readiness-report
Compare original and translation side by side
🇺🇸
Original
English🇨🇳
Translation
ChineseAgent Readiness Report
Agent就绪度报告
Evaluate how well a repository supports autonomous AI development by analyzing it across eight technical pillars and five maturity levels.
通过从8个技术支柱和5个成熟度级别对代码仓库进行分析，评估其对AI自主开发的支持程度。
Overview
概述
Agent Readiness measures how prepared a codebase is for AI-assisted development. Poor feedback loops, missing documentation, or lack of tooling cause agents to waste cycles on preventable errors. This skill identifies those gaps and prioritizes fixes.
Agent就绪度用于衡量代码库为AI辅助开发做好了多少准备。反馈机制不完善、文档缺失或工具不足会导致Agent在可避免的错误上浪费时间。本技能可识别这些差距，并确定修复的优先级。
Quick Start
快速开始
The user will run to evaluate the current repository. The agent will then:
/readiness-report- Clone the repo, scan repository structure, CI configs, and tooling
- Evaluate 81 criteria across 9 technical pillars
- Determine maturity level (L1-L5) based on 80% threshold per level
- Provide prioritized recommendations
用户将运行来评估当前代码仓库。随后Agent会执行以下操作：
/readiness-report- 克隆仓库，扫描仓库结构、CI配置和工具
- 从9个技术支柱的81项标准进行评估
- 根据每个级别80%的阈值确定成熟度级别（L1-L5）
- 提供按优先级排序的建议
Workflow
工作流程
Step 1: Run Repository Analysis
步骤1：运行仓库分析
Execute the analysis script to gather signals from the repository:
bash
python scripts/analyze_repo.py --repo-path .This script checks for:
- Configuration files (.eslintrc, pyproject.toml, etc.)
- CI/CD workflows (.github/workflows/, .gitlab-ci.yml)
- Documentation (README, AGENTS.md, CONTRIBUTING.md)
- Test infrastructure (test directories, coverage configs)
- Security configurations (CODEOWNERS, .gitignore, secrets management)
执行分析脚本以从仓库中收集信息：
bash
python scripts/analyze_repo.py --repo-path .该脚本会检查以下内容：
- 配置文件（.eslintrc、pyproject.toml等）
- CI/CD工作流（.github/workflows/、.gitlab-ci.yml）
- 文档（README、AGENTS.md、CONTRIBUTING.md）
- 测试基础设施（测试目录、覆盖率配置）
- 安全配置（CODEOWNERS、.gitignore、密钥管理）
Step 2: Generate Report
步骤2：生成报告
After analysis, generate the formatted report:
bash
python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.json分析完成后，生成格式化报告：
bash
python scripts/generate_report.py --analysis-file /tmp/readiness_analysis.jsonStep 3: Present Results
步骤3：展示结果
The report includes:
- Overall Score: Pass rate percentage and maturity level achieved
- Level Progress: Bar showing L1-L5 completion percentages
- Strengths: Top-performing pillars with passing criteria
- Opportunities: Prioritized list of improvements to implement
- Detailed Criteria: Full breakdown by pillar showing each criterion status
报告包含以下内容：
- 总体得分：通过率百分比及达到的成熟度级别
- 级别进度：显示L1-L5完成百分比的进度条
- 优势：达标标准最多的支柱
- 改进机会：按优先级排序的待改进项列表
- 详细标准：按支柱划分的完整标准状态明细
Nine Technical Pillars
9个技术支柱
Each pillar addresses specific failure modes in AI-assisted development:
| Pillar | Purpose | Key Signals |
|---|---|---|
| Style & Validation | Catch bugs instantly | Linters, formatters, type checkers |
| Build System | Fast, reliable builds | Build docs, CI speed, automation |
| Testing | Verify correctness | Unit/integration tests, coverage |
| Documentation | Guide the agent | AGENTS.md, README, architecture docs |
| Dev Environment | Reproducible setup | Devcontainer, env templates |
| Debugging & Observability | Diagnose issues | Logging, tracing, metrics |
| Security | Protect the codebase | CODEOWNERS, secrets management |
| Task Discovery | Find work to do | Issue templates, PR templates |
| Product & Analytics | Error-to-insight loop | Error tracking, product analytics |
See for the complete list of 81 criteria per pillar.
references/criteria.md每个支柱针对AI辅助开发中的特定问题场景：
| 支柱 | 用途 | 关键信号 |
|---|---|---|
| 风格与验证 | 即时捕获bug | 代码检查器、格式化工具、类型检查器 |
| 构建系统 | 快速可靠的构建 | 构建文档、CI速度、自动化 |
| 测试 | 验证正确性 | 单元/集成测试、覆盖率 |
| 文档 | 为Agent提供指引 | AGENTS.md、README、架构文档 |
| 开发环境 | 可复现的环境配置 | Devcontainer、环境模板 |
| 调试与可观测性 | 诊断问题 | 日志、追踪、指标 |
| 安全 | 保护代码库 | CODEOWNERS、密钥管理 |
| 任务发现 | 确定待执行任务 | 问题模板、PR模板 |
| 产品与分析 | 从错误到洞察的闭环 | 错误追踪、产品分析 |
查看获取每个支柱的完整81项标准列表。
references/criteria.mdFive Maturity Levels
5个成熟度级别
| Level | Name | Description | Agent Capability |
|---|---|---|---|
| L1 | Initial | Basic version control | Manual assistance only |
| L2 | Managed | Basic CI/CD and testing | Simple, well-defined tasks |
| L3 | Standardized | Production-ready for agents | Routine maintenance |
| L4 | Measured | Comprehensive automation | Complex features |
| L5 | Optimized | Full autonomous capability | End-to-end development |
Level Progression: To unlock a level, pass ≥80% of criteria at that level AND all previous levels.
See for detailed level requirements.
references/maturity-levels.md| 级别 | 名称 | 描述 | Agent能力 |
|---|---|---|---|
| L1 | 初始级 | 基础版本控制 | 仅支持人工协助 |
| L2 | 管理级 | 基础CI/CD和测试 | 处理简单、定义明确的任务 |
| L3 | 标准化级 | 具备Agent生产就绪能力 | 处理日常维护工作 |
| L4 | 量化级 | 全面自动化 | 处理复杂功能开发 |
| L5 | 优化级 | 完全自主能力 | 端到端开发 |
级别进阶规则：要解锁某个级别，需通过该级别≥80%的标准且通过所有前置级别的标准。
查看获取详细的级别要求。
references/maturity-levels.mdInterpreting Results
结果解读
Pass vs Fail vs Skip
通过、失败与跳过
- ✓ Pass: Criterion met (contributes to score)
- ✗ Fail: Criterion not met (opportunity for improvement)
- — Skip: Not applicable to this repository type (excluded from score)
- ✓ 通过：符合标准（计入得分）
- ✗ 失败：不符合标准（待改进项）
- — 跳过：不适用于当前仓库类型（不计入得分）
Priority Order
优先级顺序
Fix gaps in this order:
- L1-L2 failures: Foundation issues blocking basic agent operation
- L3 failures: Production readiness gaps
- High-impact L4+ failures: Optimization opportunities
按以下顺序修复差距：
- L1-L2失败项：阻碍Agent基础运行的核心问题
- L3失败项：生产就绪性差距
- 高影响L4+失败项：优化机会
Common Quick Wins
常见快速优化项
- Add AGENTS.md: Document commands, architecture, and workflows for AI agents
- Configure pre-commit hooks: Catch style issues before CI
- Add PR/issue templates: Structure task discovery
- Document single-command setup: Enable fast environment provisioning
- 添加AGENTS.md：为AI Agent编写命令、架构和工作流文档
- 配置提交前钩子：在CI阶段之前捕获风格问题
- 添加PR/问题模板：规范任务发现的结构
- 文档化单命令环境搭建：实现快速环境部署
Resources
资源
- - Repository analysis script
scripts/analyze_repo.py - - Report generation and formatting
scripts/generate_report.py - - Complete criteria definitions by pillar
references/criteria.md - - Detailed level requirements
references/maturity-levels.md
- - 仓库分析脚本
scripts/analyze_repo.py - - 报告生成与格式化脚本
scripts/generate_report.py - - 按支柱划分的完整标准定义
references/criteria.md - - 详细的级别要求
references/maturity-levels.md
Automated Remediation
自动化修复
After reviewing the report, common fixes can be automated:
- Generate AGENTS.md from repository structure
- Add missing issue/PR templates
- Configure standard linters and formatters
- Set up pre-commit hooks
Ask to "fix readiness gaps" to begin automated remediation of failing criteria.
查看报告后，常见问题可自动修复：
- 根据仓库结构生成AGENTS.md
- 添加缺失的问题/PR模板
- 配置标准代码检查器和格式化工具
- 设置提交前钩子
发送“修复就绪度差距”指令，即可开始对未达标标准进行自动化修复。