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Found 876 Skills
Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-technical founders.
A fast, extensible progress bar for Python and CLI. Instantly makes your loops show a smart progress meter with ETA, iterations per second, and customizable statistics. Minimal overhead. Use for monitoring long-running loops, simulations, data processing, ML training, file downloads, I/O operations, command-line tools, pandas operations, parallel tasks, and nested progress bars.
This skill should be used when the user requests to add a new third-party API service to the AWS billing/quota monitoring system. It handles the complete onboarding process including adapter creation, Lambda deployment, CloudWatch alarms, Dashboard updates, and verification. Triggers on requests mentioning "add service monitoring", "monitor API balance", "setup quota alerts", "add to billing dashboard", or similar service integration requests.
List Langfuse traces with filtering options. Use when checking recent LLM calls, debugging issues, or monitoring costs.
List Langfuse sessions. Use when checking user sessions, analyzing conversation flows, or monitoring session activity.
CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD, GitOps, monitoring.
Real-time serial log monitoring for ESP32 and microcontrollers. Capture device output to a file and monitor logs in real-time. Use when debugging embedded devices, investigating crashes, or monitoring device behavior.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Automatically sync Agents.md, claude.md and gemini.md files in the project to maintain content consistency. Supports automatic monitoring and manual triggering.
Reporting framework for monitoring trust, sentiment, and regulator-facing KPIs.
Track Clawdbot AI model usage and estimate costs. Use when reporting daily/weekly costs, analyzing token usage across sessions, or monitoring AI spending. Supports Claude (opus/sonnet), GPT, and Codex models.
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.