machine-learning
1K
cross-validation-setup
Cross Validation Setup - Auto-activating skill for ML Training. Triggers on: cross validation setup, cross validation setup Part of the ML Training skill category.
jeremylongshore
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
1K
gradient-clipping-helper
Gradient Clipping Helper - Auto-activating skill for ML Training. Triggers on: gradient clipping helper, gradient clipping helper Part of the ML Training skill category.
jeremylongshore
data-ai
machine-learning
1K
setting-up-experiment-tracking
This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of different model runs.
jeremylongshore
data-ai
machine-learning
1K
setting-up-experiment-tracking
This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of different model runs.
jeremylongshore
data-ai
llm-ai
1K
skill-creator
Design and create Agent Skills using progressive disclosure principles. Use when building new skills, planning skill architecture, or writing skill content.
hotovo
data-ai
data-analysis
1K
analyzing-marketing-campaign
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
https-deeplearning-ai
data-ai
data-analysis
1K
analyzing-marketing-campaign
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
https-deeplearning-ai
data-ai
data-analysis
989
temporal
Temporal graph operations on Semantica — scoped queries at a point in time, graph snapshots, node change timelines, temporal causal analysis, and graph state reconstruction. Uses AgentContext.find_precedents(as_of=), ContextGraph.state_at(), CausalChainAnalyzer.trace_at_time(), and TemporalQueryRewriter. Sub-commands: query, snapshot, timeline, causal-at, precedents-at.
Hawksight-AI
data-ai
data-analysis
989
visualize
Visualize the Semantica knowledge graph — topology, centrality, communities, paths, embeddings, decision insights, and temporal evolution. Uses GraphAnalyzer, CentralityCalculator, CommunityDetector, PathFinder, and ContextGraph analytics. Sub-commands: topology, centrality, community, path, decision-graph, insights, temporal, embedding.
Hawksight-AI
data-ai
data-analysis
989
deduplicate
Detect duplicate entities, duplicate groups, and relationship duplicates in Semantica using fuzzy matching, schema heuristics, and graph similarity.
Hawksight-AI
data-ai
data-analysis
987
data-model-creation
Optional advanced tool for complex data modeling. For simple table creation, use relational-database-tool directly with SQL statements.
TencentCloudBase
data-ai
data-engineering
987
typescript-bun-drizzle-quality
Build or review Bun fullstack TypeScript code with Drizzle-backed SQL. Use for backend or cross-layer changes touching API/domain logic, schema or query design, migrations, runtime/type debugging, and boundary validation between contracts, business rules, and persistence.
databuddy-analytics
data-ai
machine-learning
974
hugging-face-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
huggingface
data-ai
llm-ai
974
hugging-face-cli
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
huggingface
data-ai
machine-learning
974
hugging-face-evaluation
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
huggingface
data-ai
llm-ai
974
hugging-face-jobs
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
huggingface
data-ai
machine-learning
974
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
huggingface
data-ai
data-engineering
972
analyze-spec
Socratic deep-interview analysis of a spec file to ensure zero ambiguity before implementation
a16z
data-ai
data-engineering
971
batch
Research and plan a large-scale change, then execute it in parallel across 5-30 isolated worktree agents that each open a PR. Use when the user wants to make a sweeping, mechanical change across many files (migrations, refactors, bulk renames) that can be decomposed into independent parallel units.
remorses
data-ai
llm-ai
961
creating-hat-collections
Use when creating new Ralph hat collection presets, designing multi-agent workflows, or adding hats to existing presets
mikeyobrien
data-ai