Chandler · usage
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Prompt

I need monthly bookings by sales rep, globally, for each month January 2026 through August 2026 inclusive. Before running a large query, please tell me how the model handles this: 1. Which measure is the governed "bookings" measure, and what does it count (new MRR? ARR? contract value?) 2. Is there a dimension that splits bookings into New Business vs Existing Business, and within Existing Business into Expansion vs Attrition/Contraction? I need those three categories separately because targets are set that way. 3. Which field is the sales rep / opportunity owner, and is there a team or region dimension alongside it? 4. Which date field drives the month (close date, booking date, effective date)? Give me the field names and the definitions, not the full dataset yet.

Outcome
success
Cost
$0.3739
excludes embeddings
Latency
24.9 s
Query attempts
0
Rows returned
—
Tokens
35,219 / 435
in / out

Request

request_id
27fad3f1-ac94-421b-a790-dd1f52445a91
When
2026-09-16 17:16
Tool
ask
User
chris.scott@dext.com · Chris Scott
Identity
google
Session
ba995a01-0dbb-4a67-ad2b-f6e34517f1a1
Model
gpt-6-astra

Route taken

Topic
—
Omni model
—
Match method
agent loop
Nudge shown
—
Question shape
not classified
Caveat raised
—
Sharper question offered
—

Per-step detail — each LLM call, each Omni request, each rejected draft — is traced to stdout only and is not stored, so it cannot be shown here. Query attempts counts run_query executions, not model turns.

Feedback

No feedback linked to this request.

Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.