# Benchmark

Benchmark one session (or a small recent set) against the rolling average using Agent...

- Repository: https://github.com/hoangsonww/Claude-Code-Agent-Monitor (1136 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill benchmark -g -a claude-code -y`
Codex: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill benchmark -g -a codex -y`
Cursor: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill benchmark -g -a cursor -y`
Gemini CLI: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill benchmark -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add hoangsonww/Claude-Code-Agent-Monitor`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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}`.

The rest: https://github.com/hoangsonww/Claude-Code-Agent-Monitor/blob/HEAD/plugins/ccam-insights/skills/benchmark/SKILL.md