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fla-triton-to-gluon

Workflow for porting an existing Triton kernel in `fla/ops/**` to Gluon (`triton.experimental.gluon`) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA (WGMMA / tcgen05), and scheduling (persistent kernels, warp specialization). Covers when a port is worth it, an incremental porting sequence that keeps numerical parity at every step, a Triton-to-Gluon API mapping, compile-time / autotune / smem-budget management for heavily unrolled kernels, and a pitfall checklist (proxy fences, mbarrier semantics, layout costs, bitwise-cancellation traps, NaN-poisoned OOB handling). Use when a Triton kernel is register-bound, when `num_stages` pipelining underperforms, or when Hopper/Blackwell features (TMA, TMEM, tcgen05) are needed.

73

Quality

92%

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SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, expert-level body: a clearly sequenced porting workflow with parity validation gates at every step, concrete executable commands, and a rare density of non-obvious, version-specific pitfalls. The main improvement lever is structural — splitting the pitfall checklist and deep detail into reference files — plus trimming a few framing passages and adding one concrete pipelining code example.

Suggestions

Move the pitfall checklist and the compile-time/autotune/smem-budget sections into a `references/pitfalls.md` (and optionally `references/compile-budget.md`), keeping SKILL.md as a lean overview with one-level-deep, clearly signaled pointers.

Add one small concrete cp.async or TMA pipelining code snippet (prologue/steady-state/epilogue with the mbarrier phase math) to convert the 'first big jump' section from an outline into copy-adaptable code, mirroring the tutorial kernels already cited.

Tighten the intro and Related-skills lines and compress the tutorial-name enumeration to just the ordered-list URL plus a note that the last six are advanced, trimming roughly a screen of tokens without losing navigability.

DimensionReasoningScore

Conciseness

The body is dense with non-generic, hard-won specifics (mbarrier 'noinc' semantics, 228KB smem budget math, proxy-fence exceptions) and assumes Claude's competence throughout, teaching no basic concepts. It sits at the 4 anchor rather than 5 because the ~230-line document has a few passages that could be trimmed — the intro framing, the Related-skills list, and the enumeration of all 15 tutorial names — though all are close to earning their tokens.

4 / 5

Actionability

Copy-paste-ready pytest and benchmark commands, an executable import block, a concrete Triton→Gluon mapping table, and specific debug commands (`gl.static_print`, NCU metric names) make this mostly executable guidance. It falls short of the 5 anchor because the pipelining section — flagged as 'the first big jump' — is presented as a labeled skeleton rather than code, and no complete kernel example is inlined (though the skeleton's flexibility is explicitly justified and complete kernels are pointed to in the repo).

4 / 5

Workflow Clarity

The porting sequence is explicitly numbered 1–5 with an explicit validation gate at every step ('must pass the same parity tests ... before any optimization', 'keeping numerical parity after every step'), a frozen pytest command, a re-autotune checkpoint after each layer, and a numbered pitfall checklist framed as 'check here first when things break' — a built-in error-recovery feedback loop matching the 5 anchor.

5 / 5

Progressive Disclosure

The body is well-organized with clear section headers, an at-a-glance mapping table, and well-signaled references (tutorial URL series, tutorial/example source paths in the Triton repo), making navigation easy. It scores 4 rather than 5 because it is monolithic — the pitfall checklist and the compile-time/smem-budget section are prime candidates for `references/` files to keep the top-level overview leaner.

4 / 5

Total

17

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: it states a concrete multi-part workflow in third person, includes an explicit 'Use when...' trigger clause with specific conditions, and occupies a niche so distinct that mis-triggering is essentially impossible. Every clause carries information with no buzzword padding.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — porting Triton kernels in `fla/ops/**` to Gluon for explicit control over 'tensor layouts, shared memory, async data movement (cp.async / TMA), MMA (WGMMA / tcgen05), and scheduling (persistent kernels, warp specialization)', plus 'API mapping, compile-time / autotune / smem-budget management ... and a pitfall checklist'. Coverage is comprehensive with no vague filler, matching the 5 anchor rather than the 4 anchor's 'minor gaps'.

5 / 5

Completeness

It explicitly answers 'what' (a porting workflow covering worth-it criteria, incremental parity-preserving sequence, API mapping, autotune/smem management, pitfall checklist) and 'when' via an explicit 'Use when a Triton kernel is register-bound, when `num_stages` pipelining underperforms, or when Hopper/Blackwell features ... are needed' clause with concrete trigger conditions — the 5 anchor shape.

5 / 5

Trigger Term Quality

Natural terms a user with this need would actually say are all present: 'Triton kernel', 'register-bound', '`num_stages` pipelining underperforms', 'Hopper/Blackwell features (TMA, TMEM, tcgen05)'. The domain is deeply technical and these are its natural vocabulary, matching the comprehensive-coverage anchor rather than the 'a few natural terms missing' 4 anchor.

5 / 5

Distinctiveness Conflict Risk

'Porting an existing Triton kernel in `fla/ops/**` to Gluon (`triton.experimental.gluon`)' is a sharp, distinct niche with trigger terms no other skill would claim; conflict risk is minimal.

5 / 5

Total

20

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20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
fla-org/flash-linear-attention
Reviewed

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