tongminglaic/ako4x/bench
Claude Code and agent skill by tongminglaic/ako4x
- community
Checking that the SKILL.md file is still in the repository.
Run performance benchmarks to get a verdict on whether a kernel change actually helped. Use --ab-compare for sub-1x deltas (drift cancels in same container), --variance-check for noise floor, and the subset modes (--first, --smoke; --extremes is modal-backend only) for compile-correctness probes only — those are NOT performance verdicts. The only authoritative source for "is this change real". Benchmark specifics (config.toml schema, status enum, scoring formula, baseline rule, fresh-inputs contract) live in the `benchmark` skill.
Install
npx vskill@latest install tongminglaic/ako4x/benchSKILL.md
Bench
Single source of performance verdicts on this AKO4X harness. Command entry: bash scripts/bench.sh. The active benchmark is flashinfer-bench — its schema, behavior, and frozen-for-comparability segments live in the benchmark skill.
Detailed reference: benchmark.md. Top-level workflow:
Methodology under noise
Generic benchmarking methodology — applies regardless of the benchmark underneath:
- A/B compare (
--ab-compare <prior-label>) runs the current solution and a labeled trajectory snapshot back-to-back in the same process / container; cross-session drift cancels in the delta. First-line tool for any sub-1x decision. - Variance check (
--variance-check N) runs the current solution N times against itself to measure the session's noise floor. Use to set the threshold below which deltas should be treated as noise. - Drift-cancellation reasoning: any single-run
--labelheadline can move by per-session drift (Modal is typically a few % CV) without code change. A standalone +1x bump is not evidence of a real improvement on a noisy backend. - Subset filters (
--first,--smoke,--index,--group;--extremesis modal-backend only) are for compile / correctness probes only — running on ≤3 workloads typically shows 2-3x variance vs the full-bench mean and is NOT a performance verdict.
Frozen for bench comparability
The active benchmark's scoring formula, baseline freshness rule, and tolerance behavior are frozen across multi-run campaigns so results stay comparable. Edits to those get rejected across runs. The benchmark-specific frozen items are listed in the benchmark skill under "Frozen for bench comparability".
COUPLED references
- Runtime core:
scripts/bench_utils.py(shared by bench / profile / sanitize) - Local backend:
scripts/run_local.py - Modal backend:
scripts/run_modal.py - Correctness audit:
scripts/cheat_check_modal.py— modal-only; no local equivalent. Skipped on local-backend runs.
Editing scripts/bench_utils.py is allowed for non-frozen behavior (e.g., output formatting, error diagnostics). The frozen segments listed in the benchmark skill are not.
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingtemplates/skills/bench/SKILL.md
Popularity
- Repository stars
- 74
- Repository forks
- 13
- 7-day trend score
- 22
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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