Skill UI

Automated guidance for managing model checkpoints in ML training workflows, offering best practices, sample code, and validation to keep experiments stable and reproducible.

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

Automates guidance for model export helper tasks across ML deployment, delivering step-by-step best practices, production-ready code/configuration, and validation against common standards.

This comprehensive playbook provides effective patterns and structured templates for managing on-call shift transitions. It guides engineers on documenting active incidents, ongoing investigations, recent system changes, and upcoming maintenance events. The goal is to ensure seamless context transfer, maintaining continuity and reliable incident response across different shifts.

This skill guides developers on migrating their codebase from direct OpenAI API calls to using OpenRouter. By making minimal changes—primarily updating the base URL and API key—users gain access to over 400 models from multiple providers (including Anthropic and Google) through a unified SDK interface, ensuring high compatibility and flexible model switching.

This comprehensive guide provides developers with step-by-step instructions for migrating applications from direct calls to proprietary Large Language Model (LLM) APIs (such as OpenAI or Anthropic) to utilizing the unified OpenRouter gateway. It details essential code changes, including updating base URLs, managing API keys, adding custom headers, and correctly prefixing model identifiers. This ensures a seamless transition, enabling multi-model compatibility and simplified API management.

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

A comprehensive specialist that combines automated static analysis (SonarQube, CodeQL, Semgrep) with advanced AI capabilities. This tool performs multi-layered code reviews assessing security vulnerabilities, performance bottlenecks, architectural coherence, and maintainability across various programming languages, integrating seamlessly into CI/CD pipelines.

This guide details the process of migrating search and LLM functionalities from legacy APIs (Google CSE, Bing, SerpAPI) to Perplexity Sonar. It demonstrates how to utilize the Strangler Fig pattern to build a robust adapter layer. The primary benefit is consolidating a complex, multi-step pipeline (Search -> Raw Results -> LLM Summarization) into a single, efficient API call, ensuring automatic citation handling and streamlined development.

This guide details the recommended pattern for managing per-project plugin settings and state within a development environment. Plugins store their configuration in a dedicated `.claude/plugin-name.local.md` file. This file utilizes YAML frontmatter for structured settings (like enabling flags, retry counts, and parameters) and a Markdown body for additional context or instructions. It provides techniques for reading these settings using Bash hooks, commands, and agents.

This guide details implementing robust Role-Based Access Control (RBAC) within a PostHog Enterprise environment. It covers managing the three-level hierarchy (Organization > Project > Resource), assigning granular member roles (Owner, Admin, Member), configuring scoped API keys for secure integrations, and enforcing enterprise-grade authentication via SSO/SAML. Includes steps for setting up project permissions and auditing system changes.