Kiro Kmodel Calibrator

Overview

This skill defines the reproducible offline process for recomputing per-model Kmodel coefficients used by StaticFlow's conservative Kiro cache estimate.

The output is a recommendation, not a production write. Update the live values manually in /admin/kiro-gateway after reviewing the sample quality.

When to Use

• Kiro model pricing or multiplier guidance changed
• Recent Kiro credit usage trends drifted away from current estimates
• A new Kiro model needs a default Kmodel

Do not use this skill to mutate production config automatically.

Data Scope

Query the content DB table llm_gateway_usage_events under the canonical root:

• /mnt/wsl/data4tb/static-flow-data/lancedb

Use only rows matching all of these conditions:

• provider_type = "kiro"
• status_code = 200
• credit_usage_missing = false
• credit_usage is finite and >= 0
• created_at is within the last 30 days

Historical note:

• For Kiro calibration, existing input_uncached_tokens should be treated as the historical total input token estimate for that request.

Model Normalization

Normalize aliases before bucketing:

• claude-opus-4.6 -> claude-opus-4-6

Keep all other model names unchanged.

Formula

For each sample, define:

• Tin = input_uncached_tokens
• Tout = output_tokens
• Cobs = credit_usage

Drop samples where:

• Tin <= 0
• Tout < 0
• Tin > 200_000
• Tin + 5 * Tout <= 0

Then compute:

ratio = Cobs / (Tin + 5 * Tout)

Group by normalized model name and compute:

• sample count
• p50
• p80
• p90

Recommended runtime coefficient:

• Kmodel = p80

p80 is intentionally conservative: it reduces the chance of overstating cache_read_input_tokens.

Output Contract

For each model, report:

• normalized model name
• sample count
• p50
• p80
• p90
• recommended Kmodel

Also report:

• date window used
• filters applied
• rows dropped by each filter if available

Guardrails

• Do not auto-write the result into LanceDB or admin config
• Do not mix failed requests into calibration
• Do not use a single global coefficient across models
• If a model has too few samples, say so explicitly instead of inventing a value