi-agent
by davila7
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
ai-agents-architect
by davila7
"Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling."
crewai
by davila7
"Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents."
ray-train
by davila7
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
ray-data
by davila7
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
model-merging
by davila7
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
moe-training
by davila7
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
agent-manager-skill
by davila7
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.