usage-kernel

Codex Usage Tracker

Use the tracker as a factual local data plane. The tools return exact or

explicitly graded facts; the model owns inference, explanation, and

recommendations.

Use the three-step loop scope → batch → evidence:

1. Scope. Start with the needed usage_query whenever the question maps

to a curated template or a known typed request. The result carries its

generation, grade, history coverage, and cache state. Call usage_status

only when the query reports an absent or insufficiently fresh index, or

when the user explicitly asks about operational state. A committed

generation remains queryable while refresh is active or recommended. When

the needed fields are unfamiliar, set include_guidance=true on the same

usage_query call that carries the first batch; use an empty batch only for

standalone capability discovery.

1. Batch. Prefer one batched usage_query call. Its arguments always wrap

one or more query requests in the requests array. Execute a curated

server-side template with

{"requests":[{"template":"<name>"}]}; for the common thread leaderboard

use {"requests":[{"template":"top_threads"}]}. Templates query the

hydrated snapshot and report its coverage; refresh complete history only

when the user asks. For same five ranked threads, use result 1 for

labels, selectors, totals, shares, and token classes; use result 2 only for

cost/credits. Do not query again unless user asks for evidence. Use

{"requests":[{"template":"weekly_drivers"}]} for the latest indexed

seven-day thread leaderboard,

{"requests":[{"template":"week_over_week"}]} for that window versus the

immediately preceding seven days, and

{"requests":[{"template":"latest_incremental_change"}]} for the active

generation's inserted calls and leading affected thread. These templates

derive their anchors from the committed snapshot; do not discover dates or

generation numbers first. Use

{"requests":[{"template":"model_effort"}]} for model/effort mix and

{"requests":[{"template":"tools"}]} for structural tool facts. Do not repeat a

successful curated template or request guidance after it returns rows.

Supply parameters only when the selected template requires them.

Otherwise send only the typed dataset, operation, dimensions, measures,

filters, and limits needed for the question. Do not copy or reconstruct a

returned template body.

Preserve the returned generation, grade, coverage, counts, and explicit

row/byte limits. Compose filters as

{field, operator, value} using only the dataset fields and operators in

filter_grammar; in takes an array of 1–25 values. Phase queries require

one returned scope-filter template for a thread, turn, or time window.

1. Evidence. Rank candidates from the facts first. Call usage_evidence

only after ranking, and only with an exact returned logical selector. Use

live=true for the same timeline in live mode.

Label every claim:

• fact — returned exact/deterministic data; for partial, state the

hydration preset/cutoff and never generalize to all history;

• estimate — returned estimated data with coverage and provenance;
• hypothesis — model inference that still needs evidence;
• unsupported — unavailable from the returned scope and not asserted.

The optional context dataset is aggregate-only private local evidence. Its

observed_bytes and events measures are exact for the structurally observed

payload strings that were indexed. estimated_tokens is available only when

an explicit tokenizer populated it; always preserve its estimator identity and

coverage. Never describe category bytes or estimates as exact billed input

tokens, and keep unattributed_input_tokens unsupported when the response

reports it as null. If context composition is disabled, continue with the

base accounting datasets instead of starting hidden work.

Call usage_refresh only when freshness matters. Reuse the returned job; never

start a duplicate. Use usage_job_status with a bounded wait_seconds value

so the host waits; do not short-interval poll from the model. Use

usage_allowance for observed allowance facts and preserve its provenance and

limitations. Do not infer waste or productivity from token totals alone.

Never inspect raw logs as a fallback, invent missing selectors, claim narrative

findings the tools did not return, or expose prompts, reasoning, tool

arguments/output, secrets, or local paths.