Results for “product-data”

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations from Arize AI.

Turn any text into a faceless explainer video with typography, abstract graphics, diagrams, and data visualization.

For analysis/task/review agents using a Professional Skill on batch, stream, warehouse, lineage, replay, or data quality; not for transactions without distributed-data impact.

Generate production-ready Python code for Dataverse SDK with advanced patterns including error handling, batch operations, OData optimization, and Pandas integration.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Uploads audience members to Google products like Customer Match and mobile device ID audiences using the Data Manager API.

Use this skill for on-chain data, explorers, Dune-style queries, wallets, transfers, contract events. Trigger when the task involves crypto work related to Onchain Data Analytics, production implementation, audits, debugging, strategy, or validation.

Transforms scattered competitor data into a decision-ready product strategy report with positioning maps, feature matrices, pricing analysis, and opportunity windows.

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.

Extract Amazon product reviews by ASIN using the BrowserAct API, returning structured data including ratings, text, reviewer info, and verified purchase status.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.

Design and implement production-grade GraphQL APIs with schema design, DataLoader for N+1 prevention, Apollo Client caching, and federation patterns.

Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.

Extracts Amazon product details, buy box owner, other sellers' prices, and seller ratings for a given ASIN using the BrowserAct API.

Generate optimized SQL queries from natural language descriptions across multiple database platforms including BigQuery, PostgreSQL, MySQL, and Snowflake. Reads database schemas from uploaded files or diagrams to produce accurate, production-ready queries.

Spring Boot 3.x - Java framework for production-ready applications with dependency injection, REST APIs, data access, security, and actuator monitoring

Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Runs an Opportunity Solution Tree workflow with a lightweight graph database and CLI, routing through outcome, opportunity, solution, and assumption phases.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Expert product analytics advisor for Senior PMs. Use when defining success metrics for a PRD, designing an A/B experiment, setting up an analytics tracking plan, analyzing post-launch impact, or when data exists but there's no clarity on what to measure. Produces structured metrics frameworks that connect to product decisions, not dashboards.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Builds multi-tenant Laravel SaaS applications with stancl/tenancy v3, covering tenant identification, data isolation, bootstrappers, queues, and production patterns.

Manage and monitor Heap product analytics by querying the Heap API for account info, defined events, user properties, segments, event volume, active users, and data health.

AIDE file integrity monitoring reference. Database initialization, integrity checks, update workflow, aide.conf configuration, selection rules, report parsing, and production deployment with CIS benchmark compliance.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.

Use when the request is broad and spans idea evaluation, MVP planning, UX, frontend, backend, database, auth, integrations, and launch strategy. Trigger on asks like "build this app", "turn this idea into a product", "design the whole system", or "plan everything end to end".

Use this skill before merging, deploying, or shipping changes. It reviews code for bugs, security, auth, data loss, migrations, observability, performance, and rollback risks.