用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/eirkkk/winland-Android --skill status命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
基于 SOC 职业分类
正在显示 SKILL.md
| name | status |
| description | Show experiment dashboard with results, active loops, and progress. |
| compatibility | opencode |
Show experiment results, active loops, and progress across all experiments.
/ar:status # Full dashboard
/ar:status engineering/api-speed # Single experiment detail
/ar:status --domain engineering # All experiments in a domain
/ar:status --format markdown # Export as markdown
/ar:status --format csv --output results.csv # Export as CSV
python {skill_path}/scripts/log_results.py --experiment {domain}/{name}
Also check for active loop:
cat .autoresearch/{domain}/{name}/loop.json 2>/dev/null
If loop.json exists, show:
Active loop: every {interval} (cron ID: {id}, started: {date})
python {skill_path}/scripts/log_results.py --domain {domain}
python {skill_path}/scripts/log_results.py --dashboard
For each experiment, also check for loop.json and show loop status.
# CSV
python {skill_path}/scripts/log_results.py --dashboard --format csv --output {file}
# Markdown
python {skill_path}/scripts/log_results.py --dashboard --format markdown --output {file}
DOMAIN EXPERIMENT RUNS KEPT BEST CHANGE STATUS LOOP
engineering api-speed 47 14 185ms -76.9% active every 1h
engineering bundle-size 23 8 412KB -58.3% paused —
marketing medium-ctr 31 11 8.4/10 +68.0% active daily
prompts support-tone 15 6 82/100 +46.4% done —