Agent skill · data analytics · erichowens
cost-verification-auditor
Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget
Why this skill is useful
Provides specific variance calculation methods and anti-patterns for accurate cost estimation that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
Cost Verification Auditor Verify that token cost estimates are within ±20% of actual Claude API usage. When to Use ✅ Use for: Validating token estimation systems after implementation Pre-deployment cost accuracy checks Debugging unexpected API bills Periodic estimation drift detection ❌ NOT for: Looking up model pricing (use pricing docs) Budget planning or forecasting Cost optimization strategies Comparing models by price Core Audit Process Decision Tree Variance Formula Common Anti-Patterns Anti-Pattern: The 500-Token Overhead Myth Novice thinking: "Claude Code adds ~500 tokens overhead, so add that to every estimate." Reality: Direct API calls have ~10 token overhead. The 500+ overhead is ONLY when using Claude Code's full context (system prompts, tools, conversation history). Timeline: Pre-2025: Many tutorials used 500+ token estimates 2025+: Direct API overhead is minimal (~10 tokens) What to use instead: Context Overhead --------- ---------- Direct API call ~10 tokens With system prompt 50-200 tokens With tools/functions 100-500 tokens Claude Code full context 500-2000 tokens How to detect: Consistent 40-90% overestimation = overhead too high. --- Anti-Pattern: Per-Node Accuracy Obsession Novice thinking: "Every node must be within ±20% or the estimator is broken." Reality: LLM output length is non-deterministic. Per-node output variance of 30-50% is normal. What matters is aggregate cost accuracy. What to use instead: Focus on total DAG cost variance (should be ±20%) Accept per-node output variance up to ±40% Use constrained prompts ("list exactly 3") to reduce variance How to detect: Input estimates accurate, output varies wildly = normal LLM behavior. --- Anti-Pattern: Peeking Before Estimating Novice thinking: "Let me run the API call first to see what tokens we get, then build the estimator." Reality: This produces perfectly-fitted estimates that fail on new prompts. Estimation must happen BEFORE execution. Correct approach: 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/cost-verification-auditor