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Found 802 Skills
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.
Static code analysis and complexity metrics
Use when writing SQL queries, building analytics dashboards, tracking metrics, designing data pipelines, or analyzing user behavior and product usage
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).
Overview The TikTok Agent allows users to extract data from TikTok, including video metrics, creator profiles, and hashtag velocity, to bypass the limitations of manual trend-spotting. With the TikTo
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.
Use when the user needs prompt design, optimization, few-shot examples, chain-of-thought patterns, structured output, evaluation metrics, or prompt versioning. Triggers: new prompt creation, prompt optimization, few-shot example design, structured output specification, A/B testing prompts, evaluation framework setup.
Transform vague product ideas into concrete, executable strategies with clear metrics, user impact, and technical feasibility.
Assess investment suitability obligations under FINRA Rules 2111 and 2090 across all three suitability prongs. Use when the user asks about reasonable-basis, customer-specific, or quantitative suitability, product-specific concerns for complex products, leveraged ETFs, variable annuities, or alternatives, household-level suitability, hold recommendations, or the institutional suitability exemption. Also trigger when users mention 'is this investment suitable', 'turnover ratio is too high', 'cost-to-equity ratio', 'churning metrics', 'suitability questionnaire design', 'complex product due diligence', 'customer refused to provide their risk tolerance', or ask whether a recommendation fits a customer's profile.
Retrieve search and usage analytics from Glean. Use when analyzing search patterns, popular queries, or platform adoption metrics.