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Found 1,605 Skills
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Analyze stocks and cryptocurrencies using Yahoo Finance data. Supports portfolio management, watchlists with alerts, dividend analysis, 8-dimension stock scoring, viral trend detection (Hot Scanner), and rumor/early signal detection. Use for stock analysis, portfolio tracking, earnings reactions, crypto monitoring, trending stocks, or finding rumors before they hit mainstream.
Set up a newsjack monitoring profile for a company so newsjack-detector can run on a schedule. Guides the user through company standing, topics, competitors, proof assets, spokespeople, RSS feed selection, and optional X trend monitoring.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment.
Telegram bot management and monitoring. TRIGGERS - telegram bot, claude-orchestrator, bot status, bot restart.
Expert-level Prometheus monitoring, metrics collection, PromQL queries, alerting, and production operations
DigitalOcean management services for monitoring, uptime checks, and resource organization with Projects. Use when setting up observability, alerts, and operational visibility on DigitalOcean.
Git-centric implementation workflow. Enforces clean checkout, creates a properly named branch, tracks progress in a WIP markdown file, and commits/pushes continuously so remote git logs serve as the primary monitoring channel. Use when starting any plan-based implementation task.
Comprehensive MLOps workflows for the complete ML lifecycle - experiment tracking, model registry, deployment patterns, monitoring, A/B testing, and production best practices with MLflow
Autonomous multi-agent task orchestration with dependency analysis, parallel tmux/Codex execution, and self-healing heartbeat monitoring. Use for large projects with multiple issues/tasks that need coordinated parallel execution.
Framework for defining, monitoring, and enforcing guardrail metrics across experiments.