ML Ops
Machine learning operations
ln-1000-pipeline-orchestrator
by levnikolaevich
"Meta-orchestrator (L0): reads kanban board, drives Stories through pipeline 300->310->400->500 in parallel via TeamCreate. Max 3 concurrent Stories. Auto squash-merge to develop on quality gate PASS."
implement-feature
by tddworks
Guide for implementing features in ClaudeBar following architecture-first design, TDD, rich domain models, and Swift 6.2 patterns. Use this skill when: (1) Adding new functionality to the app (2) Creating domain models that follow user's mental model (3) Building SwiftUI views that consume domain models directly (4) User asks "how do I implement X" or "add feature Y" (5) Implementing any feature that spans Domain, Infrastructure, and App layers
single-cell-multi-omics-integration
by Starlitnightly
Quick-reference sheet for OmicVerse tutorials spanning MOFA, GLUE pairing, SIMBA integration, TOSICA transfer, and StaVIA cartography.
bulk-rna-seq-deconvolution-with-bulk2single
by Starlitnightly
Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.
single2spatial-spatial-mapping
by Starlitnightly
Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.
pipeline-model-architecture
by TencentBlueKing
BK-CI 流水线核心模型（Model）架构详解，涵盖 Pipeline/Stage/Container/Task 四层结构、模型序列化、版本管理、模型校验。当用户理解流水线数据结构、开发流水线功能、处理模型转换或进行模型扩展时使用。
nix-config
by malob
Disable auto TTS for this session
principle-outcome-oriented-execution
by cursor
"Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code."
principle-model-the-domain
by cursor
"Apply when writing stateful logic, or when code branches a lot or repeats a shape assumption across files. Encode the domain in a structure instead of scattered conditionals."
python-panel-data
by meleantonio
Panel data analysis with Python using linearmodels and pandas.
WorldThreatModelHarness
by danielmiessler
Persistent world model system across 11 time horizons (6mo→50yr) for adversarial analysis of ideas, strategies, and investments. USE WHEN threat model, world model, test idea, test strategy, future analysis, test investment, how will this hold up, test against future, update world models, view world models, time horizon analysis, adversarial future test, stress test idea.
create-squad
by VapiAI
Create multi-assistant squads in Vapi with handoffs between specialized voice agents. Use when building complex voice workflows that need multiple assistants with different roles, like triage-to-booking or sales-to-support handoffs.
TRL Training on Hugging Face Jobs
by patchy631
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Overview
by patchy631
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Trackio - Experiment Tracking for ML Training
by patchy631
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
dstack-presets
by dstackai
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.
sap-ai-core
by secondsky
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
sap-hana-ml
by secondsky
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
stata-regression
by meleantonio
Run regression analyses in Stata with publication-ready output tables.
/model-switch - Agent CLI Live Switcher
by yohey-w
Samurai-inspired multi-agent system for Claude Code. Orchestrate parallel AI tasks via tmux with shogun → karo → ashigaru hierarchy.
/shogun-bloom-config — Bloom Routing Wizard
by yohey-w
Samurai-inspired multi-agent system for Claude Code. Orchestrate parallel AI tasks via tmux with shogun → karo → ashigaru hierarchy.
video-generate
by bytedance
Generate videos using Seedance models. Invoke when user wants to create videos from text prompts, images, or reference materials.
python-pipeline
by jamditis
Python data processing pipelines with modular architecture. Use when building content processing workflows, implementing dispatcher patterns, integrating Google Sheets/Drive APIs, or creating batch processing systems. Covers patterns from rosen-scraper, image-analyzer, and social-scraper projects.
numerai-model-upload
by numerai
Create Numerai Tournament model upload pickles (.pkl) with a self-contained predict() function. Use when preparing upload artifacts, debugging numerai_predict import errors, or documenting model-upload requirements and testing steps.