cost-verification-auditor

---
name: cost-verification-auditor
description: 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
license: MIT
---

# 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

```
Has estimator? ──No──→ Build estimator first (see Calibration Guidelines)
      │
     Yes
      ↓
Define 3+ test cases (simple/medium/complex)
      ↓
Estimate BEFORE execution (no peeking!)
      ↓
Execute against real API
      ↓
Calculate variance: (actual - estimated) / estimated
      ↓
Variance ≤ ±20%? ──Yes──→ PASS ✓
      │
     No
      ↓
Apply fixes from Anti-Patterns section
      ↓
Re-run verification
```

### Variance Formula

```typescript
const inputVariance = (actual.inputTokens - estimate.inputTokens) / estimate.inputTokens;
const outputVariance = (actual.outputTokens - estimate.outputTokens) / estimate.outputTokens;
const costVariance = (actual.totalCost - estimate.totalCost) / estimate.totalCost;

// PASS if both input AND output within ±20%
const passed = Math.abs(inputVariance) <= 0.20 && Math.abs(outputVariance) <= 0.20;
```

## 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. Estimate based on prompt length and heuristics
2. Execute API call
3. Compare variance
4. Adjust heuristics if needed

## Calibration Guidelines

### Input Token Estimation

```typescript
// Calibrated 2026-01-30
const inputTokens = Math.ceil(prompt.length / CHARS_PER_TOKEN) + OVERHEAD;
```

| Text Type | CHARS_PER_TOKEN | Notes |
|-----------|-----------------|-------|
| English prose | 4.0 | Most consistent |
| Code | 3.0-3.5 | Symbols tokenize differently |
| Mixed | 3.5 | Balanced (recommended default) |
| JSON/structured | 3.0 | Punctuation heavy |

### Output Token Estimation

| Prompt Constraint | Multiplier | Notes |
|-------------------|------------|-------|
| "List exactly N items" | 0.8x input | Highly constrained |
| "Brief summary" | 1.0x input | Moderate |
| "Explain in detail" | 2-3x input | Expansive |
| Unconstrained | 1.5x input | Variable |

**Always**: Minimum 100 output tokens for any meaningful response.

### Model Behavior

| Model | Output Tendency |
|-------|-----------------|
| Claude Opus | Longer, more detailed |
| Claude Sonnet | Balanced |
| Claude Haiku | Concise, efficient |

## Quick Fixes

| Symptom | Cause | Fix |
|---------|-------|-----|
| Overestimating by 40%+ | Overhead too high | Reduce from 500 → 10 |
| Underestimating inputs | Chars/token too high | Reduce from 4.0 → 3.5 |
| Output wildly varies | LLM non-determinism | Use constrained prompts |
| Total cost accurate but per-node off | Normal aggregation | Accept it, focus on totals |

## Verification Checklist

- [ ] 3+ test cases (simple, medium, complex)
- [ ] Estimates run BEFORE API calls
- [ ] Variance formula: `(actual - estimated) / estimated`
- [ ] Target: ±20% for input AND output
- [ ] Report includes actionable recommendations

## References

See `/references/calibration-data.md` for detailed calibration tables and historical data.