Data & Analytics

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".

Design an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".

Audit existing fine-tuning or prompt engineering work — find quality gaps and optimization opportunities. Use when asked to "audit our fine-tuning work", "find prompt engineering gaps", or "look for optimization opportunities".

Systematically optimize prompts for a task — few-shot, chain-of-thought, structured output. Use when asked to "optimize this prompt", "add chain-of-thought", or "improve structured output".

Design a fine-tuning pipeline — PEFT config, dataset format, training loop, and evaluation. Use when asked to "fine-tune a model", "set up a LoRA config", or "should we fine-tune or prompt".

Audit existing model evaluation code — find metric misuse, missing CIs, and evaluation leakage. Use when asked to "audit our model evaluation", "find metric misuse", or "check for evaluation leakage".

Design an evaluation framework for a ML model — metrics, splits, and reporting. Use when asked "how should we evaluate this model", "design evaluation metrics", or "plan our train test split".

Compare two or more models statistically — significance testing and error analysis. Use when asked "which model is better", "is this improvement significant", or "compare model performance".

Frontend engineer — UI components, dashboards, design system implementation, and frontend audits. Use when asked to "build this UI component", "audit the frontend", "implement the design system", or "build a dashboard".

Use when asked to implement a chart, select a visualization type, or build a data display component. Examples: "implement chart for time series", "best visualization for comparison data", "chart component for analytics"

Audit existing visualizations in a codebase or notebook — find misleading charts and quality issues. Use when asked to "audit our charts", "find misleading visualizations", or "review chart quality".

Design an exploratory data analysis workflow for a dataset. Use when asked to "explore this dataset", "run an EDA", or "what is in this data".

Design or critique a data visualization — chart type selection, encoding, and clarity. Use when asked "what chart should I use", "critique this visualization", or "design this chart".

Product analyst — metrics architecture, funnel analysis, A/B test design, retention, and growth measurement. Use when asked to "design an A/B test", "analyze the funnel", "define a north star metric", or "measure retention".

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.

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".

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".

Analytics and BI engineer — dashboards, metrics design, reporting pipelines, and data storytelling. Use when asked to "build a dashboard", "design a metric", "write a report", or "audit our analytics".

Design and spec an analytical dashboard — define the question each chart answers, write the SQL queries, spec the layout and refresh cadence. Produces a complete dashboard spec ready to implement. Use when asked to "build a dashboard", "analytics dashboard", "BI dashboard", "weekly product health", or "visualize this data".

Use when asked to select chart types for analytics dashboards, choose BI visualizations, or design data displays. Examples: "best chart for sales data", "dashboard visualization for metrics", "analytics chart selection"

Review existing analytics — find all dashboards and reports, check who uses them, whether metrics are defined, and whether they drive decisions. Recommend what to keep, kill, or add. Use when asked "are our dashboards useful", "analytics review", or "metrics audit".

Design a type scale and hierarchy — sizes, weights, line-heights, and named tokens. Use when asked to "design a type scale", "set up type hierarchy", or "define typography tokens".