Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add BuilderCed/agent-skills --skill tech-debt-detectorgit clone --depth 1 https://github.com/BuilderCed/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/builderced/agent-skills/tech-debt-detector)<a href="https://agentmods.dev/skills/builderced/agent-skills/tech-debt-detector"><img src="https://agentmods.dev/badge/skills/builderced/agent-skills/tech-debt-detector/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/builderced/agent-skills/tech-debt-detector"><img src="https://agentmods.dev/badge/skills/builderced/agent-skills/tech-debt-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01051 |
| Opus 5.5 | $0.00014 | $0.00420 |
| Sonnet 5.5 | $0.00007 | $0.00210 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
tech-debt-detector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Debt Detector (AI-Generated Code)
Research shows a 2500% increase in code defects linked to AI-generated code (BCG 2026). This skill identifies debt patterns specific to LLM outputs.
When to Use
- After generating code with AI (review before merging)
- During code review of AI-assisted PRs
- When refactoring AI-generated modules
- When investigating production issues in AI-written code
- Periodic tech debt audits
AI-Specific Debt Patterns
Pattern 1: Shallow Implementation
LLMs produce code that works for the happy path but fails on edges.
Signals:
- No error handling beyond generic try/catch
- No input validation at system boundaries
- No null/undefined checks on external data
- No timeout on network calls
- Functions that work for sample data but fail at scale
Check: For each function, ask: "What happens with empty input? Null? Very large input? Concurrent access? Network failure?"
Pattern 2: Over-Abstraction
LLMs love creating abstractions even when unnecessary.
Signals:
- Wrapper classes with no added logic
- Factory patterns for single implementations
- Interface with exactly one implementation
- Helper functions called only once
- Generic framework for a specific problem
Check: "Can I delete this abstraction and use the concrete implementation directly?"
Pattern 3: Stale Patterns
LLMs use patterns from training data that may be outdated.
Signals:
- Class components in React (should be hooks)
- Callbacks instead of async/await
- var instead of const/let
- jQuery patterns in modern codebase
- Deprecated API usage
Check: "Is this the current recommended pattern for this framework version?"
Pattern 4: Copy-Paste Drift
LLMs generate similar code for similar tasks without deduplication.
Signals:
- 3+ functions with >70% similar logic
- Same validation logic repeated in multiple places
- Similar error messages with slight variations
- Duplicate type definitions
Check: "Are there 3+ places doing essentially the same thing?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 138 lines · 34 tokens per session scan A bfb1279845ae
tech-debt-detector is a skill published in the GitHub repository BuilderCed/agent-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,051 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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