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.
1---2name: benchmark3description: 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.4---56# Benchmark78Score a session against the rolling population average and report its percentile on9cost, tokens, tool count, and complexity using Agent Monitor data.1011## Input1213The user provides: **$ARGUMENTS**1415This may be:16- A single session ID — benchmark that session17- "latest" — benchmark the most recent session18- "latest N" — benchmark the N most recent sessions, each vs the average19- empty — benchmark the most recent session (default)2021## Data Sources2223| Endpoint | Returns |24|----------|---------|25| `GET /api/sessions?limit=N` | Population of sessions with `cost`, `model`, `started_at`, `metadata` (turn_count, total_turn_duration_ms) — builds the rolling baseline |26| `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 |27| `GET /api/workflows/{sessionId}` | `complexity` (score), `stats` (tool/event counts), `toolFlow` (distinct tools used) — the target session's tool count and complexity |28| `GET /api/analytics` | `avg_events_per_session`, `tool_usage`, `daily_sessions` — corroborates population-level averages |2930## Report Sections3132### 1. Build the Baseline33Fetch the population with `GET /api/sessions?limit=200` (the rolling set). For each34session gather cost (`GET /api/pricing/cost/{id}` or the list `cost` field), total35tokens (sum of the 4 token types from the pricing breakdown), tool count and36complexity (`GET /api/workflows/{id}`). Compute mean, median, and standard37deviation for each metric across the population.3839### 2. Measure the Target40For the requested session, pull the same four metrics:41- **Cost** — `total_cost` from `GET /api/pricing/cost/{id}`.42- **Total tokens** — `input + output + cache_read + cache_write` summed from the breakdown.43- **Tool count** — distinct/total tools from `GET /api/workflows/{id}` `stats`/`toolFlow`.44- **Complexity score** — `complexity.score` from `GET /api/workflows/{id}`.4546### 3. Percentile and Deviation47For each metric report the target's percentile within the population (share of48sessions at or below it) and its z-score `(value − mean) / stddev`. Label each:49below average / typical / above average / outlier (|z| > 2).5051### 4. Verdict52State whether the session was normal overall. If it is an outlier, name which53metric drove it (e.g., complexity p96, cost p91 → an unusually heavy session).5455## Output5657- A Markdown table: metric | session value | population mean | percentile | z-score | label.58- Currency in USD to 4 decimals; tokens and tool counts as integers; complexity to 2 decimals.59- Use ▲ for above-average and ▼ for below-average vs the mean.60- One-line verdict: "Normal session" or "Outlier — driven by <metric> (pNN)".61- When benchmarking multiple sessions, one row block per session plus a summary line.62- Read-only: percentiles come only from the fetched population; never fabricate the baseline.
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