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.

Quality

73%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./skills/bio/flowio/SKILL.md

Quality

Content

65%

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

The content is highly actionable with executable examples and reasonable sequencing, but it is verbose with significant repetition and inlines API reference material that should live in the (currently missing) referenced file. Batch workflows also lack validation feedback loops.

Suggestions

Create references/api_reference.md and move the detailed API attribute and FCS-keyword material there, leaving SKILL.md a lean overview that links to it.

De-duplicate the basic-reading examples shown in Quick Start, Standard Reading, and Inspecting into a single canonical example.

Add an explicit validate->fix->retry checkpoint to the batch directory-glob workflow (e.g., verify event/channel counts per file before appending to summaries).

Dimension	Reasoning	Score
Conciseness	The ~600-line body repeats basic reading across Quick Start, Standard Reading, and Inspecting sections and re-explains FCS segment structure and preprocessing formulas Claude already knows, so it could be tightened considerably despite being accurate reference material.	2 / 3
Actionability	Code examples are fully executable with concrete attributes and copy-paste-ready snippets (e.g., FlowData(...).as_array(), create_fcs(...)), matching the anchor for executable, specific guidance.	3 / 3
Workflow Clarity	Multi-step flows like multi-dataset handling and modify-and-re-export are sequenced with error recovery, but the batch directory-glob example lacks validate->fix->retry checkpoints, which caps batch-operation workflow clarity at 2 per the rubric.	2 / 3
Progressive Disclosure	A single one-level reference (references/api_reference.md) is signaled, but the referenced file does not exist in the bundle and a large amount of API-detail content is inline in SKILL.md rather than split out, matching the 'content that should be separate is inline' anchor.	2 / 3
	Total	9 / 12 Passed

Description

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.

The description is specific and trigger-rich, with concrete actions and clear niche distinctiveness. Its main weakness is the absence of an explicit 'Use when...' trigger clause, leaving the activation guidance only implied.

Suggestions

Add an explicit trigger clause such as 'Use when parsing or inspecting FCS files, extracting flow cytometry events, or converting FCS data to CSV/DataFrame.'

Mention the '.fcs' file extension to strengthen natural trigger coverage for users referring to files by extension.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Parse FCS files v2.0-3.1', 'Extract events as NumPy arrays', 'read metadata/channels', 'convert to CSV/DataFrame' — matching the anchor for several specific concrete actions.	3 / 3
Completeness	Clearly states what the skill does, but the 'when' is only implied via 'for flow cytometry data preprocessing' with no explicit 'Use when...' trigger clause, which caps completeness at 2 per the rubric guideline.	2 / 3
Trigger Term Quality	Includes natural terms a user would say — 'FCS', 'Flow Cytometry Standard', 'flow cytometry', 'NumPy arrays', 'CSV/DataFrame' — giving good coverage of likely user phrasing.	3 / 3
Distinctiveness Conflict Risk	FCS / flow cytometry file handling is a clear niche with distinct triggers unlikely to overlap with unrelated skills.	3 / 3
	Total	11 / 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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (608 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	13 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/bio/flowio/SKILL.md
Commit: df37802

Reviewed: about 1 hour ago

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