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benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

68

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

72%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 well-structured with strong progressive disclosure and actionable code, but its batch workflows omit validation/verification checkpoints, which caps workflow clarity. Minor conciseness gains are available by deduplicating the trigger list against the description.

Suggestions

Add validation/verification steps to batch workflows — e.g. check `export_data` is non-empty before `export_data[0].keys()`, and verify each bulk-imported sequence was created before proceeding.

Trim or merge the "When to Use This Skill" bullet list, which overlaps heavily with the frontmatter description.

Replace the `auto_validate(task)` placeholder in the workflow automation example with a concrete check or a note that the helper must be user-defined.

DimensionReasoningScore

Conciseness

Mostly efficient with executable code blocks and brief operational notes, but the "When to Use This Skill" list largely reiterates the description and could be trimmed.

4 / 5

Actionability

Provides concrete, mostly copy-paste-ready code for retry, pagination, fields, bulk import, inventory audit, workflow automation, and export; minor gap is the undefined `auto_validate(task)` placeholder in the workflow example.

4 / 5

Workflow Clarity

Sequences are present but batch operations (bulk entity import, workflow task updates, data export) lack validation or verification checkpoints, capping this dimension at 3 per the rubric's destructive/batch guidance.

3 / 5

Progressive Disclosure

Clear overview with well-signaled, one-level-deep references (core_capabilities, authentication, eventbridge, api_endpoints, sdk_reference), all of which exist as real files, with key examples inline and bulk detail pushed to references.

5 / 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, third-person description that clearly conveys both the capability surface and explicit usage triggers for the Benchling integration skill. It is comprehensive yet concise with no over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete capability areas — "registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries" — giving comprehensive coverage of what the integration does.

5 / 5

Completeness

Explicitly states both what it does and when to use it via the concrete "Use when automating lab data with benchling-sdk or the v2 API" trigger clause.

5 / 5

Trigger Term Quality

Good keyword coverage with synonyms ("Python SDK", "REST API", "benchling-sdk", "v2 API") plus the natural phrase "automating lab data", though a few common user phrasings are absent.

4 / 5

Distinctiveness Conflict Risk

Targets a clear niche (Benchling platform) with distinct triggers like "benchling-sdk" and "v2 API", presenting minimal conflict risk with other skills.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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