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Found 859 Skills
Use this skill when calculating, analyzing, or reporting SaaS business metrics. Triggers on MRR, ARR, churn rate, LTV, CAC, LTV:CAC ratio, cohort analysis, net revenue retention, expansion revenue, board deck metrics, investor reporting, unit economics, payback period, or SaaS financial modeling. Covers metric definitions, formulas, spreadsheet implementation, cohort tables, and board-ready reporting for founders, finance teams, and growth operators.
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.
Access research-grade sensor data using SensorKit. Use when reading ambient light levels, accelerometer data, rotation rates, device usage patterns, keyboard metrics, or media events for approved research studies. Requires SensorKit entitlement and research study authorization.
Fetch and analyze OpenRank and other statistical metrics for an open source repository or developer using OpenDigger data. Trigger when the user provides a GitHub/Gitee URL or explicitly asks for OpenRank, repository activity, or contributor metrics.
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.
Service metrics, RED metrics (Rate, Errors, Duration), and runtime-specific telemetry for .NET, Java, Node.js, Python, PHP, and Go applications.
Datadog integration. Manage Monitors, Dashboards, Incidents, Notebooks, Logs, Metrics and more. Use when the user wants to interact with Datadog data.
Designs structured benchmarks for comparing algorithms, models, or implementations. Selects appropriate metrics (latency, throughput, memory, accuracy), designs representative test cases, captures hardware/software context, produces comparison tables with tradeoff analysis, and includes reproduction instructions. Triggers on: "benchmark", "compare performance", "which is faster", "latency comparison", "memory comparison", "run benchmark", "design benchmark", "compare implementations", "evaluate algorithms", "performance comparison", "throughput test", "speed test". Use this skill when comparing two or more implementations, algorithms, or models.
Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.
Build a production-ready multilabel classifier on tabular data using XGBoost wrapped in MultiOutputClassifier. Use when each row can have multiple labels simultaneously (tags, attributes, gene functions, content moderation categories, multi-disease detection). Covers hamming loss, per-label metrics, label co-occurrence, MultiOutputClassifier vs ClassifierChain, and per-label SHAP. Default to this for any tabular multilabel problem.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.