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benchmark

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.

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  • SKILL.md3.3 KB
  • agents/openai.yaml256 B

SKILL.md(原文)

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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

EndpointReturns
GET /api/sessions?limit=NPopulation 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/analyticsavg_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 <metric> (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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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