zendev-lab

Use when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.

中文简介正在准备中。当前显示原文说明。

Use when Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.

中文简介正在准备中。当前显示原文说明。

Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.

中文简介正在准备中。当前显示原文说明。

Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.

中文简介正在准备中。当前显示原文说明。

Use when designing a Spark feature or writing or materially revising a design proposal that needs repository-first research, user input on unresolved key choices, and an owner-aligned implementation plan before code changes.

中文简介正在准备中。当前显示原文说明。

Use when a working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.

中文简介正在准备中。当前显示原文说明。

Use when a running Spark or DSH web service needs real-browser verification — page load, client-plugin boot errors, console errors, failed requests, or onboarding/dialog rendering — with the repository's own Playwright chromium.

中文简介正在准备中。当前显示原文说明。

Use when adding or repairing Spark regression tests for asynchronous ownership, cancellation, persistence, restart recovery, or cross-surface projections. Not a coverage campaign, performance benchmark, source-text policy check, or full delivery review.

中文简介正在准备中。当前显示原文说明。

Use when a Spark change needs its authoritative owner, affected boundaries, risks, acceptance criteria, and smallest verifiable slice established before implementation or review.

中文简介正在准备中。当前显示原文说明。

Use when adding or repairing Spark regression tests for asynchronous ownership, cancellation, persistence, restart recovery, or cross-surface projections. Not a coverage campaign, performance benchmark, source-text policy check, or full delivery review.

中文简介正在准备中。当前显示原文说明。

Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.

中文简介正在准备中。当前显示原文说明。

Use when designing a Spark feature or writing or materially revising a design proposal that needs repository-first research, user input on unresolved key choices, and an owner-aligned implementation plan before code changes.

中文简介正在准备中。当前显示原文说明。

Use when a working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.

中文简介正在准备中。当前显示原文说明。

Use when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.

中文简介正在准备中。当前显示原文说明。

Use when a Spark change needs its authoritative owner, affected boundaries, risks, acceptance criteria, and smallest verifiable slice established before implementation or review.

中文简介正在准备中。当前显示原文说明。

Use when Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.

中文简介正在准备中。当前显示原文说明。

Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.

中文简介正在准备中。当前显示原文说明。

Use when a running Spark or DSH web service needs real-browser verification — page load, client-plugin boot errors, console errors, failed requests, or onboarding/dialog rendering — with the repository's own Playwright chromium.

中文简介正在准备中。当前显示原文说明。