Benchmark
Use when you need measured performance evidence by running a repeatable command on the current branch and a baseline refFrom its SKILL.md
npx -y skills add josephneumann/claude-corps --skill benchmarkAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 1 command, including `git status --short`.
SKILL.md
3.6 KB, 756 tokens by cl100k_base, as published. Nobody here has run it
/benchmark $ARGUMENTS
Run a repeatable benchmark command on the current branch and compare it with a baseline ref. This skill is for measurement, not speculation.
If the user wants architectural performance review without running code, use /multi-review with performance-oracle instead.
Arguments
/benchmark "<command>" [--baseline <ref>] [--iterations N]
Defaults:
--baseline origin/main--iterations 5- warmup runs:
1
Reject the request if no command is provided.
Safety Rules
- The benchmark command must be repeatable
- Do not use commands that intentionally mutate repo-tracked files
- Build artifacts and caches are acceptable if they are not tracked
- If the command dirties the worktree, stop and report it
Phase 1: Pre-Flight
- Record current branch and
git status --short - Verify the worktree is clean enough to benchmark safely
- Resolve the baseline ref
- Determine whether the benchmark command depends on repo-local dependencies, generated assets, or setup steps
- Create a temporary detached worktree for the baseline
- Establish equivalent runtime environments for current and baseline before timing anything
Use a temporary directory under /tmp or equivalent. Never benchmark the baseline by checking out over the user's current branch.
Environment setup rules:
- Read local sources of truth first:
CLAUDE.md,README.md, and project manifests such aspackage.json,pyproject.toml,Cargo.toml, orMakefile - If the benchmark command depends on installed dependencies or generated artifacts, run the same setup flow in both environments before measuring
- Prefer the project's existing setup command if one is documented, for example
pnpm install --frozen-lockfile,uv sync,cargo fetch, ormake setup - Do not count setup time as benchmark time
- If you cannot establish equivalent environments on both refs, stop and report that the benchmark would be untrustworthy
Phase 2: Measurement Protocol
For both current branch and baseline:
- Run 1 warmup execution
- Run
Nmeasured executions - Capture wall-clock duration for every measured run
- If the command prints benchmark metrics already, include them in the notes, but wall-clock time is the required common metric
Prefer a consistent timing mechanism for every run. Keep environment conditions as similar as possible across both refs.
Phase 3: Summarize Results
Compute for current and baseline:
- all measured run times
- median
- min
- max
- percentage delta of median vs baseline
If run-to-run variance is high, say so. Do not overclaim a tiny difference hidden by noise.
Required Output
## Benchmark Summary
Command: ...
Baseline: ...
Iterations: ...
### Current Branch
Runs: [...]
Median: ...
Min/Max: ...
### Baseline
Runs: [...]
Median: ...
Min/Max: ...
### Delta
Current vs baseline median: ...%
### Notes
- command-native metrics if any
- variance caveats
- anything that may have skewed results
Cleanup
Always remove the temporary baseline worktree before exiting, even on failure.
Failure Handling
- If the benchmark command fails on either ref, report the failing ref and stop
- If the baseline ref cannot be resolved, stop
- If cleanup fails, report it explicitly
Interpretation Rules
- Measured improvement beats theoretical reasoning
- High variance weakens confidence
- A benchmark proves only the command that was run, not overall product performance
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most evals benchmarks skills give in 756 tokens
Counted across 499 of the 513 authors here whose files we hold, read 2026-09-06
- Spawn with-skill and baseline runs in the same turnin 31 of 499, across 24 files
- Keep SKILL.md under 500 linesin 31 of 499, across 24 files
- Draft assertions while test runs are in progressin 31 of 499, across 24 files
- Compare against the baseline after changesin 31 of 499, across 13 files
- Define evals before codingin 26 of 499, across 17 files
- Run evals frequently during developmentin 25 of 499, across 16 files
- Keep evals fastin 24 of 499, across 15 files
- Version evals with codein 24 of 499, across 15 files
- Generate the eval viewer before evaluating outputs yourselfin 24 of 499, across 17 files
- Generate an eval report after runsin 24 of 499, across 15 files
- Track pass@k metrics over timein 22 of 499, across 14 files
- Save a baseline before making changesin 21 of 499, across 9 files
Said here and by no other author read
- Run the same benchmark on a baseline ref
- Record current branch and git status before starting
- Create a temporary detached worktree for the baseline
- Establish equivalent runtime environments on both refs before timing
- Prefer the project's documented setup command in both environments
- Capture wall-clock duration for every measured run
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.