使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill tech-debt开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Tech Debt Management
Systematically identify, categorize, and prioritize technical debt.
Categories
| Type | Examples | Risk |
|---|---|---|
| Code debt | Duplicated logic, poor abstractions, magic numbers | Bugs, slow development |
| Architecture debt | Monolith that should be split, wrong data store | Scaling limits |
| Test debt | Low coverage, flaky tests, missing integration tests | Regressions ship |
| Dependency debt | Outdated libraries, unmaintained dependencies | Security vulns |
| Documentation debt | Missing runbooks, outdated READMEs, tribal knowledge | Onboarding pain |
| Infrastructure debt | Manual deploys, no monitoring, no IaC | Incidents, slow recovery |
Prioritization Framework
Score each item on:
- Impact: How much does it slow the team down? (1-5)
- Risk: What happens if we don't fix it? (1-5)
- Effort: How hard is the fix? (1-5, inverted — lower effort = higher priority)
Priority = (Impact + Risk) x (6 - Effort)
Output
Produce a prioritized list with estimated effort, business justification for each item, and a phased remediation plan that can be done alongside feature work.
