Files
AgentHub/plugins/agenthub-insights/skills/benchmark/SKILL.md
T
nntrivi2001 17d56d0e01 refactor!: rebrand CCAM to AgentHub
Clean break, no compatibility alias. Renames the product name, the CLI
binary (`ccam` -> `agenthub`), the env var prefix (`CCAM_` ->
`AGENTHUB_`), the Prometheus metric namespace (`ccam_*` ->
`agenthub_*`), the tmux session prefix (`ccam-lane-<id>` ->
`agenthub-lane-<id>`), the per-repo runtime profile directory
(`.ccam/profile` -> `.agenthub/profile`), the lanes state root
(`~/.claude/ccam-lanes` -> `~/.claude/agenthub-lanes`), the bundled
plugins (`plugins/ccam*` -> `plugins/agenthub*`) and their slash
commands (`/ccam-doctor` -> `/agenthub-doctor`, `/ccam:*` ->
`/agenthub:*`).

Because there is no alias, an existing install has to be updated by
hand: rename its `.ccam/profile` directory and any `CCAM_*` variables,
and reinstall the plugin under its new name.

Two other name families are deliberately left alone here, since they are
not just strings: the `Claude-Code-Monitor` repository/marketplace name,
and the `agent-dashboard` npm package plus its `~/.claude/agent-dashboard`
runtime directory, which holds the live database and needs a migration
rather than a rename.

Verified: server 1406/1406, client 459/459, `grep -i ccam` clean across
tracked files, `openapi.yaml` regenerated (95 paths, unchanged count).
2026-09-07 12:09:15 +07:00

67 lines
3.3 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
---
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 <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.