Chandler · usage
Questions
1
LLM cost
$0.14
all calls priced
Cost / question
$0.1399
Success rate
—
0 failed
Latency p50
3.9 s
p95 3.9 s
Users
1
Sessions
1
1.0 questions each
Tokens
13,842 / 29
in / out

Questions per day

0122026-09-30 · 0 questions2026-10-01 · 1 questions30 Sep01 Oct

LLM cost per day

$0.00$0.10$0.202026-09-30 · $0.0000 recorded2026-10-01 · $0.1399 recorded30 Sep01 Oct

By tool

ask
1
$0.1399 recorded

By outcome

Feedback

Positive
0
Negative
0
Neutral
0
Unlinked
0

Feedback only attaches to a question when the calling model hands back a valid request_id. Unlinked rows are real feedback that reached no question page — listed below.

What Chandler said beyond the number

Answers carrying a caveat
0 of 0
Answers offered a sharper question
0
Asked what they expected
1
Asked which field was meant
0
Advice on a plain lookup
0

Advice on a plain lookup must read 0 — the firing policy refuses it, so any other value is a bug rather than a dial to turn. A high offered a sharper question rate is the thing to fix first: a check that fires on everything gets ignored on everything, and once the closing lines read as boilerplate the source citation is skipped with them. Asked and never resumed counts users left holding a question nobody relayed an answer to — a client that dropped the conversation. If that climbs, switch off the probe before the ambiguity check: one is a product experiment, the other is Chandler refusing to guess between two readings.

Question shapes

decision_shaped
1
$0.1399 recorded

(not classified) is a real state, not a gap: an exact cache hit answers before the router runs. If decision_shaped runs much above a third of classified questions the router is too generous, and the offer will feel constant however tight the gates below it are.

Top topics

1
$0.1399 recorded

Top users by cost

$0.1399
1 questions

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.