Conciseness

The body is mostly dense and earning its place, but includes unnecessary duplication and padding: the ≤5 vs ≥10 jobs rule is stated three times ("When to Use", the "Comparison with /run-experiment" table, and the "Rule" line), "Why This Exists" repeats "Rationale / Source", and internal migration/date-stamped details ("Phase 3.3 (Arch C)", "os.execv shims", the 2026-04-16 session audit) inflate the token budget without guiding execution. It is above the noticeably-verbose anchor (2) but has clear tightening opportunities.

Actionability

Guidance is fully executable: real bash for binding run identifiers (`PROJECT_DIR="${PROJECT_DIR:?...}"`), ARIS_REPO resolution with explicit error branches, scp/nohup launch commands, `%q`-escaped run_meta persistence, a jq one-liner for status rollup, concrete YAML manifest/grid/phase syntax, and the exact OOM regex `torch\.OutOfMemoryError: CUDA out of memory`. Common cases are covered with copy-paste-ready commands, with placeholders clearly flagged.

Workflow Clarity

The five-step workflow (Parse → Pre-flight → Launch → Monitoring → Post-completion) is clearly sequenced with explicit validation checkpoints: pre-flight gates (SSH, conda env, cwd, preconditions, GPU availability), expected-output-based completion verification, a bounded OOM detect→kill→delay→requeue feedback loop, stale-screen re-evaluation, wave-settlement conditions, and idempotent resume-on-restart. Batch-operation validation is present, so no cap applies.

Progressive Disclosure

Good structure with clear section headers and well-signaled one-level-deep references (`../shared-references/compute-env-contract.md`, `../shared-references/external-cadence.md`, canonical script paths in See Also); no bundle files exist to nest. It falls short of 5 because scheduler internals, the comparison table, and the rationale narrative are inlined in a ~400-line SKILL.md where some content (e.g., legacy-shim migration notes) could be split out or dropped.

Specificity

The description lists several concrete actions — "SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention" — naming the domain plus four specific capabilities. It falls short of a 5 because capabilities like grid/manifest building, state persistence, and resume-on-crash are absent, leaving minor gaps in coverage.

Completeness

It explicitly answers both questions: the first sentence states what the skill does (SSH job queue with OOM-aware retry, stale-screen cleanup, wave-transition race prevention) and the "Use when user says..." clause gives concrete trigger phrases plus an explicit insufficiency condition for the sibling skill. This matches the anchor for a clear, explicit what-and-when with concrete triggers.

Trigger Term Quality

Strong natural trigger phrases — "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments" — including a bilingual synonym and an actionable threshold ("when /run-experiment is insufficient for 10+ jobs"). A few natural variations are missing (e.g., "hyperparameter sweep", "queue experiments", "run experiments in parallel"), so it does not reach comprehensive coverage.

Distinctiveness Conflict Risk

The niche is clear — SSH-based orchestration of large ML experiment batches on GPU servers — and the trigger "when /run-experiment is insufficient for 10+ jobs that need orchestration" explicitly draws the boundary against the closest sibling skill, minimizing wrong-skill triggering.