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 overlap 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 many sections and examples that could be offloaded to reference files or omitted entirely (e.g., basic pandas/numpy operations, FCS file structure explanation). The content would benefit greatly from aggressive trimming and better progressive disclosure.
Suggestions
Reduce the main skill to Quick Start, Core Workflows (reading, writing, multi-dataset), and Error Handling, moving Common Use Cases, Advanced Topics, and Troubleshooting to separate reference files.
Remove explanations of concepts Claude already knows, such as what FCS file segments are, how to convert arrays to DataFrames, and the 'When to Use This Skill' section.
Add explicit validation steps to batch processing and file modification workflows (e.g., verify output FCS file can be re-read after creation).
Consolidate redundant code examples—the CSV conversion, channel extraction, and filtering examples all demonstrate similar read-process-export patterns that could be combined.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~400+ lines. It explains FCS file structure (HEADER, TEXT, DATA, ANALYSIS segments), includes a 'When to Use This Skill' section listing obvious use cases, provides redundant examples (e.g., CSV conversion and channel extraction are straightforward pandas/numpy operations Claude already knows), and has sections like 'Integration Notes' and 'Summary' that add little value. Many code examples repeat similar patterns. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples throughout. Every workflow includes complete Python code with imports, specific method calls, and realistic parameters. The API usage is concrete with specific attribute names, method signatures, and parameter values. | 3 / 3 |
Workflow Clarity | Multi-step workflows like 'Extract, Modify, and Recreate' are presented with clear sequences. However, batch processing lacks validation checkpoints (no verification that output files are valid FCS), and the error handling section shows recovery patterns but doesn't integrate them into the workflows as explicit validation steps. | 2 / 3 |
Progressive Disclosure | There is a reference to 'references/api_reference.md' for detailed API docs, which is good. However, the main file contains far too much inline content that should be in separate reference files—the common use cases, advanced topics, and troubleshooting sections bloat the skill significantly. The overview doesn't effectively serve as a concise entry point. | 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
b58ad7e
- Reviewed
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