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modal-serverless-gpu

Run approved CPU or GPU work through OpenScience compute_job on the user's configured Modal account. Use for isolated scientific scripts, dependency provisioning, durable outputs, logs, status, cancellation, and recovery. Never invoke the Modal SDK or CLI directly.

72

Quality

90%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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 tight, well-structured instruction skill: an efficient contract, a clear sequenced workflow, and a concrete dispatch-card example. The main gaps are unshown action shapes for the post-dispatch operations and the absence of an explicit failed-job recovery loop.

Suggestions

Add a compact example (or field list) for the follow-up actions named in step 4 — status, logs, artifact retrieval, cancellation, release — so each is executable, not just named.

Add one explicit error-recovery step, e.g., 'If the job fails, read the returned logs, fix the script or packages, and dispatch a corrected job with a new name' to complete the validate-and-retry loop.

Clarify when action "plan" versus "start" is required with a one-line criterion, since step 2 currently leaves the choice to judgment.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence: terse contract bullets ('Commands are ordinary shell commands inside /workspace, such as python analysis.py') and a single copy-paste JSON card, with no explanation of what Modal or GPUs are. Every token earns its place, matching the 'lean and efficient' anchor rather than anchor 4's 'minor instances that could be trimmed'.

5 / 5

Actionability

The dispatch card example is concrete and executable (action/name/command/target/uploads/artifacts/packages with pinned versions), and upload/artifact globs are specified. However, the follow-up operations named in step 4 ('status, logs, artifacts, cancellation, delivery retry, or release') are only referenced, not shown in executable form, so it sits between 'mostly executable with minor gaps' (4) and fully copy-paste-ready coverage of common cases (5).

4 / 5

Workflow Clarity

The 5-step workflow is clearly sequenced with an inspection checkpoint (action: 'plan' before 'start') and post-run guidance to preserve failures and uncertainty, plus interruption handling ('keep the same job ID and inspect status later'). It falls short of anchor 5 because there is no explicit validate/fix/retry loop for a failed job, though verification states are reported — so the destructive/batch cap at 3 does not apply.

4 / 5

Progressive Disclosure

The skill is under 50 lines with no bundle files; the single external pointer ('Read <compute-capability>') is clearly signaled and one level deep, and sections (Contract, Workflow, example) are well organized. Per the simple-skill exception, this matches the top anchor; it does not exhibit the inline-bloat or buried-reference patterns of anchors 3 and below.

5 / 5

Total

18

/

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, lifecycle-complete, with an explicit 'Use when' clause and an explicit boundary against direct SDK/CLI use. The only gap is modest synonym coverage for trigger terms (e.g., serverless, cloud, batch).

DimensionReasoningScore

Specificity

Phrases like 'Run approved CPU or GPU work', 'dependency provisioning', and 'durable outputs, logs, status, cancellation, and recovery' list multiple concrete actions covering the full job lifecycle. It matches the comprehensive-coverage anchor and exceeds anchor 4, which expects only minor gaps; none are evident.

5 / 5

Completeness

The description explicitly answers both questions: 'what' ('Run approved CPU or GPU work through OpenScience compute_job on the user's configured Modal account') and 'when' ('Use for isolated scientific scripts, dependency provisioning, durable outputs, logs, status, cancellation, and recovery'). It clearly exceeds anchor 4, where the 'when' clause is present but less explicit.

5 / 5

Trigger Term Quality

Natural terms users would say are present ('CPU or GPU work', 'scientific scripts', 'logs, status, cancellation', 'Modal'), but common synonyms like 'serverless', 'cloud compute', 'remote execution', or 'batch' are missing. This fits 'good keyword coverage; a few natural terms missing' rather than the comprehensive-synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (Modal compute jobs dispatched via compute_job) and adds an explicit negative boundary ('Never invoke the Modal SDK or CLI directly'), minimizing conflict risk with adjacent compute or SDK skills. Anchor 4 would imply residual overlap risk with closely related skills, which the boundary clause forecloses.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/cloud-compute/modal/SKILL.md

Reviewed

Table of Contents

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