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.
73
66%
Does it follow best practices?
Impact
85%
1.11xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/opentrons-integration/SKILL.mdQuality
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 2-3 specific concrete actions the skill enables, such as 'pipetting commands, deck layout configuration, liquid transfers, and module control' 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 hardware 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
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a comprehensive but excessively verbose API reference that reads more like documentation than a focused skill file. While the code examples are excellent and fully executable, the content fails on token efficiency by including exhaustive method listings and basic explanations that Claude doesn't need. The lack of bundle files means all content is crammed into one massive file with no progressive disclosure.
Suggestions
Reduce content by 60-70%: move detailed API reference (module control methods, well access patterns, flow rate settings) into a separate `references/api_reference.md` file and keep only the protocol structure template and 1-2 key patterns in SKILL.md
Remove the 'When to Use This Skill' section entirely—this duplicates the frontmatter description and overview
Cut the 'Best Practices' and 'Troubleshooting' sections to only non-obvious, Opentrons-specific tips (e.g., thermocycler slot allocation, multi-channel column addressing behavior)
Add explicit validation workflow: show how to run `opentrons_simulate` on a protocol file and check output before deploying to robot hardware
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. Explains basic concepts Claude already knows (what aspirate/dispense mean, what PCR is, how Python dicts work). Lists every possible method exhaustively rather than focusing on what's non-obvious. The 'When to Use This Skill' section restates the overview. Best practices like 'add comments' and 'handle errors gracefully' are generic advice Claude doesn't need. | 1 / 3 |
Actionability | All code examples are concrete, executable Python with correct API calls, specific labware names, real parameter values, and complete protocol patterns. The serial dilution, plate replication, and PCR setup examples are copy-paste ready and cover common use cases thoroughly. | 3 / 3 |
Workflow Clarity | The protocol patterns show clear step sequences (load labware → load instruments → perform operations), but there are no validation checkpoints. No mention of running simulation to verify before execution (despite listing it in best practices), no error recovery loops, and no verification steps between critical operations like thermocycler cycling. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with everything inline. References to 'references/api_reference.md' and 'scripts/' directory at the bottom don't exist (no bundle files provided). The massive API reference content (well access methods, every module's full API, etc.) should be split into separate reference files, with SKILL.md serving as a concise overview. | 1 / 3 |
Total | 7 / 12 Passed |
Validation
81%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
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