Loading...
Loading...
Found 675 Skills
Comprehensive audio analysis with waveform visualization, spectrogram, BPM detection, key detection, frequency analysis, and loudness metrics.
Use this skill when the user discusses experiment design, ablations, training runs, evaluation, baselines, metrics, failures, or result interpretation that should be logged into Obsidian experiment and result notes.
Help users improve retention and engagement metrics. Use when someone is dealing with churn, optimizing activation flows, building habit-forming products, or trying to increase user engagement and lifetime value.
Use when building cloud-native apps. Keywords: kubernetes, k8s, docker, container, grpc, tonic, microservice, service mesh, observability, tracing, metrics, health check, cloud, deployment, 云原生, 微服务, 容器
Assess, quantify, and prioritize technical debt using code analysis, metrics, and impact analysis. Use when planning refactoring, evaluating codebases, or making architectural decisions.
Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.
Track user metrics and provide data-driven insights for product decisions. Use when measuring product health, analyzing user behavior, conducting cohort analysis, or optimizing key metrics. Covers acquisition, engagement, retention, revenue metrics, and data-driven decision making.
This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
Use ONLY when creating NEW registrable components in ML projects that require Factory/Registry patterns. ✅ USE when: - Creating a new Dataset class (needs @register_dataset) - Creating a new Model class (needs @register_model) - Creating a new module directory with __init__.py factory - Initializing a new ML project structure from scratch - Adding new component types (Augmentation, CollateFunction, Metrics) ❌ DO NOT USE when: - Modifying existing functions or methods - Fixing bugs in existing code - Adding helper functions or utilities - Refactoring without adding new registrable components - Simple code changes to a single file - Modifying configuration files - Reading or understanding existing code Key indicator: Does the task require @register_* decorator or Factory pattern? If no, skip this skill.
Choose the right metrics for a LaunchDarkly experiment, guarded rollout, or release policy. Use when the user wants to know which metrics to use, which is the primary metric for an experiment, what guardrails to add, or which events to monitor in a rollout. Surfaces what will auto-attach from existing release policies before making additional recommendations.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.