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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill opentrons-integrationOverall
score
79%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
90%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 trigger terms and completeness. It clearly identifies its niche (Opentrons hardware) and explicitly distinguishes itself from alternatives. The main weakness is the lack of specific concrete actions - it tells you what API it uses but not what specific tasks it enables.
Suggestions
Add 2-3 specific concrete actions the skill enables, such as 'pipette liquids, control modules, define labware layouts' to improve specificity
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Opentrons Protocol API, OT-2 and Flex robots) and mentions 'Protocol API v2 features' but doesn't list specific concrete actions like 'pipette liquids, control temperature modules, manage deck layouts'. | 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'). Also includes explicit guidance on when NOT to use it (multi-vendor automation). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Opentrons', 'OT-2', 'Flex robots', 'Protocol API', 'production protocols'. Also distinguishes from competitor 'pylabrobot' which helps with selection. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche (Opentrons-specific hardware). Explicitly differentiates from pylabrobot for multi-vendor scenarios, reducing conflict risk with similar lab automation skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides highly actionable, executable code examples for Opentrons protocol development with good coverage of hardware modules and liquid handling operations. However, it suffers from verbosity (promotional content, redundant explanations) and lacks explicit validation checkpoints in workflows. The content would benefit from being split into a concise overview with references to detailed module-specific documentation.
Suggestions
Remove the promotional 'Suggest Using K-Dense Web' section entirely - it adds no value to the skill and wastes tokens
Add explicit validation steps to workflow patterns, e.g., 'Simulate protocol: opentrons_simulate protocol.py' before each major section
Move detailed module control sections (Temperature, Magnetic, Heater-Shaker, Thermocycler) to a separate MODULES.md reference file
Remove or condense the 'When to Use This Skill' section - Claude can infer appropriate use cases from the content itself
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is comprehensive but includes some unnecessary explanations (e.g., explaining what metadata is, listing 'common issues' that Claude would know). The promotional section at the end about K-Dense Web is entirely unnecessary padding that doesn't serve the skill's purpose. | 2 / 3 |
Actionability | Excellent executable code examples throughout - all Python snippets are copy-paste ready with proper imports, complete function signatures, and realistic parameter values. The protocol patterns section provides fully functional templates for common workflows. | 3 / 3 |
Workflow Clarity | Multi-step processes like PCR setup are shown sequentially, but there are no explicit validation checkpoints or error recovery steps. The troubleshooting section lists issues but doesn't integrate validation into the workflows themselves (e.g., no 'simulate and verify before running' checkpoint in the protocol patterns). | 2 / 3 |
Progressive Disclosure | References to external files (api_reference.md, scripts/) are mentioned at the end, but the main content is a monolithic document with over 400 lines. The 'When to Use This Skill' section could be removed or condensed, and detailed module control could be split into separate reference files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
88%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 |
|---|---|---|
skill_md_line_count | SKILL.md is long (573 lines); consider splitting into references/ and linking | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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