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codex-readiness-integration-testCodex readiness 集成测试

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

1,717

周安装

73

GitHub Stars

17,927

下载量

602
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:codex-readiness-integration-test(Codex readiness 集成测试)
来源仓库:https://github.com/openai/skills
仓库路径:skills/codex-readiness-integration-test
安装命令:
npx skills add https://github.com/openai/skills --skill codex-readiness-integration-test
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/openai/skills --skill codex-readiness-integration-test

简介

codex-readiness-integration-test 执行多阶段集成测试,验证代理执行质量。

  • 始终在 execute 模式下运行,输出包含日志与评估结果。
  • 需先通过 repo-local HOME 完成 Codex CLI 认证。
  • 每次运行生成独立时间戳目录,避免结果覆盖。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

LLM Codex Readiness Integration Test

This skill runs a multi-stage integration test to validate agentic execution quality. It always runs in execute mode (no read-only mode).

Outputs

Each run writes to .codex-readiness-integration-test/<timestamp>/ and updates .codex-readiness-integration-test/latest.json.

New outputs per run:

  • agentic_summary.json and logs/agentic.log (agentic loop execution)
  • llm_results.json (automatic LLM evaluation)
  • summary.txt (human-readable summary)

Pre-conditions (Required)

  • Authenticate with the Codex CLI using the repo-local HOME before running the test. Run these in your own terminal (not via the integration test): HOME=$PWD/.codex-home XDG_CACHE_HOME=$PWD/.codex-home/.cache codex login HOME=$PWD/.codex-home XDG_CACHE_HOME=$PWD/.codex-home/.cache codex login status
  • The integration test creates {repo_root}/.codex-home and {repo_root}/.codex-home/.cache/codex as its first step.

Workflow

  1. Ask the user how to source the task.

- Offer two explicit options: (a) user provides a custom task/prompt, or (b) auto-generate a task. - Do not run the entry point until the user chooses one option.

  1. Generate or load {out_dir}/prompt.pending.json.

- Use the integration test's expected prompt path, not prompt.json at the repo root. - With the default out dir, this path is .codex-readiness-integration-test/prompt.pending.json. - If --seed-task is provided, it is used as the starting task. - If not provided, generate a task with skills/codex-readiness-integration-test/references/generate_prompt.md and save the JSON to {out_dir}/prompt.pending.json. - The user must approve the prompt before execution (no auto-approve mode). Make sure to output a summary of the prompt when asking the user to approve.

  1. Execute the agentic loop via Codex CLI (uses AGENTS.md and change_prompt).
  2. Run build/test commands from the prompt plan via skills/codex-readiness-integration-test/scripts/run_plan.py.
  3. Collect evidence (evidence.json), deterministic checks, and run automatic LLM evals via Codex CLI.
  4. Score and write the report + summary output.

Configuration

Optional fields in {out_dir}/prompt.pending.json:

  • agentic_loop: configure Codex CLI invocation for the agentic loop.
  • llm_eval: configure Codex CLI invocation for automatic evals.

If these fields are omitted, defaults are used.

Requirements

  • The LLM evaluator must fail if evidence mentions the phrase Context compaction enabled.
  • Use qualitative context-usage evaluation (no strict thresholds).

What this test covers well

  • Runs Codex CLI against the real repo root, producing real filesystem edits and git diffs.
  • Executes the approved change prompt and then runs the build/test plan in-repo.
  • Captures evidence, deterministic checks, and LLM eval artifacts for review.

What this test does not represent

  • The agentic loop may use non-default flags (e.g., bypass approvals/sandbox), so interactive guardrails differ.
  • Uses a dedicated HOME (.codex-home), which can change auth/config/cache vs normal CLI use.
  • Auto-generated prompts and one-shot execution do not simulate interactive guidance.
  • MCP servers/tools are not exercised unless explicitly configured.

Notes

  • The prompts in skills/codex-readiness-integration-test/references/ expect strict JSON.
  • Use skills/codex-readiness-integration-test/references/json_fix.md to repair invalid JSON output.
  • This skill calls the codex CLI. Ensure it is installed and available on PATH, or override the command in {out_dir}/prompt.pending.json.
  • If the agentic loop detects sandbox-blocked tool access, it now writes requires_escalation: true to {run_dir}/agentic_summary.json and exits with code 3. Re-run the integration test with escalated permissions in that case.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

26.6%
按下载量换算160

OpenCode

23.93%
按下载量换算144

Codex

15.94%
按下载量换算96

Gemini CLI

12.74%
按下载量换算77

Antigravity

8.33%
按下载量换算50

Cursor

3.59%
按下载量换算22

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/openai/skills --skill codex-readiness-integration-test;npx skills add openai/skills --skill "codex-readiness-integration-test" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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