Conciseness

Lean and efficient with zero padding — no explanation of what Triton or Liger is, no library tutorials; every line is a mode rule, stage, file path, checkpoint, or reference. "NVIDIA GPUs only" is a one-token constraint. Matches the 'every token earns its place' anchor.

Actionability

Concrete guidance dominates: the eight generated files are enumerated with exact paths and NEW/MODIFY flags ("src/liger_kernel/ops/{kernel}.py — NEW Triton kernels + autograd Function"), and mode detection is phrased as explicit verb lists. Not 5 because the executable substance (actual commands, code patterns) is delegated to analyzer.md/generator.md/validator.md and templates/ rather than anything runnable from the body itself — a minor gap for an orchestrator skill.

Workflow Clarity

Clear 3-stage sequence with explicit checkpoints between every stage ("**Human checkpoint:** Present PyTorch reference + kernel profile. Confirm before proceeding.") and a validation stage with a stated hard gate ("unit tests (hard gate — stops on persistent failure)"). Not 5 because the error-recovery feedback loop (fix and re-run on gate failure) is only implied by 'stops on persistent failure' and its detail lives in validator.md rather than being stated inline.

Progressive Disclosure

Textbook one-level-deep structure: the body is an overview and each workflow (Analyzer, Generator, Validator), the profile schema, three tier examples, and templates are clearly signaled with links. Not 5 because the referenced files (analyzer.md, generator.md, validator.md, kernel-profile-format.md, examples/*, templates/) are not present in the skill bundle, so the well-designed navigation cannot actually resolve.

Specificity

Multiple concrete actions are enumerated — "Creates new kernels from PyTorch operations (local files, URLs, code snippets, or natural language) with ops, module wrappers, functional APIs, unit tests, benchmarks, and plots" plus "Also modifies existing Liger kernels" — giving comprehensive coverage of what the skill produces. Not below 4 because coverage goes well beyond 'several specific actions' with input modalities, output artifact types, and both create/modify paths.

Completeness

Clearly answers both: what ("Develops production-ready Triton kernels... Creates new kernels... with ops, module wrappers, functional APIs, unit tests, benchmarks, and plots") and when ("Use when adding a new Triton kernel, converting a PyTorch operation to Triton, or updating an existing Liger kernel") with concrete trigger phrases. The 'Use when' clause is explicit, so the cap of 3 does not apply.

Trigger Term Quality

Good natural phrases users would say: "adding a new Triton kernel", "converting a PyTorch operation to Triton", "updating an existing Liger kernel". Not 5 because common synonyms like "writing", "optimizing", or "fusing" a kernel, or references to GPU/autograd contexts, are missing.

Distinctiveness Conflict Risk

Names a clear niche ("Triton kernels for Liger Kernel") with distinct triggers tied to that niche; minimal overlap risk with generic PyTorch or testing skills. Not below 5 because the domain-specific terms would not naturally trigger any other skill.