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Analyze logs for performance insights and issues

A/B test design — produce an experiment spec with hypothesis, primary metric, MDE, sample size, run time, and decision rule. Also determines when NOT to A/B test and what to do instead. Use when asked to "design an A/B test", "should we test this", "experiment design", "how do we know if this works", "what's the sample size", or "set up an experiment".

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Instrumentation plan — design event taxonomy, property schema, and tracking plan for analytics tools. Use when asked to "what should we track", "instrumentation plan", "set up analytics events", "analytics event schema", "tracking plan", or "instrument this feature".

Metrics architecture — produce a complete metrics plan given a product description. North Star, input metrics tree, instrumentation spec, action triggers, and counter-metrics. Use when asked to "design a metrics framework", "what should we measure", "build a metrics system", "define our KPIs", "what are our success metrics", "metrics strategy", or "what do we track".

Analytics reconnaissance — scan existing event tracking, metric definitions, dashboards, and analytics configuration to understand what is currently being measured. Use when asked to "what are we tracking", "audit our analytics", "what metrics exist", "analytics inventory", or before designing new metrics or instrumentation.

Durable agent memory with Cascade object storage, client-side encryption, and local FTS index

Create Make.com (Integromat) scenarios with AI assistance - visual automation design

Scan for top market movers - gainers, losers, volume spikes, and unusual activity

Real-time market price tracking with multi-exchange feeds and advanced alerts

Analyze market sentiment from social media, news, and on-chain data

Marketing team — 3 agents: Ink, Buzz, Pitch

Automate changelog generation: fetch recent changes, synthesize release notes, run quality gates, and prepare a PR.

Detect memory leaks and analyze memory usage patterns

Advanced mempool analysis for MEV opportunities, pending transaction monitoring, and gas price optimization

Aggregate and centralize performance metrics

Train and optimize machine learning models with automated workflows

Persistent memory and learning-loop skills for AI coding agents. MCP-native with 20 tools, single Go binary, ships with measured efficacy harness.

Deploy ML models to production

Comprehensive model evaluation with multiple metrics

Model interpretability and explainability

Track and manage model versions

Deploy monitoring stacks (Prometheus, Grafana, Datadog)

Design complex n8n workflows with AI assistance - loops, branching, error handling