扫描代码库中的技术债，评估严重程度，跟踪趋势，生成按优先级排序的修复计划。适用于用户提到技术债、代码质量、重构优先级、债务评分、清理冲刺或代码健康评估时，也用于遗留代码现代化规…
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# Tech Debt Tracker **Tier**: POWERFUL 🔥 **Category**: Engineering Process Automation **Expertise**: Code Quality, Technical Debt Management, Software Engineering ## Overview Tech debt is one of the most insidious challenges in software development - it compounds over time, slowing down development velocity, increasing maintenance costs, and reducing code quality. This skill provides a comprehensive framework for identifying, analyzing, prioritizing, and tracking technical debt across codebases. Tech debt isn't just about messy code - it encompasses architectural shortcuts, missing tests, outdated dependencies, documentation gaps, and infrastructure compromises. Like financial debt, it accrues "interest" through increased development time, higher bug rates, and reduced team velocity. ## What This Skill Provides This skill offers three interconnected tools that form a complete tech debt management system: 1. **Debt Scanner** - Automatically identifies tech debt signals in your codebase 2. **Debt Prioritizer** - Analyzes and prioritizes debt items using cost-of-delay frameworks 3. **Debt Dashboard** - Tracks debt trends over time and provides executive reporting Together, these tools enable engineering teams to make data-driven decisions about tech debt, balancing new feature development with maintenance work. ## Quick Start — scan → prioritize → dashboard All paths relative to this skill folder. The scanner's JSON output feeds the prioritizer directly; dated inventory snapshots feed the dashboard. ### 1. Scan the codebase ```bash python3 scripts/debt_scanner.py /path/to/codebase --format json --output debt_inventory.json ``` Emits `debt_inventory.json` with `scan_metadata`, `summary`, `debt_items[]`, `file_statistics`, and `recommendations`. Report the `summary` counts to the user. (Dry run: `assets/sample_codebase`.) ### 2. Prioritize the backlog ```bash python3 scripts/debt_prioritizer.py debt_inventory.json --framework wsjf --team-size 6 --sprint-capacity 20 --format json --output debt_priorities.json ``` Frameworks: `cost_of_delay` (default), `wsjf`, `rice`. Output contains `prioritized_backlog` (work top-down), `sprint_allocation` (paste into sprint planning), and `insights`. ### 3. Track trends over time Keep dated snapshots (`debt_YYYY-MM-DD.json`), then: ```bash python3 scripts/debt_dashboard.py --input-dir snapshots/ --period monthly --format both --output debt_dashboard ``` Or pass files explicitly (samples: `assets/historical_debt_2024-01-15.json assets/historical_debt_2024-02-01.json`). The dashboard reports trend direction and executive-ready summaries — use it to verify a cleanup sprint actually reduced debt. ### Verification loop After a remediation sprint: re-run step 1, re-run step 3 with the new snapshot, and assert the targeted categories' counts dropped. A cleanup that doesn't move the dashboard is rework, not debt paydown. ## Technical Debt Classification Framework → See references/debt-frameworks.md for details (also: references/debt-classification-taxonomy.md, references/prioritization-framework.md, references/stakeholder-communication-templates.md) ## Common Pitfalls and How to Avoid Them ### 1. Analysis Paralysis **Problem**: Spending too much time analyzing debt instead of fixing it. **Solution**: Set time limits for analysis, use "good enough" scoring for most items. ### 2. Perfectionism **Problem**: Trying to eliminate all debt instead of managing it. **Solution**: Focus on high-impact debt, accept that some debt is acceptable. ### 3. Ignoring Business Context **Problem**: Prioritizing technical elegance over business value. **Solution**: Always tie debt work to business outcomes and customer impact. ### 4. Inconsistent Application **Problem**: Some teams adopt practices while others ignore them. **Solution**: Make debt tracking part of standard development workflow. ### 5. Tool Over-Engineering **Problem**: Building complex debt management systems that nobody uses. **Solution**: Start simple, iterate based on actual usage patterns. Technical debt management is not just about writing better code - it's about creating sustainable development practices that balance short-term delivery pressure with long-term system health. Use these tools and frameworks to make informed decisions about when and how to invest in debt reduction.
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