Skill UI

Automate Amplitude product analytics by orchestrating Composio's Rube MCP toolkit to send events, resolve user activity, manage cohorts, and sync identities while always querying RUBE_SEARCH_TOOLS for current schemas.

Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts using aggregate or synthetic data. Not for patient care or live clinical use.

Implements a robust, multi-tenant authorization layer for Cohere API v2. This solution enforces Role-Based Access Control (RBAC), manages individual API keys per team, and implements per-team usage limits and daily budget tracking. It is essential for large-scale applications requiring strict resource isolation and cost governance across multiple internal services.

This expert acts as a dedicated data analysis partner for Product Managers. It helps users explore data, write complex queries (e.g., SQL, BigQuery), analyze key metrics, and create structured dashboards. Use this tool when you need to derive actionable insights from complex datasets, compare user cohorts, or understand product performance trends to inform critical business decisions.

Query and download public cancer imaging data (CT, MR, PET, pathology) from the NCI Imaging Data Commons. Supports metadata SQL lookups, DICOM access via idc-index, REST or MCP, cohort selection, browser visualization and license checks without authentication.

A sophisticated tool for commercial leaders preparing board-ready forecasts. It goes beyond single-number projections by decomposing pipeline into commit, best-case, and pipe-only tiers. It also projects cohort-level Net/Gross Revenue Retention (NRR/GRR) to detect leaky cohorts and scores per-stage funnel confidence, ensuring all assumptions are explicit for executive review.

This skill generates a comprehensive, structured churn analysis, moving far beyond simple headline rates. It meticulously separates avoidable vs. unavoidable customer losses, identifies high-risk segments (by tier, cohort, etc.), calculates critical metrics like Net Revenue Retention (NRR), and pinpoints actionable early warning signals. The output culminates in prioritized, data-driven intervention recommendations to improve customer retention.

A structured framework for performing in-depth cohort analysis. It helps measure user retention rates, estimate Lifetime Value (LTV), and segment users based on behavior over time. Use this when analyzing growth metrics, identifying churn risks, or presenting product health to leadership.

A comprehensive framework for structuring deep-dive product data analysis, including funnel analysis, metric triage, and cohort studies. This guide forces the analyst to answer four critical questions: What changed, Why did it change, What is the impact, and What should we do next, ensuring insights lead directly to actionable business recommendations.

This skill provides querying capabilities for the 1000 Genomes Project dataset, covering 3,202 whole-genome-sequenced individuals on the GRCh38 assembly. It allows users to identify variants within a region, find specific individuals who carry defined genetic variants, or determine the relatedness between two named individuals. Results include allele frequencies (AF), AlphaMissense scores, and various clinical annotations. Use this tool for advanced genomic cohort analysis.

This tool performs deep customer cohort analysis to understand retention patterns, lifetime value (LTV), and the long-term impact of various acquisition channels and initial behaviors. Users can segment customers by time, source, or revenue tier to identify high-value groups, optimize onboarding, and improve overall customer retention strategies. Ideal for SaaS, eCommerce, and B2B businesses tracking customer lifecycle.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.