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Benchmark

Skill josephneumann/claude-corps/skills/benchmark

Production workflow framework for Claude Code — multi-agent orchestration, parallel worktrees, autonomous task execution, and compound engineering skills

Install
npx -y skills add josephneumann/claude-corps --skill benchmark

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing 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.

What its author says it does

Copied from the file, not written here

Use when you need measured performance evidence by running a repeatable command on the current branch and a baseline ref

SKILL.md

3.6 KB, 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

  1. Record current branch and git status --short
  2. Verify the worktree is clean enough to benchmark safely
  3. Resolve the baseline ref
  4. Determine whether the benchmark command depends on repo-local dependencies, generated assets, or setup steps
  5. Create a temporary detached worktree for the baseline
  6. 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 as package.json, pyproject.toml, Cargo.toml, or Makefile
  • 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, or make 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:

  1. Run 1 warmup execution
  2. Run N measured executions
  3. Capture wall-clock duration for every measured run
  4. 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

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.