GitHub
context-engineering
[ARCHIVED] Full 4D Context Canvas reference. For new AI features, use /spec --ai. For debugging, use /ai-debug. For quality checks, use /context-check.
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tools machine learning AI Skills
Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
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calibrate
Post-launch AI feature calibration workflow. Document error patterns, review eval performance, and decide on agency promotion. Based on CC/CD framework for continuous calibration of AI products.
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GitHub
prompt-engineering
Expert prompt optimization system for building production-ready AI features. Use when users request help improving prompts, want to create system prompts, need prompt review/critique, ask for prompt optimizatio...
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GitHub
text-analyst
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (L...
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GitHub
hugging-face-evaluation
Add and manage evaluation results in Hugging Face model repositories using the new .eval_results/ format. Supports extracting scores from model cards, importing from Artificial Analysis API, and batch processin...
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GitHub
evaluation-harness
Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmark...
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GitHub
cost-latency-optimizer
Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost re...
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GitHub
rlm
Recursive Language Model for processing large contexts (>50KB). Use for complex analysis tasks where token efficiency matters. Achieves 40% token savings by letting the LLM programmatically explore context via...
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GitHub
using-ai-engineering
Route AI/ML tasks to correct Yzmir pack - frameworks, training, RL, LLMs, architectures, production
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GitHub
using-dynamic-architectures
Use when building networks that grow, prune, or adapt topology during training. Routes to continual learning, gradient isolation, modular composition, and lifecycle orchestration skills.
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GitHub
using-ml-production
Router skill directing to deployment, optimization, MLOps, and monitoring guides.
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GitHub
using-training-optimization
Router to training optimization skills based on symptoms and training problems
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GitHub
using-llm-specialist
LLM specialist router to prompt engineering, fine-tuning, RAG, evaluation, and safety skills.
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GitHub
using-neural-architectures
The architecture selection router for CNNs, Transformers, RNNs, GANs, GNNs by data modality and constraints
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GitHub
adaptive-wfo-epoch
Adaptive epoch selection for Walk-Forward Optimization using efficient frontier analysis. Per-fold epoch sweeps with WFE-based selection and carry-forward priors. TRIGGERS - epoch selection, WFO epoch, walk-for...
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GitHub
pytorch-ml
Deep learning with PyTorch - model training, GPU acceleration, and data science workflows
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GitHub
pal-mcp-server
AI orchestration and multi-model collaboration MCP server. Use when you need multi-model AI collaboration, code reviews, debugging assistance, planning, consensus building, or advanced reasoning across multiple...
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GitHub
ask-council
Multi-model ensemble consultation. Runs 3 models in parallel for diverse perspectives.
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GitHub
bayesian-optimizer
Optimizes black-box functions (e.g., experimental yield) using Gaussian Processes, ideal for self-driving labs.
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GitHub
mc-waypoint
Labels a coordinate with a waypoint name for reasoning about spatial relationships. Use to mark important locations for navigation and planning
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GitHub
mc-training-capture
Enable or disable automatic training data capture (partial observation grid + optional ground truth).
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GitHub
data-science
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, analy...
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GitHub
ai-ml-development
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.
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GitHub
senior-ml-engineer
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructu...
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