Conciseness

The ~40-line body is relentlessly operational — every sentence carries non-obvious constraints ('Do not narrow parameter matrices, add skips, relax precision or thresholds, special-case test values, cache results across trials') or tool behavior the model cannot know ('the baseline worktree cannot reuse a checked-out branch, and slash-containing refs break its temporary directory naming'). No background on FLA, Triton, or benchmarking is re-explained; every token earns its place, matching anchor 5.

Actionability

Provides a copy-paste executable baseline command ('FLA_CI_ENV=0 python -m benchmarks.ops.verify --op chunk_gla --base "$FLA_BENCH_BASE"') plus concrete driver flags ('--test-file', '--gate-k', '--no-gate') and a real record template. The gap versus anchor 5: the command for benchmarking a candidate iteration and for the final head-to-head measurement is never shown, so the reader must infer the invocation from the baseline example.

Workflow Clarity

Three clearly sequenced phases (freeze gate and baseline → iterate from evidence → keep and report) with explicit validation checkpoints ('Confirm the baseline passes the full gate and measure it before changing kernels', 'benchmark only a passing candidate', 'Promote only with a full green gate') and error-recovery feedback loops ('After repeated attempts without an improvement above noise, re-profile and review the failed directions'; 'An unexplained large speedup needs investigation'). This matches the full anchor: sequence, checkpoints, and recovery loops.

Progressive Disclosure

The body stays a decision-level overview; detail is correctly split into two real, clearly signaled one-level-deep references ('Read [measurement and correctness traps](references/TRAPS.md) before interpreting results'; '[opt-log-template.md](references/opt-log-template.md) provides a compact record format'), both of which exist in the bundle and deliver what is promised. Scoping and platform profiling are delegated to sibling skills, and the search-history rule ('stays out of kernel comments and the final PR description') keeps ephemera out of the main flow — matching anchor 5.

Specificity

Names the domain ('FLA kernel performance', 'GLA' context via the skill name) and lists several specific process components — 'frozen correctness gate', 'measured baseline', 'reproducible candidate comparisons'. Each is stated as a noun phrase rather than a distinct concrete action, which leaves minor coverage gaps versus the comprehensive anchor 5, but it clearly exceeds the 1–2-action level of anchor 3.

Completeness

The 'what' is clear ('Iterate on FLA kernel performance with a frozen correctness gate, a measured baseline, and reproducible candidate comparisons'), but there is no 'Use when...' clause or equivalent explicit trigger guidance anywhere in the description. Per the judging guidelines this caps completeness at 3, matching the anchor 'clear what but when is missing or only weakly implied'.

Trigger Term Quality

'kernel performance', 'frozen correctness gate', 'baseline', and 'candidate comparisons' are natural phrases a maintainer would say when requesting this workflow. Common variations users might actually say — 'speed up', 'faster', 'optimize', 'tune', 'benchmark', 'regression' — are only partially covered, so it fits anchor 4 (good coverage, a few natural terms missing) rather than the synonym-complete anchor 5.

Distinctiveness Conflict Risk

'FLA kernel performance' pins a clear niche with low conflict against generic skills, but the performance/benchmark vocabulary ('measured baseline', 'candidate comparisons') overlaps with the closely related sibling profiling skills (fla-nvidia-performance, fla-ascend-performance), fitting anchor 4 (mostly distinct, minor overlap with closely related skills) rather than the minimal-conflict anchor 5.