cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill accesslint-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/accesslint-auditContext preview
The summary Claude sees to decide when to auto-load this skill.
Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string
name: accesslint-audit description: "Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits." risk: safe source: "https://github.com/AccessLint/skills" date_added: "2026-06-02"
You audit accessibility and optionally fix what's broken.
If unsure, ask. Don't default-to-fix when the user only asked for an audit.
For very large sweeps where main-thread context cost matters, you can be invoked via `Task` (general-purpose agent) for context isolation. The recipe is the same either way.
Three flows, in order of preference.
1. **`audit_live`** — try first for any URL. Connects to a running Chrome debug session, or auto-launches Chrome minimized — no user setup needed. Single call; IIFE bytes don't enter your context. 2. **`audit-live-page` prompt** — use when the user needs their **existing browser session** audited (authenticated app, specific state) and a browser MCP (chrome-devtools-mcp, playwright-mcp, puppeteer-mcp) is connected. Invoke via `Skill` with `mode: "fix"` or `mode: "plan"`. 3. **`audit_html`** — for raw HTML strings, files (`Read` first, then `audit_html`), or JSX you've rendered to a string. Pair with `audit_diff({ html })` for fix-mode verification.
For non-URL targets, skip straight to flow 3. For URLs, try flow 1; on auto-launch failure, try flow 2 if a browser MCP is connected; otherwise fall back to flow 3 with a note that live-DOM coverage is limited.
State the scope explicitly at the start of your report.
1. **Map the surface.** Glob/Grep to enumerate components, templates, styles. Sample representative files; don't open everything blindly. 2. **Audit live where possible** — the rendered DOM catches issues source can't show. Use the flow picker above. 3. **Look for patterns.** If one component fails a rule, similar components likely do too. Group by rule ID and component family — don't list 30 instances of the same issue 30 times. 4. **Prioritize by user impact.** Critical/serious first. Many low-impact violations of one rule are often a single root-cause fix. 5. **Use `format: "compact"` for sweep-time calls.** Reserve verbose output for rules you'll expand in the report. 6. **Trust `Source:` lines.** Live-DOM audits against React dev builds attach `Source: <file>:<line> (Symbol)` per violation via DevTools fibers. Use it as the file pointer instead of grepping selectors. Fall back to stable hooks → visible text → tree position when absent. 7. **Stop and ask if a single audit returns more than ~50 violations** — a 200-violation report isn't actionable.
The engine catches what's mechanically detectable. Manual judgment is needed for content clarity, screen-reader announcement quality, keyboard flow coherence, and complex visual contrast — flag those for human review, don't guess.
# Accessibility audit — <scope> ## Summary - N critical, M serious, K moderate, J minor (after deduplication) - Most impactful patterns: <one-line each, max 3> ## Critical (blocks access) For each pattern: - **Pattern**: <one-line description> - **WCAG**: <ID> — <name> - **Affected files**: <file:line> (×N if repeated) - **Fix**: <directive from engine output, or specific code change> - **Why critical**: <user impact> ## Serious [same shape] ## Moderate / Minor [Bullet list, deduplicated by rule. Skip per-instance detail unless the fix differs.] ## Recommendations - Architectural / pattern-level changes that would prevent recurrence. - Tooling or component abstractions worth introducing. - What to verify manually (screen reader, keyboard, low-vision testing). ## Positive findings What the codebase does well — short, factual, reinforces practices to keep.
Include rule IDs in every entry. Quote the `Fix:` directive verbatim for `mechanical` rules. For `visual` / `contextual`, leave a `TODO` with the rule ID; don't invent content.
1. **Baseline.** Audit with `name: "before"` and `format: "compact"`. 2. **Plan + apply.** For each violation:
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Repo: LiHongwei-cn/lihongwei-cn
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…