weapp-tailwindcss-troubleshoot
诊断 weapp-tailwindcss 与 `weapp-tw` CLI 的样式未生成、JS/模板 class 未转译、classNameSet、@source/cssEntries、source candidates、rpx、伪类/custom variant、组件隔离、Rspack、压缩、watch/HMR、H5/Web/App/uni-app x 多端产物问题。Use for debugging, troubleshooting, CLI output failures, classes not working, broken hot reload, wrong output, or runtime compatibility;不用于新项目配置、自研 API 设计或 Lynx 专属接入。
Works with
--- name: weapp-tailwindcss-troubleshoot description: 诊断 weapp-tailwindcss 与 `weapp-tw` CLI 的样式未生成、JS/模板 class 未转译、classNameSet、@source/cssEntries、source candidates、rpx、伪类/custom variant、组件隔离、Rspack、压缩、watch/HMR、H5/Web/App/uni-app x 多端产物问题。Use for debugging, troubleshooting, CLI output failures, classes not working, broken hot reload, wrong output, or runtime compatibility;不用于新项目配置、自研 API 设计或 Lynx 专属接入。 license: MIT --- # weapp-tailwindcss troubleshoot 从生成、转译、产物到运行时逐层定位根因,避免用配置猜测掩盖症状。 ## 诊断顺序 1. 收集最小复现:框架、bundler、目标端、版本、命令、原始 class、期望与实际产物。 2. 读取 [references/symptom-matrix.md](references/symptom-matrix.md),先归类症状,再执行对应最短路径。 3. 检查 Tailwind 入口是否为纯 CSS、是否实际进入构建图、`cssEntries` 是否为绝对路径、`@source` 是否覆盖源码。 4. 检查是否存在第二个 Tailwind 生成器,或 H5/Web 被错误 `disabled`。 5. 分别验证 CSS 候选、模板/JS safe class、最终平台样式文件和真实页面;不要只看固定的 `app.wxss` 文件名。 6. watch/HMR 问题要在同一进程连续新增 class,区分首次构建成功与增量图失效。 7. CLI 问题先确认 `web`/`weapp` target、stdin/stdout、source map 与 watch 模式,不期待它改写模板或脚本。 8. 只有根因确认后才修改配置,并补一条可重复的回归验证。 ## 安全边界 - JS 转译只接受 Tailwind 验证过的 `classNameSet`;未命中时修复扫描或刷新时序,不启用启发式全字符串转译。 - `space-*` 先处理结构和组件虚拟节点,再最小扩展 `cssOptions.cssChildCombinatorReplaceValue`。 - App WebView 兼容必须记录 Android Chromium、iOS WebKit 或 Harmony/HBuilderX 环境,并以真实运行截图或日志为证据。 - 不为排障临时加入 `@tailwindcss/vite` 或 `@tailwindcss/postcss`。 - ReactLynx/Rspeedy 构建与 CSS encoder 问题转到 `$weapp-tailwindcss-lynx`。 ## 输出要求 先给根因层级和证据,再给最小修复、验证命令、预期结果和未解决时的下一条观测点。
More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.

