oncokb-datastar-review

Reviews Datastar code changes for Tao-aligned architecture and correct attribute usage.

Updated Aug 31, 2026

Search Tags:#code review #frontend architecture #signals #hypermedia #sse #datastar #server-driven ui

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npx skills add https://github.com/oncokb/oncokb-skills --skill oncokb-datastar-review-oncokb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: oncokb-datastar-review
Source: https://github.com/oncokb/oncokb-skills/tree/main/skills/oncokb-datastar-review
Command: npx skills add https://github.com/oncokb/oncokb-skills --skill oncokb-datastar-review-oncokb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Reviewing Datastar-based UI changes requires checking architecture principles, attribute semantics, and interaction reliability, which is easy to get wrong without a structured checklist. ## Core Features & Use Cases - Tao Principle Verification: Checks backend-as-source-of-truth, server-driven patching, and minimal frontend state against the Tao of Datastar reference. - Attribute and Action Correctness: Validates `data-` attributes, `@action` expressions, modifiers, and SSE patch event usage. - Reliability Review: Flags optimistic success updates, missing loading indicators, and morph/patch state preservation issues. - Use Case: When a pull request adds Datastar-driven SSE flows or new `data-` attributes, run this review to get blockers and non-blocking findings with concrete fix directions. ## Quick Start Review my Datastar changes in this pull request for Tao alignment, attribute correctness, and reliability issues.

Frequently Asked Questions about oncokb-datastar-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I review Datastar code for architecture issues?▼

Apply the Tao of Datastar principles as review guardrails: verify backend-as-source-of-truth, server-driven patching, and minimal frontend state. Check each changed file using data-* attributes or @action expressions against these principles and report pass/fail with evidence.

What should I check in a Datastar pull request review?▼

Check attribute semantics and modifiers, backend action usage like @get and @post, loading indicators for long operations, and morph behavior with data-ignore-morph or data-preserve-attr. Separate blockers from non-blocking maintainability improvements.

Does Datastar prefer SSE over polling for updates?▼

Yes, Datastar's Tao recommends text/event-stream responses since SSE supports 0..n events and aligns with the Datastar update model. For streaming or multi-update flows, prefer SSE over ad-hoc polling patterns.

Why are optimistic UI updates flagged in Datastar reviews?▼

Optimistic updates claim success before backend confirmation, violating the backend-as-source-of-truth principle. Reviews flag these unless explicitly agreed upon, preferring pending states followed by confirmed backend results.

When should I use data-ignore-morph in Datastar?▼

Use data-ignore-morph only when morphing would destroy state that must be preserved, such as third-party widget DOM. The Tao advises trusting morphing by default and avoiding premature micro-patching complexity.