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roboflow-cloud-storage

Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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, well-structured API skill with an explicit validate-before-trigger workflow and clear lifecycle checkpoints. Minor gaps: no concrete sample invocation with argument values, and the detailed parameter tables could be split into a reference file.

Suggestions

Add one copy-paste-ready example invocation of connect_cloud_storage (or datasource_create) with realistic argument values to cover the most common setup case.

Consider moving the credential-type and mirror_configs parameter tables into a references/ file, keeping SKILL.md as a tighter overview with a one-level pointer.

Show a short poll-loop example (datasource_job_get on a batchId until status is terminal) to make the Trigger → Poll feedback loop fully concrete.

DimensionReasoningScore

Conciseness

Dense, factual reference with no padding or explanation of concepts Claude already knows (e.g., no 'what is S3'); every line carries tool names, parameters, defaults, or return shapes.

5 / 5

Actionability

Concrete tool names, parameter names, return shapes, and a sequenced fast path give highly actionable guidance, but there is no single example invocation showing real argument values for the common case.

4 / 5

Workflow Clarity

The Lifecycle section gives an explicit Create → Validate → Trigger → Poll sequence with a validation checkpoint before credit-consuming triggers and a poll step for error feedback, satisfying the batch-operation validation requirement.

5 / 5

Progressive Disclosure

Well-organized sections with clearly signaled one-level references to sibling skills; however all detail (credential-type and mirror-rule tables) is inlined in a single ~130-line doc with no split reference file.

4 / 5

Total

18

/

20

Passed

Description

100%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.

An exemplary description: explicit trigger clause, multiple concrete capabilities, natural synonyms, and a sharply defined niche with negligible conflict risk. No changes needed.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs' — with comprehensive coverage of the skill's capability surface.

5 / 5

Completeness

Opens with an explicit 'Use when connecting cloud storage ... to Roboflow to mirror images' trigger and follows with the concrete actions performed, clearly answering both what and when.

5 / 5

Trigger Term Quality

Natural user-facing terms ('cloud storage', 'AWS S3', 'S3-compatible', 'Google Cloud Storage', 'mirror images') with synonyms covered; no file-extension analog applies to this domain.

5 / 5

Distinctiveness Conflict Risk

The Roboflow + cloud-storage-mirror niche is highly specific with distinct triggers, making conflict with unrelated skills minimal.

5 / 5

Total

20

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
roboflow/computer-vision-skills
Reviewed

Table of Contents

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