Total 50,673 skills, AI & Machine Learning has 8493 skills
Showing 12 of 8493 skills
Build AI agents with persistent threads, tool calling, and streaming on Convex. Use when implementing chat interfaces, AI assistants, multi-agent workflows, RAG systems, or any LLM-powered features with message history.
Use when user asks to 'lint agent configs', 'validate skills', 'check CLAUDE.md', 'validate hooks', 'lint MCP'. Validates agent configuration files against 155 rules across 10+ AI tools.
Workspace guide to introduce OpenWork and onboard new users.
Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper.
Convert an ML research paper into a complete, runnable code repository. 3-stage pipeline from Paper2Code — Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation). Use for reproducing paper methods.
Generate complete academic survey papers using multi-LLM parallel outline generation, RAG-based subsection writing, citation validation, and local coherence enhancement. Based on AutoSurvey pipeline. Use for writing comprehensive literature surveys.
Guide developers integrating EUrouter into their applications. EUrouter is an OpenAI-compatible AI gateway for EU/GDPR compliance. Use when integrating EUrouter, switching from OpenRouter or OpenAI, configuring EU data residency, routing AI requests to EU providers, managing API keys, or asking about EUrouter's API for chat completions, embeddings, streaming, tool calling, vision, model routing, or GDPR compliance features.
Audit, score, and improve any project's Claude Code configuration. Analyzes CLAUDE.md, skills, agents, hooks, MCP servers, and settings. Trigger: /refine, /refine audit, /refine quick
Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
Interact with Moltbook social network for AI agents. Post, reply, browse, and analyze engagement. Use when the user wants to engage with Moltbook, check their feed, reply to posts, or track their activity on the agent social network.
Generate ideas in one shot using creative sampling
Use when checking cross-file consistency: tools vs frontmatter, agent references, duplicate rules, contradictions.