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monitor-experiment

Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 lean, command-first monitoring workflow with concrete ssh/screen/modal/wandb examples and clearly gated optional steps. The main gaps are the unexecuted Feishu step, implicit rather than explicit validation checkpoints, and inlined W&B API detail that a reference file could absorb.

Suggestions

Make Step 6 actionable: include the actual Feishu API call or payload format, or point to a script/reference that implements it.

Add an explicit validation checkpoint before declaring an experiment done (e.g., verify the JSON result file is complete/non-empty and exit status is 0) rather than relying on the prose 'Key Rules'.

Move the wandb API snippets into a reference file and keep only the metric checklist inline to shorten the always-loaded body.

DimensionReasoningScore

Conciseness

The body is command-driven with almost no concept explanation — every step is a snippet or bullet — matching 'efficient; minor instances of over-explanation'. Not 5 because the external-cadence blockquote and asides like 'This gives the auto-review-loop richer signal' add tokens that don't change behavior.

4 / 5

Actionability

Steps 1–3.5 give copy-paste-ready ssh/screen/wandb-python commands, matching 'concrete code or commands with minor gaps'. Not 5 because Step 6 ('Send experiment_done notification') gives no actual command or payload, and several commands depend on unexplained placeholders like <results_dir>.

4 / 5

Workflow Clarity

A clearly sequenced 1→6 workflow with condition-gated branches (wandb, modal, feishu) and recovery guidance ('If hardcopy fails, check for log files', 'If results look wrong, check training logs for errors'), fitting 'clear sequence with most checkpoints present'. Not 5 because validation is advisory prose rather than explicit checkpoints, and no verification exists before the Feishu notification fires.

4 / 5

Progressive Disclosure

A single well-sectioned document with no bundle files; structure is good and navigable, matching 'good structure; most content appropriately placed'. Not 5 because the ~45-line W&B API section is detail that could live in a reference file, and the body links ../shared-references/external-cadence.md, a path outside the bundle that cannot be verified from this skill's directory.

4 / 5

Total

16

/

20

Passed

Description

83%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 with explicit what/when structure and natural trigger phrasing. Its only weaknesses are trigger terms that skew generic ("monitor", "is it done") and a capability list that undersells the summarize/interpret half of the skill body.

Suggestions

Add synonyms like "training done", "run status", or "fetch results" to the trigger list to reduce overlap with generic monitoring skills.

Mention the summarization/interpretation and comparison-table output so the 'what' matches what the skill actually delivers.

DimensionReasoningScore

Specificity

"Monitor running experiments, check progress, collect results" lists three specific concrete actions in a named domain, matching the 'several specific actions; minor gaps in coverage' anchor. Not 5 because the body also performs summarization, interpretation, and notification that the description omits.

4 / 5

Completeness

Explicitly answers both what ("Monitor running experiments, check progress, collect results") and when ("Use when user says 'check results', 'is it done', 'monitor', or wants experiment output") with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Concrete natural phrases users would say — "check results", "is it done", "monitor", "experiment output" — give good keyword coverage. Not 5 because common variants like "training done", "how are the runs going", or "fetch results" are missing.

4 / 5

Distinctiveness Conflict Risk

The ML experiment-monitoring niche is clear and mostly distinct, but the trigger words "monitor" and "is it done" are generic enough to overlap with other monitoring or loop-type skills. Fits 'minor overlap risk with closely related skills', not the minimal-conflict anchor.

4 / 5

Total

17

/

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/monitor-experiment/SKILL.md

Reviewed

Table of Contents

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