ML Ops
Machine learning operations
worktree
by HikaruEgashira
Planが承認/完了した直後に自律的に呼び出す必要があるスキルです。 Trigger: plan approved, plan completed, taskを開始します
ask-laravel-architect
by NavanithanS
Laravel scaffolding for SQL or Mongo (Official/Jenssegers), SoftDeletes, API standards.
gcp-batch-inference
by viktor-ferenczi
Running batch inference on Google Cloud (also known as Vertex AI)
deepagents-overview
by christian-bromann
Understanding Deep Agents framework - what they are, how to create them with createDeepAgent, and the agent harness architecture with built-in middleware for planning, filesystems, and subagents.
understanding-db-schema
by C0ntr0lledCha0s
Deep expertise in Logseq's Datascript database schema. Auto-invokes when users ask about Logseq DB schema, Datascript attributes, built-in classes, property types, entity relationships, schema validation, or the node/block/page data model. Provides authoritative knowledge of the DB graph architecture.
implement-paper-from-scratch
by GhostScientist
Guides you through implementing a research paper step-by-step from scratch. Use when asked to implement a paper, code up a paper, reproduce research results, or build a model from a paper. Focuses on building understanding through implementation with checkpoint questions.
modal-deployment
by ferdousbhai
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch jobs, scheduling tasks, serving APIs with GPU acceleration, or scaling compute-intensive workloads. Triggers on requests for serverless GPU infrastructure, LLM inference, model training/fine-tuning, parallel data processing, cron jobs in the cloud, or deploying Python web endpoints.
swiftui-view-refactor
by dagba
Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.
world-labs-text-prompt
by CloudAI-X
Text-to-world generation best practices, prompt structure, style descriptors
building-commands
by C0ntr0lledCha0s
Expert at creating and modifying Claude Code slash commands. Auto-invokes when creating/updating commands, modifying command frontmatter (model, allowed-tools, argument-hint), designing workflows, or writing to /commands/.md files.
project-development
by ken-cavanagh-glean
Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
langchain-multimodal
by christian-bromann
Work with multimodal inputs/outputs in LangChain - includes images, audio, video, content blocks, and vision capabilities
pal-mcp-expert
by mamba-mental
Expert guidance for using the Pal MCP Server (zen-pal-nas). This skill should be used when working with multi-model AI orchestration, tool workflows (chat, thinkdeep, planner, consensus, debug, codereview, precommit, clink), configuration troubleshooting, or optimizing model selection strategies. Activates automatically when user mentions Pal MCP, zen-pal-nas, or specific tool names.
rust-ml
by peixotorms
Use when building machine learning or AI inference in Rust. Covers inference, model loading, tensor operations, GPU and CUDA acceleration, batch processing, feature extraction, embeddings, tokenizer, hugging face integration, deep learning, ONNX with tract, model singletons with OnceLock, candle, tch-rs, ndarray tensors, and data pipelines with polars.
buildkite
by HJewkes
Buildkite CI/CD integration. Use when the user needs to check build status, trigger builds, read build logs, debug failures, manage pipelines, or any Buildkite workflow. Triggers include "buildkite", "build", "pipeline", "CI", "deploy", "build log", "build failed".
context-fundamentals
by ken-cavanagh-glean
Understand the components, mechanics, and constraints of context in agent systems. Use when designing agent architectures, debugging context-related failures, or optimizing context usage.
langchain-structured-output
by christian-bromann
Get structured, validated output from LangChain agents and models using Pydantic schemas, type-safe responses, and automatic validation
cost-planning-for-solana-apps
by SanctifiedOps
Estimate and control costs for Solana apps: RPC, indexing, storage, bots, and on-chain fees. Use for budgeting and scaling.
hugging-face-space-deployer
by GhostScientist
Create, configure, and deploy Hugging Face Spaces for showcasing ML models. Supports Gradio, Streamlit, and Docker SDKs with templates for common use cases like chat interfaces, image generation, and model comparisons.
langchain-tool-calling
by christian-bromann
How chat models call tools - includes bindTools, tool choice strategies, parallel tool calling, and tool message handling
langchain-rag
by christian-bromann
Build Retrieval Augmented Generation (RAG) systems with LangChain - includes embeddings, vector stores, retrievers, document loaders, and text splitting
advanced-evaluation
by ken-cavanagh-glean
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
langchain-chat-models
by christian-bromann
Guide to using chat model integrations in LangChain including OpenAI, Anthropic, Google, Azure, and Bedrock
langchain-models
by christian-bromann
Initialize and use LangChain chat models - includes provider selection (OpenAI, Anthropic, Google), model configuration, and invocation patterns