Total 50,673 skills, AI & Machine Learning has 8493 skills
Showing 12 of 8493 skills
Full RPI lifecycle orchestrator. Research → Plan → Pre-mortem → Crank → Vibe → Post-mortem. One command, sequential skill invocations with human gates and hands-free validation. Triggers: "rpi", "full lifecycle", "end to end", "research to production".
Search and analyze your own session logs (older/parent conversations) using jq.
Create custom slash commands for Claude Code including syntax, arguments, bash execution, file references, and frontmatter configuration. Use when creating slash commands, custom commands, .md command files, or when asked about command creation, /command syntax, or command best practices.
Vertex Ai Pipeline Creator - Auto-activating skill for GCP Skills. Triggers on: vertex ai pipeline creator, vertex ai pipeline creator Part of the GCP Skills skill category.
Smart LLM router — save 78% on inference costs. Routes every request to the cheapest capable model across 30+ models from OpenAI, Anthropic, Google, DeepSeek, and xAI.
AI agent operational rules including token discipline, navigation-first approach, and output contracts. Use when you need efficient and predictable agent behavior during development tasks.
Apply compaction, masking, and caching strategies
Enter the Gigaverse as an AI agent. Create a wallet, quest through dungeons, battle echoes, and earn rewards. The dungeon awaits.
AI-Driven Specification-Driven Development (SDD) workflow orchestrator - guides skill selection and general SDD methodology
Guidance for recovering PyTorch model architectures from state dictionaries, retraining specific layers, and saving models in TorchScript format. This skill should be used when tasks involve reconstructing model architectures from saved weights, fine-tuning specific layers while freezing others, or converting models to TorchScript format.
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.