opentrons-integration

Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.

1.11x

Quality

70%

Does it follow best practices?

Impact

85%

1.11x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/opentrons-integration/SKILL.md

Quality

Discovery

89%

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 description that clearly identifies its niche (Opentrons Protocol API), provides explicit 'use when' guidance, and even includes negative routing to a related skill. Its main weakness is that it could be more specific about the concrete actions it enables (e.g., pipetting, deck setup, liquid transfers) rather than staying at the API/platform level.

Suggestions

Add specific concrete actions the skill enables, such as 'configure pipettes, define deck layouts, execute liquid transfers, manage labware' to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain (Opentrons Protocol API, OT-2 and Flex robots) and mentions 'writing protocols' and 'Protocol API v2 features', but doesn't list specific concrete actions like pipetting, deck layout configuration, or liquid handling steps.	2 / 3
Completeness	Clearly answers both what ('Official Opentrons Protocol API for OT-2 and Flex robots') and when ('Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features'), plus includes explicit negative routing guidance ('For multi-vendor automation... use pylabrobot').	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'Opentrons', 'OT-2', 'Flex', 'Protocol API', 'Protocol API v2', 'production Opentrons protocols'. Also includes the differentiator 'pylabrobot' to help with routing decisions.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with clear niche (Opentrons-specific protocols) and explicitly differentiates itself from a related skill (pylabrobot for multi-vendor automation), making conflict very unlikely.	3 / 3
	Total	11 / 12 Passed

Implementation

50%

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

The skill provides comprehensive, executable Opentrons API coverage with excellent code examples, but is far too verbose for a SKILL.md file—it reads more like full API documentation than a concise skill reference. It explains many concepts Claude already knows, lacks validation/simulation workflow steps for hardware operations, and keeps too much detail inline rather than splitting into reference files.

Suggestions

Reduce content by 60-70%: keep only the protocol structure template, one complete pattern example, and key gotchas/non-obvious details. Move module control, well access, and additional patterns to separate reference files.

Remove the 'When to Use This Skill' section entirely—this duplicates frontmatter metadata and explains obvious context.

Add explicit simulation/validation workflow: show the actual command to simulate a protocol (e.g., `opentrons_simulate my_protocol.py`) and what to check in the output before running on hardware.

Remove the troubleshooting section or reduce to only non-obvious issues; 'check for air bubbles' and 'verify module is connected' waste tokens on things Claude can reason about.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~400+ lines. Extensively documents basic API patterns Claude already knows or can infer from docstrings. Sections like 'When to Use This Skill', basic well access methods, and explanations of what aspirate/dispense mean are unnecessary padding. The troubleshooting section states obvious things like 'check for air bubbles' and 'verify module is connected.'	1 / 3
Actionability	All code examples are concrete, executable Python with correct API calls, specific labware names, pipette identifiers, and deck slot references. The protocol patterns (serial dilution, PCR setup, plate replication) are complete, copy-paste ready protocols.	3 / 3
Workflow Clarity	The protocol patterns show clear sequences of steps, but there are no validation checkpoints. The skill mentions 'simulate first' in best practices but doesn't show how to run simulation or validate output. For protocols controlling expensive lab hardware, missing verification steps (e.g., checking deck layout, validating volumes before execution) is a significant gap.	2 / 3
Progressive Disclosure	References to 'references/api_reference.md' and 'scripts/' directory exist at the bottom, but the main file is a monolithic wall of content that should be split. The extensive module control docs, well access methods, and protocol patterns could each be separate reference files, with SKILL.md serving as a concise overview pointing to them.	2 / 3
	Total	8 / 12 Passed

Validation

81%

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 — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (572 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	9 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: b58ad7e

Reviewed: 2 days ago

Table of Contents

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