Chandler · usage
← Back to sessions ← Back to this session

Prompt

Using the Salesloft tasks data (MELTANO.SALESLOFT.TASKS, joined to the Salesloft users table for owner names), count OPEN / not-completed tasks by task owner, split into two buckets: due date before today (overdue) and due date equal to today. Restrict to these Salesloft users: Eliana Brown, Luke Thomson, Freddie Brown, Joseph Ashcroft, Edwin Armah, Jack Hogg, Sam Tehrani, Dominik Kocis, Riaz Uddin, Joe Swanepoel. Also give the oldest overdue due date per owner. Return one row per owner.

Outcome
success
Cost
$1.1478
excludes embeddings
Latency
93.6 s
Query attempts
1
Rows returned
1
Tokens
1,282,852 / 4,176
in / out

Request

request_id
a92a9c43-f130-455e-8777-b0532eb4cf90
When
2026-09-10 08:13
Tool
ask
User
chris.scott@dext.com · Chris Scott
Identity
google
Session
5bd266da-b0ef-470e-b208-8921dd99db07
Model
gpt-5.6-sol

Route taken

Topic
Account Activity
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
feedback_nudge
Question shape
not classified
Caveat raised
—
Sharper question offered
—
Shown to user
How did I do with this report?

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.

SQL that ran

SELECT
  COUNT(*) AS "Indexed Lost Opportunity Tag Rows"
FROM PROD."TOUCHPOINTS_AI_LOST_OPPORTUNITY_TAGS" AS omni_dbt__touchpoints_ai_lost_opportunity_tags
LIMIT 1

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.