SeaTicket vs Taste Lab: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of SeaTicket and Taste Lab — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
SeaTicket
SeaTicket (SeaCloud Labs)
AI-powered unified issue management that automates handling of GitHub issues, Discourse topics and support requests from other channels.
Key features
- Unified Issue Inbox: Aggregates issues and support requests from GitHub repositories, Discourse topics and email/other sources into a single management interface so teams can view and act on all incoming items from one place.
- Automated Triage & Classification: Uses AI to analyze incoming content, categorize issues, assign severity/labels and route them to the appropriate team or workspace, reducing manual sorting work.
- Auto-Response & Suggested Replies: Generates suggested responses or drafts for common support questions and issue templates to accelerate replies and maintain consistency across channels.
- Issue Lifecycle Automation: Automates routine actions such as labeling, assignee assignment, status updates and issue linking based on configurable rules and AI classifications to speed resolution.
- OpenAPI & Integration Support: Provides an OpenAPI specification and integration endpoints (public repositories indicate an open-api repo) to connect SeaTicket with custom tooling, webhooks and third-party workflows.
- Admin Documentation & Customization: Includes admin documentation and configuration options (admin docs repo available) for customizing automation rules, mapping sources to projects, and managing permissions.
- Automated handling of GitHub issues
- Automated handling of Discourse topics
- Automated processing of email-based support requests
- Unified issue management across multiple sources
- OpenAPI specification published (seaticket-open-api GitHub repo) for API integrations
- Admin documentation available (seaticket-admin-docs repository)
- Streamlines workflows and boosts support productivity via AI-driven automation
Best for
- Triage GitHub Issues: Automatically classify, label and assign incoming GitHub issues to the right team or milestone, reducing time-to-response for maintainers of open-source or product repos.
- Convert Forum Topics to Trackable Issues: Turn Discourse topics or community posts into tracked issues with contextual metadata and automated routing so community reports are surfaced to engineering workflows.
- Centralize Multi-Channel Support: Aggregate email, forum and repository requests into a single queue to give support teams and maintainers a unified view and consistent handling process.
- Automate Repetitive Support Replies: Use AI-suggested replies and templates to answer frequent support questions rapidly while allowing human review for edge cases.
- Maintainers' Workflow Automation: Automatically apply labels, close duplicates, link related items and update statuses based on configurable rules to reduce manual maintenance tasks.
- Integrate with Existing Tooling: Use the provided OpenAPI endpoints and webhooks to integrate SeaTicket into CI/CD pipelines, helpdesk systems or internal dashboards for end-to-end automation.
- Automatic triage, labeling and routing of incoming GitHub issues
- Managing and responding to community support topics on Discourse
- Consolidating and automating email-based support requests into a unified issue workflow
- Integrating SeaTicket into existing toolchains using the provided OpenAPI for custom automations and webhooks
Taste Lab
Sen Lin
Taste Lab is a Claude Code skill that turns any URL into a complete design context: design tokens plus the reasoning and trade-offs behind every choice.
Key features
- Design Map Extraction: Captures every color, font weight, spacing value, radius, and shadow with exact px/hex/ratio citations across 20 measurement categories.
- Taste DNA Inference: Derives four design principles, each with a Trigger, Decision, Reason, Evidence, and Trade-off explaining why each choice was made.
- Four-Agent Pipeline: Runs Extract, Detect Patterns, Infer Taste, and Observer stages, each reading the page through a sharper lens.
- Anti-Slop Quality Gate: A final critic stage runs anti-slop checks and validates JSON before writing output.
- Dual File Output: Writes a {domain}.md and {domain}.json that any AI agent can build from.
Best for
- Cloning Design Systems: Give an AI agent a complete, reasoned design context to rebuild a site's look and feel.
- Design Reviews: Understand the deliberate trade-offs behind a website's visual decisions.
- Agent-Assisted Frontend Work: Feed structured taste files into coding agents so they make the right call on unseen pages.