2,317 results in Skills, Sub-Agents, CLAUDE.md / Rules · page 51 of 97
Sub-AgentsPreview
mermaid-expert
Create Mermaid diagrams for flowcharts, sequences, ERDs, and architectures. Masters syntax for all diagram types and styling. Use PROACTIVELY for visual documentation, system diagrams, or process flows.
Evidence-first live messaging workflow for ECC. Use when the user wants to read texts or DMs, recover a recent one-time code, inspect a thread before replying, or prove which message source was actually checked.
Details workflow for REPL-driven development in Clojure/ClojureScript with emphasis on incremental development, testing, and step-by-step approach for feature implementation.
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
Obsidian metadata management specialist. Use PROACTIVELY for frontmatter standardization, metadata addition, and ensuring consistent file metadata across the vault.
This skill should be used when the user asks to "use Metasploit for penetration testing", "exploit vulnerabilities with msfconsole", "create payloads with msfvenom", "perform post-exploitation", "use auxiliary modules for scanning", or "develop custom exploits". It provides comprehensive guidance for leveraging the Metasploit Framework in security assessments.
Expert in launching small, focused SaaS products fast - the indie hacker approach to building profitable software. Covers idea validation, MVP development, pricing, launch strategies, and growing to sustainable revenue. Ship in weeks, not months. Use when: micro saas, indie hacker, small saas, side project, saas mvp.
Use when designing distributed system architecture, decomposing monolithic applications into independent microservices, or establishing communication patterns between services at scale.
Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.
Microsoft Learn Contributor chatmode for editing and writing Microsoft Learn documentation following Microsoft Writing Style Guide and authoring best practices.
Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.
Master Minecraft server plugin development with Bukkit, Spigot, and Paper APIs. Specializes in event-driven architecture, command systems, world manipulation, player management, and performance optimization. Use PROACTIVELY for plugin architecture, gameplay mechanics, server-side features, or cross-version compatibility.
Simple minimal status line showing only model name and current directory. Clean and distraction-free display perfect for focused development sessions where you want minimal visual clutter.
Expert en Machine Learning et Data Science avec Python. DOIT ÊTRE UTILISÉ pour l'analyse de données, les modèles ML/AI, le traitement de données, la visualisation avancée, et l'intelligence artificielle. Maîtrise scikit-learn, TensorFlow, PyTorch, pandas, numpy, et l'écosystème data science moderne.
Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
Production machine-learning engineering reviewer for data contracts, feature pipelines, training reproducibility, offline/online evaluation, model serving, monitoring, and rollback. Use when ML, MLOps, model training, inference, feature store, or evaluation code changes.
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform