flowio
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
75
66%
Does it follow best practices?
Impact
91%
2.27xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/flowio/SKILL.mdQuality
Discovery
82%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, domain-specific description with excellent specificity and distinctiveness. It lists concrete actions and includes rich trigger terms relevant to flow cytometry workflows. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about FCS files, flow cytometry data, or needs to parse .fcs files for analysis.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: parse FCS files, extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific actions, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied by the domain context. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'FCS', 'Flow Cytometry Standard', 'flow cytometry', 'NumPy arrays', 'metadata', 'channels', 'CSV', 'DataFrame', and version numbers (v2.0-3.1). Good coverage of domain-specific terms a user working with flow cytometry data would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — FCS files and flow cytometry data are very specific domains unlikely to conflict with other skills. The mention of specific file format versions (v2.0-3.1) further narrows the scope. | 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 excels at actionability with comprehensive, executable code examples covering all major FlowIO operations. However, it is significantly over-verbose, including unnecessary explanations, redundant sections, and inline content that should be in reference files. The document would benefit greatly from aggressive trimming to a concise overview with pointers to detailed reference materials.
Suggestions
Reduce the main file to Quick Start, Core Workflows (condensed), Error Handling, and references to separate files for use cases, troubleshooting, and advanced topics — aim for under 150 lines.
Remove the 'When to Use This Skill' section, the 'Summary' section, and explanatory text like 'FCS files contain rich metadata in the TEXT segment' that Claude doesn't need.
Move 'Common Use Cases', 'Troubleshooting', and 'Advanced Topics' into separate referenced files (e.g., references/use_cases.md, references/troubleshooting.md).
Add a validation step to the batch processing and modify-and-recreate workflows (e.g., re-read the output file and verify event_count matches expectations).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is extremely verbose at ~400+ lines. It explains FCS file structure (HEADER, TEXT, DATA, ANALYSIS segments), includes redundant sections (Summary repeats Overview), explains concepts Claude already knows (what NumPy arrays are, what Pandas DataFrames are), and has excessive use cases that could be in a separate reference file. The 'When to Use This Skill' section is unnecessary filler. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples throughout. Every workflow includes concrete Python code with proper imports, specific method calls, and realistic parameters. Error handling examples show actual exception classes and recovery patterns. | 3 / 3 |
Workflow Clarity | Multi-step workflows like 'Extract, Modify, and Recreate' are clearly sequenced with code, and error handling shows recovery paths. However, batch processing lacks validation checkpoints (e.g., no verification that output files are valid FCS), and the modify-and-recreate workflow doesn't include a validation step to confirm the output file is well-formed. | 2 / 3 |
Progressive Disclosure | There is a reference to 'references/api_reference.md' for detailed API docs, which is good. However, the main file is monolithic with extensive inline content (common use cases, advanced topics, troubleshooting) that should be split into separate files. The Quick Start section is well-placed but the document doesn't effectively triage what belongs in the main file vs. references. | 2 / 3 |
Total | 8 / 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 (607 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
25e1c0f
- Reviewed
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