benchling-integration

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

Install with Tessl CLI

npx tessl i github:K-Dense-AI/claude-scientific-skills --skill benchling-integration

What are skills?

Overall
score

80%

Review — 78%

Does it follow best practices?

If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:

npx tessl skill review --optimize ./path/to/skill

Learn more

Validation — 14 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

Discovery

83%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description with excellent specificity and distinctive domain terminology that clearly identifies the Benchling R&D platform integration. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill over others.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user mentions Benchling, lab notebooks, DNA/protein registries, or needs to automate R&D data workflows.'

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.' These are concrete, actionable capabilities.	3 / 3
Completeness	Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through the domain context.	2 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'Benchling', 'registry', 'DNA', 'proteins', 'inventory', 'ELN', 'workflows', 'API', 'Data Warehouse', 'lab data'. Good coverage of domain-specific terms a biotech user would naturally use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with 'Benchling' as a specific platform name plus domain-specific terms like 'ELN entries', 'DNA', 'proteins', 'Data Warehouse'. Very unlikely to conflict with other skills due to the niche biotech/lab focus.	3 / 3
	Total	11 / 12 Passed

Implementation

73%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a solid, comprehensive skill for Benchling integration with excellent actionability through executable code examples and good progressive disclosure to reference materials. The main weaknesses are moderate verbosity in introductory sections and missing validation/error recovery patterns for batch and destructive operations, which is important for lab data management automation.

Suggestions

Add explicit validation and error handling loops to bulk operations (e.g., wrap bulk entity import in try/except with retry logic and validation checks)

Remove or condense the 'When to Use This Skill' section as it largely duplicates the overview and description

Add a validation checkpoint pattern for workflow automation showing how to verify task completion before proceeding to dependent operations

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary explanations (e.g., 'Benchling is a cloud platform for life sciences R&D' and the 'When to Use This Skill' section that largely restates the overview). The code examples are good but some sections could be tightened.	2 / 3
Actionability	Excellent actionability with fully executable Python code examples throughout, including SDK installation, authentication, CRUD operations, pagination patterns, and complete use case examples. Code is copy-paste ready with proper imports and realistic parameters.	3 / 3
Workflow Clarity	Steps are listed for various operations but validation checkpoints are largely missing. For bulk operations like 'Bulk Entity Import' there's no error handling or validation loop. The async operations section mentions waiting for tasks but doesn't show error recovery patterns.	2 / 3
Progressive Disclosure	Well-structured with clear overview sections and explicit references to detailed documentation (references/authentication.md, references/sdk_reference.md, references/api_endpoints.md). Navigation is clear with one-level-deep references properly signaled.	3 / 3
	Total	10 / 12 Passed

Validation

88%

Warnings & errors only

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

Criteria	Description	Result
description_trigger_hint	Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...')	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	14 / 16 Passed

Reviewed: about 3 hours ago

Table of Contents

Discovery Implementation Validation

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