machine-learning
195
formal-verification-guide
Formal methods, theorem proving, and model checking for CS research
wentorai
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
195
species-distribution-guide
Species distribution modeling with MaxEnt, SDM methods, and GBIF data
wentorai
data-ai
machine-learning
195
lean-theorem-proving-guide
LLM agent for formal theorem proving in Lean 4
wentorai
data-ai
machine-learning
195
python-reproducibility-guide
Reproducible Python environments, notebooks, and literate programming
wentorai
data-ai
machine-learning
193
explorer-tidx
Explorer-only TIDX migration skill (PG-first, CH fallback for count-heavy paths)
tempoxyz
data-ai
machine-learning
189
human-machine-brainstorm
This skill should be used when the user asks to "人机风暴", "Human-Machine Brainstorm", "human storm", "ccb brainstorm", "需求对齐调度", "spec convergence", or wants a CCB-based multi-model requirement alignment loop with Codex as the dispatcher.
cnfjlhj
data-ai
machine-learning
185
account-aware-training
Add account state (P&L, win rate, drawdown) to RL observations + drawdown penalty in rewards. Trigger when: (1) model needs account awareness, (2) training should penalize drawdowns, (3) upgrading obs_dim 5300→5600.
majiayu000
data-ai
machine-learning
185
add-awesome-tool
This skill should be used when analyzing a link to an AI tool and adding it to the awesome-ai-tools readme with proper categorization
majiayu000
data-ai
machine-learning
185
agent-model-selection
Guidelines for selecting appropriate AI model (Sonnet vs Haiku) based on task complexity, ensuring cost efficiency while maintaining quality. Use when assigning work.
majiayu000
data-ai
machine-learning
185
base-model-selector
Use when starting a fine-tuning project to determine if fine-tuning is needed, or when evaluating whether a base model meets quality thresholds for a specific domain task
majiayu000
data-ai
machine-learning
185
baseline-quality-assessment
Achieve comprehensive baseline (V_meta ≥0.40) in iteration 0 to enable rapid convergence. Use when planning iteration 0 time allocation, domain has established practices to reference, rich historical data exists for immediate quantification, or targeting 3-4 iteration convergence. Provides 4 quality levels (minimal/basic/comprehensive/exceptional), component-by-component V_meta calculation guide, and 3 strategies for comprehensive baseline (leverage prior art, quantify baseline, domain universality analysis). 40-50% iteration reduction when V_meta(s₀) ≥0.40 vs <0.20. Spend 3-4 extra hours in iteration 0, save 3-6 hours overall.
majiayu000
data-ai
machine-learning
185
cns-tinker
Apply Chiral Narrative Synthesis (CNS) framework for contradiction detection and multi-source analysis using Tinker API for model training. Use when implementing CNS with Tinker for fine-tuning models on contradiction detection, training on SciFact/FEVER datasets, or building multi-agent debate systems for narrative synthesis.
majiayu000
data-ai
machine-learning
185
create-test-lane
This skill should be used when creating new test lanes for the XML test data generator. A test lane consists of an XSD schema file paired with a meta.yaml configuration file. This skill guides the process of creating both files with proper semantic type mappings, distribution settings, and field overrides. Use when users request new test lanes, want to generate test data configurations, or need help setting up XSD + meta.yaml pairs for the testgen CLI tool.
majiayu000
data-ai
machine-learning
185
data-ai-ml-skill
Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.
majiayu000
data-ai
machine-learning
185
data-ai-skills
Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.
majiayu000
data-ai
machine-learning
185
huggingface-model-trainer
Train and fine-tune LLMs using HuggingFace TRL, Transformers, and cloud GPU infrastructure with SFT, DPO, GRPO methods
majiayu000
data-ai
machine-learning
185
laravel-models
Eloquent model patterns and database layer. Use when working with models, database entities, Eloquent ORM, or when user mentions models, eloquent, relationships, casts, observers, database entities.
majiayu000
data-ai
machine-learning
185
baseline-runner
Use this when the project needs real baseline results before or alongside the main model. Runs classical or literature-aligned baselines under the same protocol and writes a reproducible baseline summary.
tsingyuai
data-ai
machine-learning
185
research-pipeline
Use this skill when the user wants to research a topic, analyze papers, build ML models, or run experiments. Orchestrates the full pipeline: paper search → analysis → planning → implementation → review → experiments.
tsingyuai
data-ai
machine-learning
153
coot-model-building
Best Practices for Model-Building Tools and Refinement
pemsley
data-ai
machine-learning
138
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 correct model tokenizer and provides accurate cost estimates.
sundial-org
data-ai