ML Ops
Machine learning operations
fal-audio
by fal-ai-community
Text-to-speech and speech-to-text using fal.ai audio models. Use when the user requests "Convert text to speech", "Transcribe audio", "Generate voice", "Speech to text", "TTS", "STT", or similar audio tasks.
jenkinsfile-validator
by akin-ozer
Comprehensive toolkit for validating, linting, testing, and automating Jenkinsfile pipelines (both Declarative and Scripted). Use this skill when working with Jenkins pipeline files, validating pipeline syntax, checking best practices, debugging pipeline issues, or working with custom plugins.
start-feature
by leeovery
"Start a new feature through the full pipeline. Gathers context via structured interview, creates a discussion, then bridges to continue-feature for specification, planning, and implementation."
pennylane
by unitarylab
PennyLane - A versatile quantum machine learning library that supports hybrid quantum-classical computations.
qnn
by unitarylab
Skill for understanding, using, and implementing the Quantum Neural Network (QNN) with parameterized quantum circuits for supervised learning via the QNNAlgorithm class.
runtime-skills
by llama-farm
Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.
visionos-design-guidelines
by ehmo
Apple Human Interface Guidelines for Apple Vision Pro. Use when building spatial computing apps, implementing eye/hand input, or designing immersive experiences. Triggers on tasks involving visionOS, RealityKit, spatial UI, or mixed reality.
data-science
by travisjneuman
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
stanford-vibe-coding-course
by z1993
斯坦福 CS146S "The Modern Software Developer" AI开发课程学习系统。Use when: 用户提到 cs146s, stanford课程, vibe coding, AI开发学习, 或"开始学习"。
content-modeling
by adobe
Create effective content models for your blocks that are easy for authors to work with. Use this skill anytime you are building new blocks, making changes to existing blocks that modify the initial structure authors work with.
training-data-curation
by sundial-org
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.
tinker
by sundial-org
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.
tinker-training-cost
by sundial-org
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
gemini
by softaworks
Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.
feedback-mastery
by softaworks
Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.
pinecone:mcp
by pinecone-io
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.
pyhealth
by jaechang-hits
"PyHealth is a Python library for healthcare machine learning. Build clinical prediction models from EHR (Electronic Health Record) data: process MIMIC-III/IV, eICU, and OMOP-CDM datasets, encode medical codes (ICD, ATC, NDC), construct patient-level datasets, and train models (Transformer, RETAIN, GRASP, MedBERT) for tasks including mortality prediction, drug recommendation, readmission, and diagnosis prediction. Alternatives: FIDDLE (EHR preprocessing only), clinical-longformer (NLP on clinical notes only), ehr-ml (EHR embedding only)."
who-are-you
by Soul-Brews-Studio
Know ourselves - show identity, model info, session stats, and Oracle philosophy. Use when user asks "who are you", "who", "who we are", or wants to check current AI identity.
watch
by Soul-Brews-Studio
Learn from YouTube videos via Gemini transcription. Use when user says "watch", "transcribe youtube", "learn from video", or shares a YouTube URL to study.
causal-inference-root-cause
by lyndonkl
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
ai-ml-development
by travisjneuman
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
Google ADK Python Skill
by einverne
Remember: ADK treats agent development like traditional software engineering - use version control, write tests, and follow engineering best practices.
llamafile
by Jamie-BitFlight
When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.
discovery
by joelhooks
"Capture interesting finds to the Vault via Inngest. Triggers when the user shares a URL, repo, or idea with signal words like \"interesting\", \"cool\", \"neat\", \"check this out\", \"look at this\", \"came across\", or when sharing content with minimal context that implies it should be remembered. Also triggers on bare URL drops with no explicit ask. Fires a discovery/noted event and continues the conversation — the pipeline handles everything else."