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Found 865 Skills
Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.
Build and run LLM-as-judge evaluation pipelines using Amazon Bedrock Evaluation Jobs with pre-computed inference datasets. Use when setting up automated model evaluation, designing test scenarios, collecting pre-computed responses, configuring custom metrics, creating AWS infrastructure, running evaluation jobs, parsing results, and iterating on findings.
Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.
Use this skill when the user needs to build a financial model, calculate unit economics, understand MRR/ARR/churn, or figure out their quit number. Covers SaaS metrics, CAC/LTV, burn rate, cash flow modeling, and making unit economics legible for non-finance founders.
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
Use the Statsig MCP to inspect and manage Statsig entities such as gates, experiments, dynamic configs, segments, metrics, audit logs, and results.
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.
Write and run AQL (Analytic Query Language) queries to answer data questions. Use this whenever the user asks for data, wants to query a dataset, needs to filter/aggregate/join data, or asks about metrics and dimensions in Holistics.
Traefik v3 cloud-native reverse proxy. Covers providers, entrypoints, routers, middlewares, services, Docker labels, TLS/ACME, dashboard, and metrics. USE WHEN: user mentions "traefik", "traefik v3", "traefik docker", "traefik labels", "traefik middleware", "traefik dashboard", "traefik tls", "traefik acme", "traefik router", "traefik entrypoint", "traefik reverse proxy", "traefik cloudflare", "traefik let's encrypt", "traefik rate limit" DO NOT USE FOR: Caddy-based setups - use `caddy` skill, Nginx load balancing - use `load-balancer` skill, Kubernetes ingress with nginx-ingress - use `kubernetes` skill, Application-level TLS inside app code
Guides rollout configuration for experiments: variant splits, overall rollout percentage, and the critical disambiguation when a user mentions a specific percentage. Covers both initial setup and mid-experiment changes. TRIGGER when: user mentions a rollout percentage, asks about variant splits, wants to change distribution on a running experiment, or asks 'who sees what variant?' DO NOT TRIGGER when: user is asking about metrics, analytics, or experiment results.
Convert an Omni Analytics topic into a Databricks Metric View definition in Unity Catalog. Use this skill whenever someone wants to export Omni metrics to Databricks, create a Metric View from an Omni topic, harden BI metrics into Unity Catalog, or bridge Omni's semantic layer with Databricks AI/BI dashboards and Genie spaces.
Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.