Machine learning

Call POST /embeddings on Venice.

Discover Venice models, their capabilities, constraints, and pricing.

Use when code needs a judgment about natural language that rules or regex cannot make (classify, route, triage, moderate, score, rank, match, dedupe, filter…

Lint AI agent instruction files (SKILL.md, CLAUDE.md, AGENTS.md, GEMINI.md), tool definitions, system prompts, and agent configs with the deterministic…

Use when writing or reviewing AI agent configs, system prompts, or tool definitions (JSON/YAML/Python) and you need to catch ambiguous tool descriptions…

Knowledge management, document synthesis, and concept graph maintenance following Andrej Karpathy's LLM-Wiki pattern.

Unified Kaggle skill.

Provides access to the user's personal wiki, including notes, research, project documentation, decisions, and archived knowledge.

Overview of Neon, a complete set of cloud backend primitives around Lakebase Postgres: Auth, Object Storage, Functions, the AI Gateway, and Realtime.

Guides and best practices for working with Lakebase Postgres on Neon: connections, pooled vs direct, schema migrations, branching, autoscaling, scale-to-zero…

Neon Realtime makes any SQL query live.

Audit a website with the squirrelscan CLI and fix the findings in code.

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by…

Use when a medical AI paper should be found and cited by AI search engines and RAG tools.

Use when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis.

Use when designing a study that benchmarks AI systems against a human-expert panel, before data collection.

Use when designing or auditing how an LLM or multimodal LLM is evaluated on a clinical task (report generation, VQA, text extraction).

Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or…

Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar).

Anti-slop principles for B2C social posts and outreach.

Marketing-strategy foundations distilled from the Phase 11 strategy generator.

Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic…

Structured idea generation + multi-LLM debate for the product-owner stage.

One API and one credential for frontier and open-source LLMs and embedding models, built into your Neon branch and powered by Databricks.