💸 LLM Cost Leak Linter

The problem

LLM application code often contains hidden patterns that drain budgets before billing alerts trigger. Developers lack automated ways to detect uncapped generations, expensive models used for trivial tasks, or inefficient prompting patterns during development.

What it does

• Scans source files for LLM calls lacking output caps or truncation logic.
• Identifies model calls inside loops or retry blocks without attempt limits.
• Flags expensive models used for high-frequency or simple tasks based on model-pricing.json.
• Detects instances where entire files are injected into prompts without preprocessing.
• Points out missing token usage logging and cost instrumentation.

Frameworks & tools

Supports Python, JavaScript, TypeScript, and React (JSX/TSX). Works with any LLM provider but requires manual updates to pricing reference files.

Why this beats prompting it yourself

Generic LLMs often miss specific architectural cost leaks like loop retry logic or missing backoff limits. This skill provides a structured heuristic scan that maps code patterns to specific cost-risk IDs rather than offering vague advice.

Use cases

• Pre-production audit of AI features to prevent runaway costs.
• Reviewing legacy LLM implementations for potential savings.
• Standardizing cost-conscious coding practices across a team.

Known limitations

This is a heuristic linter, not a runtime monitor. It identifies risky patterns but does not track real-world token usage or live expenditure.