| name | benchmark |
| description | Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
|
Benchmark
Score a session against the rolling population average and report its percentile on
cost, tokens, tool count, and complexity using Agent Monitor data.
Input
The user provides: $ARGUMENTS
This may be:
- A single session ID — benchmark that session
- "latest" — benchmark the most recent session
- "latest N" — benchmark the N most recent sessions, each vs the average
- empty — benchmark the most recent session (default)
Data Sources
| Endpoint | Returns |
|---|
GET /api/sessions?limit=N | Population of sessions with cost, model, started_at, metadata (turn_count, total_turn_duration_ms) — builds the rolling baseline |
GET /api/pricing/cost/{sessionId} | { total_cost, breakdown:[{ input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost }] } — the target session's cost and tokens |
GET /api/workflows/{sessionId} | complexity (score), stats (tool/event counts), toolFlow (distinct tools used) — the target session's tool count and complexity |
GET /api/analytics | avg_events_per_session, tool_usage, daily_sessions — corroborates population-level averages |
Report Sections
1. Build the Baseline
Fetch the population with GET /api/sessions?limit=200 (the rolling set). For each
session gather cost (GET /api/pricing/cost/{id} or the list cost field), total
tokens (sum of the 4 token types from the pricing breakdown), tool count and
complexity (GET /api/workflows/{id}). Compute mean, median, and standard
deviation for each metric across the population.
2. Measure the Target
For the requested session, pull the same four metrics:
- Cost —
total_cost from GET /api/pricing/cost/{id}.
- Total tokens —
input + output + cache_read + cache_write summed from the breakdown.
- Tool count — distinct/total tools from
GET /api/workflows/{id} stats/toolFlow.
- Complexity score —
complexity.score from GET /api/workflows/{id}.
3. Percentile and Deviation
For each metric report the target's percentile within the population (share of
sessions at or below it) and its z-score (value − mean) / stddev. Label each:
below average / typical / above average / outlier (|z| > 2).
4. Verdict
State whether the session was normal overall. If it is an outlier, name which
metric drove it (e.g., complexity p96, cost p91 → an unusually heavy session).
Output
- A Markdown table: metric | session value | population mean | percentile | z-score | label.
- Currency in USD to 4 decimals; tokens and tool counts as integers; complexity to 2 decimals.
- Use ▲ for above-average and ▼ for below-average vs the mean.
- One-line verdict: "Normal session" or "Outlier — driven by (pNN)".
- When benchmarking multiple sessions, one row block per session plus a summary line.
- Read-only: percentiles come only from the fetched population; never fabricate the baseline.
