git:20260920.65367e0 to git:20260921.93b5e1e

---

name: arch-benchmark

description: "Run a benchmark scenario from this checkout: a skill of the plugin, a command, or a question-and-answer turn, executed in a declared runtime and scored by frontier models from several providers against the scenario's rubric. Use when a change to a skill, a lens, or the guideline needs a measurement rather than an opinion, or when someone asks how well a skill answers a question."

allowed-tools: Read, Grep, Glob, Bash(uv run:*), Bash(docker:*), Bash(ls:*), Bash(cat:*)

---

# arch-benchmark

Measure a subject against a rubric and report what the judges said.

The harness is at `${CLAUDE_SKILL_DIR}/../../benchmark/`, and it is

the only thing that runs a benchmark. Do not compose a provider call

by hand, and do not invent a flag: every flag is in

`${CLAUDE_SKILL_DIR}/../../benchmark/README.md`, and

`uv run benchmark/run.py --help` prints them. If the folder is

missing, stop and say the installation is incomplete.

## Input

`$ARGUMENTS` is one of:

- a scenario name, with or without flags ("explain-tenancy",

"review-om with all four judges");

- a question about what there is ("which scenarios are there?");

- empty, which means: list the scenarios and the provider

availability, and stop.

## Procedure

1. Find the checkout root: the harness sits at

`${CLAUDE_SKILL_DIR}/../../benchmark/`. Every command below runs

from the root, the folder that holds `benchmark/`.

2. Run `uv run benchmark/run.py list`. It prints the scenarios with

their kind and default judges, and each provider with its flag and

whether its key is present. Report an absent key as absent; it is a

provider that will be skipped, not a failure.

3. Map what the prompt asks to the flags that exist. The judges are a

bit flag: `3` is Anthropic and OpenAI, `7` adds Gemini, `15` adds

xAI; names joined by commas work too. Effort is `low`, `medium`, or

`high`. `--repeat N` runs the subject N times. `--runtime` is

`host`, `container`, or `vm`. When the prompt names something with

no flag behind it, say so and run without it.

4. Run the scenario, for example

`uv run benchmark/run.py --scenario explain-tenancy --providers 7 --effort medium --repeat 1`.

A run takes minutes and costs money at every provider selected. When

the prompt has not said which judges or how many repeats, use the

scenario's own defaults and say which they were.

5. When the prompt asks what a run would do rather than for a

measurement, add `--dry-run`: it resolves everything, writes

`run.json`, and calls nothing. A dry run leaves `run.json` and

nothing else, so read that file and report the resolved plan: the

subject command, the runtime, the judges with the model and the

fallbacks each would use, the effort, and the repeats. There is no

score to report, and inventing one is the worst thing this skill

could do.

7. List the run folder the command printed with `ls`, then read

`report.md` in it. Read `results.json` when a number in the report

needs its source.

8. Never edit a scenario, a rubric, or the model matrix to get a

better score. A score that needs the rubric changed is the finding.

## Output

After a dry run: the resolved plan from `run.json`, in prose, and the

sentence that nothing was executed and no provider was called.

After a measurement, short, in prose:

- the scenario, the runtime, the repeats, and the judges that answered,

each with its model id;

- the score per provider and the overall mean;

- the findings that matter, most severe first, in one line each;

- any provider that did not answer, with the reason it gave;

- the run folder path, and the fact that it is not checked in.

Say what was measured, not what it means for the roadmap.