
ai-debt-detector
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.
- Installs
- 3K
- Last 24h
- +33
- Trending rank
- #4223
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill ai-debt-detector- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ai-debt-detector automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
AI Debt Detector
Overview
AI agents generate code that passes the happy path but hides debt: missing error handling, orphaned resources, ignored failure modes, hallucinated packages, silent architectural drift. This skill forces a targeted audit for the exact patterns AI agents get wrong.
When to Use
- After any AI code generation session (20+ lines produced)
- Before merging AI-generated PRs
- When code works but something feels off
- After vibe-coding sprints where debt accumulates fastest
- When the agent claims done without showing verification
Process
After code generation, scan for these AI-specific debt patterns:
-
FAILURE MODES - What happens when this fails?
- Network timeout? Disk full? Permission denied? Null input?
- Is there a try/catch? Does it catch SPECIFIC errors or swallow everything?
- Are resources cleaned up on failure? (streams closed, connections returned, temp files deleted)
-
ORPHANS - What gets created but never cleaned up?
- Temp files, event listeners, intervals, subscriptions, connections
- Are there corresponding cleanup/dispose/close calls for every open/create?
- In React: does every addEventListener have a removeEventListener in cleanup?
-
EDGE CASES - What inputs break this?
- Empty array/string? null/undefined? Multi-MB input? Unicode? Concurrent calls?
- Does the code assume the happy path? (AI almost always does)
-
HALLUCINATED DEPS - Do all imports actually exist?
- Is every package in package.json/requirements.txt?
- Are API methods real? (AI invents plausible-sounding methods that don't exist)
- Does this library's latest version still export this function?
-
ARCHITECTURAL DRIFT - Does this match the project's patterns?
- Same error handling style as existing code?
- Uses the project's established utilities (not reinventing)?
- Follows the file structure convention?
Red Flags (stop and fix immediately)
catch (e) {}orcatch (e) { console.log(e) }- swallowed error- No
finallyblock when resources were opened // TODO: handle error- AI's way of punting- Import from a path that doesn't exist in the project
- Timeout set but no abort/cleanup on timeout
- Database connection opened but never released back to pool
Common Mistakes
- Trusting that compilation means correctness (compilation checks syntax, not logic)
- Reviewing only the diff without checking what the AI did NOT generate (missing error paths)
- Assuming the AI used the right library version (it often uses deprecated APIs)
- Skipping the orphan check because garbage collection handles it (it doesn't for connections, listeners, timers)
Why This Exists
AI agents systematically optimize for "looks correct" and "passes the happy path." They miss failure modes, orphan resources, and hallucinate dependencies at rates significantly higher than manual code. This skill forces an audit for those specific blind spots.
FAQ
What does the ai-debt-detector skill do?
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.
How do I install ai-debt-detector?
Run npx skills add https://github.com/wshobson/agents --skill ai-debt-detector in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai-debt-detector/ (personal) or .claude/skills/ai-debt-detector/ (project).
Which AI agents can use ai-debt-detector?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ai-debt-detector safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ai-debt-detector?
It is published by wshobson in wshobson/agents. It has 3,031 recorded installs.
More from wshobson/agents
typescript-advanced-types
wshobson/agents
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
tailwind-design-system
wshobson/agents
Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.
nodejs-backend-patterns
wshobson/agents
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
brand-landingpage
wshobson/agents
Brand-first landing page designer — runs a brand-identity interview (colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML. Use when the user asks to create, design, or build a landing page, homepage, or marketing page and has no established visual direction. Skip when they have a design mockup, need a dashboard or app UI, are working at component level, building a multi-page app, or restyling with known design tokens — use frontend-design instead.
python-testing-patterns
wshobson/agents
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
python-performance-optimization
wshobson/agents
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available