firefrost-gaming/claude-skills-reference

Files

Reza Rezvani 2f57ef8948 feat(agenthub): add AgentHub plugin with cross-domain examples, SEO optimization, and docs site fixes

- AgentHub: 13 files updated with non-engineering examples (content drafts,
  research, strategy) — engineering stays primary, cross-domain secondary
- AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent,
  dry_run.py validation (57 checks)
- Marketplace: agenthub entry added with cross-domain keywords, engineering
  POWERFUL updated (25→30), product (12→13), counts synced across all configs
- SEO: generate-docs.py now produces keyword-rich <title> tags and meta
  descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name
  propagates to all 276 HTML pages
- SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.),
  slug-as-title cleanup, domain label stripping from titles
- Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts
  references/, scripts/, assets/ links to GitHub source URLs; skills/index.md
  phantom slugs fixed (6 marketing, 7 RA/QM)
- Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands,
  21 plugins — consistent across CLAUDE.md, README.md, docs/index.md,
  marketplace.json, getting-started.md, mkdocs.yml
- Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-17 12:10:46 +01:00

4.7 KiB

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title, description

title	description
Tech Debt Tracker — Agent Skill for Codex & OpenClaw	Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw.

Tech Debt Tracker

:material-rocket-launch: Engineering - POWERFUL :material-identifier: `tech-debt-tracker` :material-github: Source

Install: claude /plugin install engineering-advanced-skills

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:

Debt Scanner - Automatically identifies tech debt signals in your codebase
Debt Prioritizer - Analyzes and prioritizes debt items using cost-of-delay frameworks
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.

Technical Debt Classification Framework

→ See references/debt-frameworks.md for details

Implementation Roadmap

Phase 1: Foundation (Weeks 1-2)

Set up debt scanning infrastructure
Establish debt taxonomy and scoring criteria
Scan initial codebase and create baseline inventory
Train team on debt identification and reporting

Phase 2: Process Integration (Weeks 3-4)

Integrate debt tracking into sprint planning
Establish debt budgets and allocation rules
Create stakeholder reporting templates
Set up automated debt scanning in CI/CD

Phase 3: Optimization (Weeks 5-6)

Refine scoring algorithms based on team feedback
Implement trend analysis and predictive metrics
Create specialized debt reduction initiatives
Establish cross-team debt coordination processes

Phase 4: Maturity (Ongoing)

Continuous improvement of detection algorithms
Advanced analytics and prediction models
Integration with planning and project management tools
Organization-wide debt management best practices

Success Criteria

Quantitative Metrics:

25% reduction in debt interest rate within 6 months
15% improvement in development velocity
30% reduction in production defects
20% faster code review cycles

Qualitative Metrics:

Improved developer satisfaction scores
Reduced context switching during feature development
Faster onboarding for new team members
Better predictability in feature delivery timelines

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.

4.7 KiB Raw Blame History