data-engineering
1.7K
add-migration
Creates a Flyway Java-based migration for schema changes. Handles table creation, column additions, tenant isolation, and ES reindex. Use when asked to modify the database schema.
OpenAEV-Platform
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
1.7K
skill-optimizer
Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.
mcollina
data-ai
machine-learning
1.6K
django-create-model
Steps for creating or modifying a Django model
fossasia
data-ai
machine-learning
1.6K
openakita-skills-baidu-famou
Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.
openakita
data-ai
data-engineering
1.6K
data-designer
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
NVIDIA-NeMo
data-ai
data-analysis
1.6K
pdf-markdown-validator
Validate PDF to Markdown conversion quality using multi-dimensional metrics. Assess table accuracy, style preservation (bold/italic/headings), robustness, and performance with standardized F1-scoring methodology.
raphaelmansuy
data-ai
machine-learning
1.5K
rails-lesson-recipes
Use this skill whenever creating a new lesson, choosing a lesson pattern, or validating lesson structure. Trigger on: 'create a lesson', 'lesson recipe', 'lesson pattern', 'terminal-only lesson', 'code editing lesson', 'database lesson', 'console lesson', 'lesson sequence', 'multi-step lesson', 'validate lesson', 'check lesson', or asking how to structure a Rails lesson — even without mentioning recipes. Provides five tested blueprints (terminal-only, code-editing, database, full-app, console/IRB) with correct frontmatter, directory layouts, content examples, and a post-creation validation checklist. Do NOT create lessons without this skill — incorrect structure causes silent failures. Do NOT use for frontmatter reference (use tutorial-lesson-config) or file organization (use rails-file-management).
palkan
data-ai
machine-learning
1.5K
rails-wasm-author-constraints
Use this skill whenever checking if a Rails feature or gem works in WASM, or understanding what's real vs. conceptual in lessons. Trigger on: 'does X work in WASM', 'can I use this gem', 'gem compatibility', 'WASM limitation', 'what works', 'supported features', 'PGLite', 'threading', 'Net::HTTP', 'ActionCable', 'background jobs', 'can I teach X', 'bundle install in WASM', 'conceptual vs real', or any Rails capability question — even without mentioning WASM. Authoritative compatibility matrix: which features work, which are shimmed, which are impossible, plus gem tiers, PGLite behavior, boot timing, and conceptual-vs-real operations. General Rails knowledge is insufficient here. Do NOT use for file organization (use rails-file-management) or frontmatter (use tutorial-lesson-config).
palkan
data-ai
machine-learning
1.5K
tutorial-lesson-config
Use this skill whenever configuring lesson frontmatter or checking inheritance rules. Trigger on: 'frontmatter', 'prepareCommands', 'mainCommand', 'editor config', 'terminal config', 'preview config', 'configuration inheritance', 'focus file', 'i18n', 'allowEdits', 'previews', 'autoReload', 'scope', 'defaults', 'what inherits', 'does X inherit', 'invalid combination', 'constraint', or any YAML frontmatter question — even without mentioning configuration. Authoritative reference for all frontmatter options, inheritance rules, defaults, and invalid-combination constraints with Rails-specific patterns. Do NOT guess frontmatter without this skill. Do NOT use for content hierarchy (use tutorial-content-structure) or file organization (use rails-file-management).
palkan
data-ai
machine-learning
1.5K
benchmark-model
Benchmark inference performance for a specific model
trymirai
data-ai
machine-learning
1.5K
list-downloaded-models
Get list of currently downloaded models
trymirai
data-ai
llm-ai
1.5K
simplemem-skill
Store and retrieve conversation memories across sessions. Use when asked to 'remember this', 'save conversation', 'add to memory', 'what did we discuss about...', 'query memories', or 'import chat history'. Also use proactively to preserve important dialogue context and decisions.
aiming-lab
data-ai
machine-learning
1.5K
choosing-a-forecaster
Guides selection of the appropriate skforecast forecaster based on the user's data characteristics and requirements. Provides a decision matrix mapping use cases to forecaster classes. Use when the user is unsure which forecaster to use or asks for a recommendation.
skforecast
data-ai
machine-learning
1.5K
complete-api-reference
Complete constructor signatures and method signatures for all skforecast forecasters, backtesting functions, search functions, cross-validation classes, preprocessing, feature selection, and drift detection. Use when the user needs exact parameter names, types, or defaults for any skforecast class or function.
skforecast
data-ai
machine-learning
1.5K
deep-learning-forecasting
Forecasts time series using recurrent neural networks (RNN, LSTM, GRU) with ForecasterRnn and the create_and_compile_model helper. Covers model architecture, training, and multi-series deep learning. Use when the user wants to use deep learning / neural networks for time series forecasting.
skforecast
data-ai
machine-learning
1.5K
feature-engineering
Creates features for time series forecasting: calendar features with feature_engine (DatetimeFeatures, CyclicalFeatures), rolling statistics with RollingFeatures, differencing, sunlight features, and data scaling. Use when the user wants to improve model accuracy through feature engineering or asks about exogenous variable creation.
skforecast
data-ai
machine-learning
1.5K
forecasting-multiple-series
Forecasts multiple time series simultaneously using a global model with ForecasterRecursiveMultiSeries or ForecasterDirectMultiVariate. Covers data formats, encoding, per-series transformers, and multi-series backtesting. Use when the user has two or more related time series.
skforecast
data-ai
machine-learning
1.5K
forecasting-single-series
Forecasts a single time series using ForecasterRecursive or ForecasterDirect. Covers data preparation, model creation, training, prediction, backtesting, and prediction intervals. Use when the user needs to predict future values of one time series.
skforecast
data-ai
machine-learning
1.5K
hyperparameter-optimization
Optimizes forecaster hyperparameters using grid search, random search, or Bayesian search (Optuna). Covers single-series and multi-series search, cross-validation configuration, and search space definition. Use when the user wants to find the best model configuration.
skforecast
data-ai
data-engineering
1.4K
go-to-production
Production readiness checklist for durable streams. Switch from dev server to Caddy binary, configure CDN caching with offset-based URLs, Cache-Control and ETag headers, Stream-Cursor for cache collision prevention, TTL and Stream-Expires-At for stream lifecycle, HTTPS requirement, request collapsing for fan-out, CORS configuration. Load before deploying durable streams to production.
durable-streams
data-ai
data-engineering
1.4K
writing-data
Writing data to durable streams. DurableStream.create() with contentType, DurableStream.append() for simple writes, IdempotentProducer for high-throughput exactly-once delivery with autoClaim, fire-and-forget append(), flush(), close(), StaleEpochError handling, JSON mode vs byte stream mode, stream closure. Load when writing, producing, or appending data to a durable stream.
durable-streams
data-ai
data-engineering
1.4K
state-schema
Defining typed state schemas for @durable-streams/state. createStateSchema() with CollectionDefinition (schema, type, primaryKey), Standard Schema validators (Zod, Valibot, ArkType), event helpers insert/update/delete/upsert, ChangeEvent and ControlEvent types, State Protocol operations, transaction IDs (txid) for write confirmation. Load when defining entity types, choosing a schema validator, or creating typed change events.
durable-streams
data-ai