Automation
Task automation and scripting
fleet-management
by kishorkukreja
When the user wants to manage vehicle fleets, optimize fleet size, or plan vehicle acquisition and maintenance. Also use when the user mentions "fleet sizing," "vehicle lifecycle," "fleet utilization," "maintenance planning," "replacement strategy," "total cost of ownership," or "fleet optimization." For route planning within a fleet, see route-optimization. For last-mile operations, see last-mile-delivery.
3dsmax-rendering
by TerminalSkills
Configure and optimize rendering in 3ds Max — V-Ray and Corona render settings, render elements, light mix, batch rendering, network rendering, denoising, and post-production workflows. Use when tasks involve setting up production renders, optimizing render times, batch rendering multiple views, or configuring render farms for archviz and product visualization.
drilling-logistics
by kishorkukreja
When the user wants to optimize oil and gas drilling operations, manage rig logistics, or coordinate upstream supply chains. Also use when the user mentions "well planning," "rig scheduling," "drilling supply chain," "mud logistics," "tubular management," "offshore logistics," "drilling materials management," or "completion operations." For midstream and downstream, see energy-logistics. For fuel distribution, see fuel-distribution.
typst-paper
by bahayonghang
Typst academic paper assistant (Chinese & English papers, conference/journal submissions). Use when writing, reviewing, compiling, or improving Typst academic papers. Use when user mentions typst compile, grammar, bibliography, deai, translate, title, logic, reviewer perspective, or any Typst paper quality improvement task. Domains: Deep Learning, Time Series, Industrial Control, Computer Science.
tooluniverse-multi-omics-integration
by mims-harvard
Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.
remote-browser
by browser-use
Controls a cloud browser from a sandboxed remote machine. Use when the agent is running in a sandbox (no GUI) and needs to navigate websites, interact with web pages, fill forms, take screenshots, or expose local dev servers via tunnels.
apify-ecommerce
by apify
Scrape e-commerce data for pricing intelligence, customer reviews, and seller discovery across Amazon, Walmart, eBay, IKEA, and 50+ marketplaces. Use when user asks to monitor prices, track competitors, analyze reviews, research products, or find sellers.
latex-paper-en
by bahayonghang
LaTeX academic paper assistant for English papers (IEEE, ACM, Springer, NeurIPS, ICML). Use when writing, reviewing, compiling, or improving English LaTeX academic papers. Use when user mentions compile, grammar, bibliography, deai, translate, title, logic, reviewer perspective, or any LaTeX paper quality improvement task. Domains: Deep Learning, Time Series, Industrial Control.
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.
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.
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.
orchestrating-swarms
by EveryInc
This skill should be used when orchestrating multi-agent swarms using Claude Code's TeammateTool and Task system. It applies when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.
ce-retune
by EveryInc
"Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one."
uipath-maestro-flow
by UiPath
"TRIGGER for .flow / .flow.ts files and UiPath Flow / Maestro Flow / Maestro Automate requests: build, edit, run, debug, fix, or evaluate a flow. Author with the TypeScript builder SDK (@uipath/maestro-builder-sdk): nodes, triggers, schedules, connectors, IXP document extraction (add or list IXP models), inline agents, HITL, chat/voice flows, bindings, brownfield edits, and the check/compile/validate loop. Operate: upload, publish, deploy, debug a real run, trigger a process, job status/traces, pause/resume/cancel/retry an instance. Diagnose: faulted runs, incidents, runtime variables, why validate passed but the run failed. Evaluate: eval sets, evaluators, simulations, eval runs. Case plans (caseplan.json) → uipath-maestro-case; BPMN (.bpmn, .bpmn.ts) → uipath-maestro-bpmn. DO NOT TRIGGER for raw IXP labelling or prompt tuning outside a Flow → uipath-ixp; standalone agents → uipath-agents; C#/XAML → uipath-rpa."
skills
by UiPath
This is a repository of skills for interfacing UiPath capabilities to external developers.
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.
medical-imaging-review
by luwill
Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
subagents-orchestration-guide
by shinpr
Guides subagent coordination through implementation workflows. Use when orchestrating multiple agents, managing workflow phases, or determining autonomous execution mode. Defines scale determination, document requirements, and stop points.