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Data & AI

Machine learning, LLMs, and data processing.

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machine-learning

641

add-model

Add a new AI model to the Pipelex inference system. Guides through all required steps: backend TOML configuration (OpenAI, Azure, Anthropic, Google, etc.), kit sync, test profile collections, and fixture regeneration. Use when the user says "add a model", "add GPT-X", "add Claude X", "new model", "register a model", "add Gemini X", "support model X", "add model to backend", or any variation of introducing a new AI model to the inference configuration. Also use when the user mentions a model name that doesn't exist in the backend configs yet and wants to add it.

Pipelex

data-ai

machine-learning

641

test-model

Test an AI model on a specific backend using the Pipelex inference test infrastructure. Handles test profile creation, fixture regeneration, and running the right test class for the model type (LLM, image gen, extract, search). Use when the user says "test model X", "test gpt-5.4 on openai", "test model on gateway", "run inference test for model", "try model X on backend Y", "verify model X works", or any variation of running inference tests against a specific model on a specific backend. Also use when the user mentions testing a model after adding it, or wants to verify a model works end-to-end with real API calls.

Pipelex

data-ai

data-analysis

639

coding-sop

Standard operating procedure for research experiments, data analysis, and visualization. Covers Python/R script execution, statistical analysis, data wrangling (pandas/tidyverse), publication-quality figures (matplotlib/plotly/ggplot2), and experiment reproducibility.

wentorai

data-ai

machine-learning

639

plotting-sop

Standard operating procedure for academic figure generation. Four rendering engines: Python (data viz), Mermaid (flowcharts), AI Image via NanoBanana/OpenRouter (complex diagrams), SVG (vector). Includes engine selection decision tree, ReAct self-correction, NanoBanana configuration, environment detection, and academic style rules.

wentorai

data-ai

data-analysis

637

analyzing-user-feedback

Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.

RefoundAI

data-ai

data-analysis

637

post-mortems-retrospectives

Help users run effective post-mortems and retrospectives. Use when someone is reviewing a project that succeeded or failed, wants to establish learning practices, is dealing with failure aftermath, or needs to improve team learning loops.

RefoundAI

data-ai

data-analysis

637

retention-engagement

Help users improve retention and engagement metrics. Use when someone is dealing with churn, optimizing activation flows, building habit-forming products, or trying to increase user engagement and lifetime value.

RefoundAI

data-ai

data-engineering

637

wren-connection-info

Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.

Canner

data-ai

data-engineering

637

wren-dlt-connector

Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.

Canner

data-ai

machine-learning

637

building-with-llms

Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.

RefoundAI

data-ai

machine-learning

634

haskell-pro

Expert Haskell engineer specializing in advanced type systems, pure functional design, and high-reliability software. Use PROACTIVELY for type-level programming, concurrency, and architecture guidance.

rmyndharis

data-ai

data-engineering

634

dbt-transformation-patterns

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

rmyndharis

data-ai

data-engineering

634

data-engineering-data-pipeline

You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

rmyndharis

data-ai

data-engineering

634

spark-optimization

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

rmyndharis

data-ai

data-engineering

634

data-quality-frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

rmyndharis

data-ai

machine-learning

634

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 and deployment workflows.

rmyndharis

data-ai

data-engineering

634

database-architect

Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.

rmyndharis

data-ai

data-engineering

634

data-engineer

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.

rmyndharis

data-ai

data-engineering

634

airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

rmyndharis

data-ai

data-engineering

634

temporal-python-pro

Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.

rmyndharis

data-ai

machine-learning

634

prompt-engineer

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.

rmyndharis

data-ai

machine-learning

634

mlops-engineer

Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

rmyndharis

data-ai

machine-learning

634

vector-index-tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

rmyndharis

data-ai

data-engineering

634

scala-pro

Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures. Use PROACTIVELY for Scala system design, performance optimization, or enterprise integration.

rmyndharis

data-ai

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