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Addlightness bench

Skill 88plug/addlightness/skills/addlightness-bench

AI code fat trim for Claude Code & Grok — measure weight, trim, verify equivalence, and benchmark.

Install
npx -y skills add 88plug/addlightness --skill addlightness-bench

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

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What its author says it does

Copied from the file, not written here

Benchmark before/after code snapshots and report the performance delta with statistical significance. Runs N=10 timed runs (hyperfine if available, else a date+awk fallback) and computes % improvement. Use when the user says "benchmark this", "is it faster", "measure the speedup", "compare before and after", "did the trim help performance", "time these two", or invokes /addlightness-bench. Triggers on /addlightness-bench.

SKILL.md

4.8 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

/addlightness-bench -- before/after performance benchmark

Time two runnable commands head-to-head and report whether the difference is real -- not just noise. This is the measurement half of addlightness; it does not trim code (/addlightness) and does not measure static weight (/addlightness-review).

Scope

  • Benchmark ONLY. No edits, no weight metrics, no refactoring.
  • Input is two commands you can actually run; output is a timing comparison with a significance verdict.
  • If the user has not trimmed anything yet, point them at /addlightness. If they want code-weight numbers (LOC/complexity), point them at /addlightness-review.

Inputs

The user supplies two runnable commands: a before command and an after command. Read trailing args / the request as exactly that pair.

  • If they hand you two snapshot files instead of commands, ask for (or infer) the command that runs each -- e.g. node old.js vs node new.js, python3 before.py vs python3 after.py.
  • Both commands must do the same work on the same input -- otherwise the comparison is meaningless. State the assumption if you have to guess.
  • Quote each command so the harness receives it as one argument.

How to run

Call the benchmark harness once:

"${CLAUDE_PLUGIN_ROOT}/scripts/benchmark.sh" \
  --runs 10 --warmup 3 \
  --before '<before-command>' \
  --after  '<after-command>'

It uses hyperfine when present and falls back to a date+awk timing loop when it is not (this plugin assumes neither hyperfine nor any other profiler is installed, so expect the fallback). It prints one JSON line -- parse that, do not eyeball stdout. The emitted keys are exactly: before_ms, after_ms, pct_change (negative = after faster), faster (bool), welch_t, significant_at_95 (bool), runs, warmup, and tool. The harness does not compute median/p95/stddev -- do not expect or report those, even under hyperfine.

Reporting

Report a compact table, then a one-line verdict:

metricvalue
before mean (ms)before_ms
after mean (ms)after_ms
% changepct_change
welch twelch_t
significant at 95%significant_at_95
  • % change (pct_change) -- negative means faster (after took less time). State it as "X% faster" / "X% slower" so the sign is unambiguous.
  • Significance verdict -- gate every claim on the emitted significant_at_95 bool. The harness flags significance via a Welch t-test against a df-aware two-tailed 95% Welch critical value (emitted as t_crit_95; ~2.1-2.3 at the default N=10), NOT a fixed 1.96 — never recompute the verdict yourself.
    • If significant_at_95 is true -> report the speedup/regression as real.
    • If false -> say so plainly: "No statistically significant difference -- the observed delta is within run-to-run noise." Do not dress up a noise-level delta as a win. A faster-looking mean with significant_at_95 false is not a result.

Controls

For the numbers to mean anything:

  • Same machine, same conditions. Run before/after back-to-back / interleaved, not at different times of day.
  • Identical N and warmup for both commands; discard warmup runs from the stats.
  • Quiesce background load -- close heavy apps, no concurrent builds; CPU contention dwarfs small deltas.
  • Beware subprocess startup variance. Benchmarking node x.js / python3 x.py includes interpreter startup, which has large jitter. If the stddev is on the order of the mean difference, the signal is swamped -- recommend more runs (25-30+) and/or moving the measured work in-process rather than per-invocation.
  • No thermal/load drift. Don't compare a run from an hour ago against a fresh one -- rerun both together.

What NOT to do

  • Never report a single-run number as a benchmark. One run is an anecdote.
  • Never claim an improvement without passing the significance gate. No gate, no win.
  • Never compare runs taken hours apart or on different machines -- thermal state, background load, and CPU governor drift invalidate the comparison.
  • Never silently swap the commands' work. If before and after don't compute the same result, a timing delta is meaningless -- flag it, don't report it.
  • Don't trim or edit code here -- that's /addlightness.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

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