Skill UI

Provides structured Customer.io advanced debugging steps—from API calls and profile checks to campaigns, webhooks, and network diagnostics—so teams can diagnose delivery issues or integration incidents with clear runbooks and command references.

Guides support staff through collecting Customer.io debug evidence—user data, logs, diagnostics scripts, and reporting templates—so tickets contain reproducible information for faster troubleshooting.

Automates deploying Customer.io integrations to production cloud platforms (Cloud Run, Vercel, Lambda, Kubernetes) with health checks and blue-green rollout control for reliable releases.

Identifies Customer.io anti-patterns across authentication, tracking, data quality, delivery, and performance so you can audit integrations, fix common mistakes, and optimize implementations with actionable checks.

Defines a multi-environment Customer.io setup that isolates dev, staging, and prod workspaces while wiring up typed configs, env-aware clients, Kubernetes overlays, CI/CD promotions, and secrets-based credential loading for secure, repeatable deployments.

Enterprise-grade Customer.io reference architecture outlining event-driven services, repositories, webhook handling, and Terraform-defined infrastructure for designing integrations and implementing reliable messaging patterns.

Implement full Customer.io webhook handling covering email, push, SMS, and in-app events, with signature verification, reliable queueing, reporting API calls, and optional BigQuery streaming for analytics.

Defines how to automate Dadata Ru operations through Composio’s Rube MCP, covering prerequisite connection checks, schema-aware tool discovery, execution patterns, and common pitfalls so agents can run reliable workflows with updated tool interfaces.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

Automates generating Markdown data dictionaries and ER summaries by introspecting PostgreSQL/MySQL schemas, capturing tables, columns, constraints, indexes, views, stats, and stored procedures for up-to-date documentation.

Actionable guidance for analyzing plans, indexing, connection pooling, partitioning, and detecting N+1 queries to keep PostgreSQL and MySQL workloads fast and reliable.

Automates Databricks bundle validation, notebook unit tests, staging deployments, and production rollouts via GitHub Actions; ideal when you want repeatable CI/CD coverage for Databricks workspaces.