GitHub
flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
☆ 272View skill
tools machine learning AI Skills
Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
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GitHub
prompt-optimizer
Prompt engineering expert that helps users craft optimized prompts using 57 proven frameworks. Use when users want to optimize prompts, improve AI instructions, create better prompts for specific tasks, or need...
☆ 272View skill
GitHub
minimax-usage
Monitor Minimax Coding Plan usage to stay within API limits. Fetches current usage stats and provides status alerts.
☆ 230View skill
GitHub
pi-orchestration
Orchestrate multiple AI models (GLM, MiniMax, etc.) as workers using Pi Coding Agent with Claude as coordinator.
☆ 229View skill
GitHub
multi-model-validation
Run multiple AI models in parallel for 3-5x speedup with ENFORCED performance statistics tracking. Use when validating with Grok, Gemini, GPT-5, DeepSeek, MiniMax, Kimi, GLM, or Claudish proxy for code review,...
☆ 224View skill
GitHub
openrouter-trending-models
Fetch trending programming models from OpenRouter rankings. Use when selecting models for multi-model review, updating model recommendations, or researching current AI coding trends. Provides model IDs, context...
☆ 224View skill
GitHub
proxy-mode-reference
Reference guide for using PROXY_MODE with external AI models. Use when running multi-model reviews, understanding which agents support PROXY_MODE, or debugging external model integration issues. Includes routin...
☆ 224View skill
GitHub
model-tracking-protocol
MANDATORY tracking protocol for multi-model validation. Creates structured tracking tables BEFORE launching models, tracks progress during execution, and ensures complete results presentation. Use when running...
☆ 224View skill
GitHub
coot-model-building
Best Practices for Model-Building Tools and Refinement
☆ 154View skill
GitHub
regression-analysis-modeling
Perform comprehensive regression analysis and predictive modeling using linear regression, decision trees, and random forests. Use when you need to predict continuous values like housing prices, sales forecasts...
☆ 152View skill
GitHub
mlflow-evaluation
MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guid...
☆ 146View skill
GitHub
tinker-training-cost
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 corr...
☆ 141View skill
GitHub
tinker
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.
☆ 141View skill
GitHub
training-data-curation
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.
☆ 141View skill
GitHub
ml-rigor
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
☆ 141View skill
GitHub
ml-rigor
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
☆ 141View skill
GitHub
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt temp...
☆ 129View skill
GitHub
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing...
☆ 129View skill
GitHub
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training a...
☆ 129View skill
GitHub
ml-training-debugger
Diagnose machine learning training failures including loss divergence, mode collapse, gradient issues, architecture problems, and optimization failures. This skill spawns a specialist ML debugging ...
☆ 124View skill
GitHub
logprob-prefill-analysis
Reproduces the full prefill sensitivity analysis pipeline for reward hacking indicators. Use when evaluating how susceptible model checkpoints are to exploit-eliciting prefills, computing token-based trajectori...
☆ 124View skill
GitHub
learn-off
Disable continuous learning mode
☆ 124View skill
GitHub
prompt-optimization
Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.
☆ 124View skill
GitHub
cascade-orchestrator
Creates sophisticated workflow cascades coordinating multiple micro-skills with sequential pipelines, parallel execution, conditional branching, and Codex sandbox iteration. Enhanced with multi-model routing (G...
☆ 124View skill
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