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Found 675 Skills
Use when designing viral loops, referral systems, growth mechanics, launch playbooks, or analyzing growth metrics for product-led growth
Use the `datadog` CLI to manage Datadog resources — monitors, metrics, events, logs, services, errors, and pipelines. Invoke this skill whenever the user asks to query, create, update, or delete Datadog monitors, search logs or errors, check metric values, list APM services, or manage log pipelines. Also trigger when the user mentions Datadog observability tasks like "check the error rate", "look at monitors", "search logs for errors", "list services", or "set up a log pipeline".
Retrieve search and usage analytics from Glean. Use when analyzing search patterns, popular queries, or platform adoption metrics.
Implement Syncfusion WPF Bullet Graph (SfBulletGraph) components for performance indicators and KPI visualization. Use this when displaying metrics against targets, creating dashboard gauges, or visualizing performance in qualitative ranges. This skill covers featured measures, comparative measures, qualitative ranges, goal tracking, and compact data visualization for dashboards.
Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, architecture & design patterns, limits & quotas, configuration, and deployment. Use when using univariate/multivariate APIs, Docker/IoT Edge containers, predictive maintenance flows, or regional limits, and other Azure AI Anomaly Detector related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).
Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. Part of the skills-for-java project
Build VoIP calling apps on Android using Telnyx WebRTC SDK. Covers authentication, making/receiving calls, push notifications (FCM), call quality metrics, and AI Agent integration. Use when implementing real-time voice communication on Android.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Produce a one-page product intent specification with problem statement, users, metrics, risks, and acceptance criteria in Given/When/Then format. Use before any technical design or implementation work begins.
Transform vague product ideas into concrete, executable strategies with clear metrics, user impact, and technical feasibility.