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experiment-queue

SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is insufficient for 10+ jobs that need orchestration.

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%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.

The body is exceptionally actionable with a well-validated workflow, but it reads as a monolithic reference: scheduler internals duplicating the bundled script, duplicated rationale sections, and repo-migration history inflate the token cost. Splitting implementation detail into references/ and cutting the history would raise both conciseness and disclosure scores without losing executable guidance.

Suggestions

Move the scheduler-internal sections (Job State Machine, OOM Handling, Stale Screen Detection, Resume-on-restart) into a references/scheduler-internals.md file, since they describe behavior already implemented in scripts/queue_manager.py; keep SKILL.md as the operator-facing workflow overview.

Collapse Step 3a to a short rule ("use $CLAUDE_SKILL_DIR/scripts if present, else tools/experiment_queue") and delete the Phase 3.3 / Arch C / shim-provenance narrative and issue-number citations — they document repo history, not what the operator must do.

Merge "Why This Exists" with "Rationale / Source" (they repeat the same 2026-04-16 audit) and drop the feature-comparison table, whose content is already covered by "When to Use This Skill" and the closing rule about ≤5 vs ≥10 jobs.

Either bundle the two referenced shared-references files (external-cadence.md, compute-env-contract.md) inside the skill or summarize their operative rules inline, since the links currently point outside the skill directory and cannot be resolved from the bundle.

DimensionReasoningScore

Conciseness

The ~430-line body is mostly dense, non-obvious instruction (path forms for scp vs ssh, resume semantics, state-file caveats), but carries clear padding: "Why This Exists" and "Rationale / Source" repeat the same audit findings, the comparison table restates "When to Use"/"Key Rules", and Step 3a narrates repo migration history ("Phase 3.3 move, Arch C", issue numbers #174/#366, shim provenance) that Claude cannot act on. This sits between the 'mostly efficient' (3) and 'efficient, minor trimming' (4) anchors, but the volume of duplicated and historical material pulls it to 3.

3 / 5

Actionability

Nearly everything is copy-paste executable: the PROJECT_DIR/RUN_TS bootstrap with a guarded placeholder, the full QUEUE_TOOLS resolution chain with an explicit error message, the scp-vs-ssh path distinction with working commands, the nohup launch, the jq status query, and the run_meta.txt persistence using printf %q. Only routine placeholders (<server>) need substitution, which the skill flags in place.

5 / 5

Workflow Clarity

Steps 1–5 are clearly sequenced with explicit validation checkpoints: pre-flight (SSH, conda env, cwd, preconditions, GPU availability) that reports blocked jobs, expected-output-based completion rather than trusting screen state, bounded OOM retry as a feedback loop, stale-screen detection every 60s, and idempotent resume-on-restart. Error-recovery paths (resume without re-running bootstrap, stale state file from the 2026-08 fix) are called out explicitly.

5 / 5

Progressive Disclosure

Section headers are good and the two scripts/ bundle files (queue_manager.py, build_manifest.py) are real and correctly referenced, but the body inlines scheduler internals that duplicate the script — the job state machine, Stale Screen Detection, OOM Handling, and Resume-on-restart algorithms are already implemented in queue_manager.py and belong in a references/ file. The two "../shared-references/*.md" links point outside the skill directory and are not present in this bundle, so navigation to them cannot be confirmed. This matches 'some structure; content that should be separate is inline' rather than the level-4 anchor, where most content is appropriately split.

3 / 5

Total

16

/

20

Passed

Description

92%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.

A strong description: concrete third-person capability statement, explicit bilingual trigger phrases, and a clear boundary against the sibling /run-experiment skill. The only gap is minor synonym coverage for sweep-style phrasings.

DimensionReasoningScore

Specificity

The description names the domain ("SSH job queue for multi-seed/multi-config ML experiments") and lists multiple concrete capabilities: "OOM-aware retry", "stale-screen cleanup", and "wave-transition race prevention". Coverage is comprehensive for the niche, and it uses third-person voice throughout.

5 / 5

Completeness

It explicitly answers both questions: the 'what' is the opening capability sentence, and the 'when' is a concrete "Use when user says ..." clause with specific trigger phrases and a job-count threshold ("10+ jobs that need orchestration").

5 / 5

Trigger Term Quality

Includes natural trigger phrases users would say — "batch experiments", "run grid", "multi-seed sweep", "auto-chain experiments", plus the bilingual "队列实验" and the explicit /run-experiment boundary. A few common synonyms (e.g., "hyperparameter sweep", "grid search", "experiment queue") are missing, keeping it just below the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (multi-seed ML experiment orchestration on SSH GPU servers) and explicitly disambiguates against the nearest competing skill ("when /run-experiment is insufficient for 10+ jobs"), minimizing wrong-skill triggering.

5 / 5

Total

19

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

13

/

16

Passed

Repository

wanshuiyin/Auto-claude-code-research-in-sleep

Path

skills/experiment-queue/SKILL.md

Reviewed

Table of Contents

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