skills.rest
Obsidian-Owl
Obsidian-OwlOFFICIAL
Explore all skills by this author →
WebsiteGitHub

dev-tech-debt-review

Detect AI/agentic anti-patterns in code changes and workflows.

1|Updated Jan 15, 2026
Search Tags:#linting#ai-agent#code-review#analyze#risk#tech-debt#conformance
One-click install
npx skills add https://github.com/Obsidian-Owl/agentlint --skill dev-tech-debt-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dev-tech-debt-review
Source: https://github.com/Obsidian-Owl/agentlint/tree/main/.claude/skills/dev.tech-debt-review
Command: npx skills add https://github.com/Obsidian-Owl/agentlint --skill dev-tech-debt-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill surfaces AI/agentic-specific anti-patterns that traditional linters miss. It focuses on tool/agent boundary violations, prompt debt, and context window issues to prevent performance and reliability problems.

Core Features & Use Cases

  • Automated anti-pattern detection across codebases, PRs, and release gates.
  • Evidence-based findings with citations to files and line numbers, aligned to Constitution principles.
  • Remediation guidance that helps teams plan targeted fixes and track improvements over time.

Quick Start

Start by auditing the changed files in your workspace: /dev.tech-debt-review. For a full audit, run /dev.tech-debt-review --all. Review the generated findings and map them to the Constitution references for remediation planning.

Frequently Asked Questions about dev-tech-debt-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect AI agent anti-patterns and prompt debt in my codebase?▼

To detect AI agent anti-patterns, scan changed files or the full codebase to surface tool boundary violations and prompt debt. This identifies agentic debt that traditional linters miss, citing file and line evidence in a structured report.

What is AI tech debt and how does it differ from traditional code debt?▼

AI tech debt includes agentic anti-patterns like tool boundary violations, prompt debt, and context window issues that traditional linters miss. It targets performance and reliability problems specific to AI-driven development workflows.

How do I audit my code changes for AI tech debt before a release?▼

Audit code changes for AI tech debt by scanning changed workspace files to identify boundary violations and prompt debt. Generate a structured report with file and line citations to map findings for targeted remediation.

Can I run a full codebase scan to find context window issues and agentic anti-patterns?▼

Yes, you can run a full codebase scan to find context window issues and agentic anti-patterns. This local, audit-friendly process analyzes the entire project to surface boundary violations and produces evidence-based tech-debt reports.

Does traditional linting catch AI agent boundary violations and prompt debt?▼

No, traditional linting does not catch AI agent boundary violations and prompt debt. You need specialized code review analysis that scans development workflows and code changes to surface these agentic anti-patterns and context window issues.

What is the best way to track AI tech debt remediation across a project?▼

The best way to track AI tech debt remediation is to generate structured reports with evidence-based findings and file citations. Map these findings to governance principles to plan targeted fixes and monitor improvements over time.

Related Skills

Explore More Software Engineering Skills→
UnsupervisedcomUnsupervisedcom
49

commit.review

Review changed files for quality issues, DRY violations, naming clarity, and missing tests.

Official
Advanced
mec07mec07
1

TechDebt

Create Jira tech debt tickets from brief notes with duplicate checks.

Community
Advanced
andregusman-raizandregusman-raiz
19

ag-referencia-qualidade

Enforce quality gates, post-execution checklists, and CI metrics in software projects.

Community
Advanced
ArabelaTsoArabelaTso
142

design-smell-detector

Detect design smells in Python codebases via static AST analysis.

Community
Intermediate