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/mturac/everything-openai-codex --skill skill-comply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。
Use this skill to monitor and verify a deployed URL or public OSS launch surface after releases — checks HTTP endpoints, SSE streams, static assets, console errors, performance regressions, PR queue health, maintainer feedback, and listing-review blockers after deploys, merges, submissions, or dependency upgrades. Smoke / canary / post-deploy / PR-watch verification.
Build reputation-safe open-source marketing from verifiable project evidence, not hype, spam, or repeated public pings. Use for launch copy, directory targeting, community posts, proof packets, and maintainer-facing positioning.
Turn public launch, directory, community, or list rejections into repo fixes and better proof without arguing, spamming, or resubmitting blindly. Use after Hacker News, Product Hunt, GitHub list PR, marketplace, or community rejection.
基于 SOC 职业分类
正在显示 SKILL.md
| name | skill-comply |
| description | 可视化技能、规则和代理定义是否被实际遵循——自动生成3种提示严格级别的场景，运行代理，分类行为序列，并报告完整工具调用时间线的合规率 |
| origin | ecc |
| tools | Read, Bash |
通过以下方式测量编码代理是否实际遵循技能、规则或代理定义：
codex -p 并通过 stream-json 捕获工具调用轨迹skills/*/SKILL.md）：工作流技能，如搜索优先、TDD 指南rules/common/*.md）：强制性规则，如 testing.md、security.md、git-workflow.mdagents/*.md）：代理是否在预期时被调用（内部工作流验证尚不支持）/skill-comply <path># Full run
uv run python -m scripts.run ~/.codex/rules/common/testing.md
# Dry run (no cost, spec + scenarios only)
uv run python -m scripts.run --dry-run ~/.codex/skills/search-first/SKILL.md
# Custom models
uv run python -m scripts.run --gen-model fast --model standard <path>
测量技能/规则是否在提示未明确支持时仍被遵循。
报告是自包含的，包括：
对于熟悉钩子的用户，报告还包含针对合规性较低的步骤的钩子提升建议。此为参考信息——主要价值在于合规性本身的可见性。