flowio

Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

78%Weight 40%Scale 1-5

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

The body is highly actionable with comprehensive executable examples and a clear workflow, but it is somewhat verbose and does not leverage the existing api_reference.md bundle — inlining reference detail rather than pointing to it.

Suggestions

Link the bundled reference from the body (e.g., 'See [api_reference.md](references/api_reference.md) for the full API') and move the detailed segment/preprocessing/channel material there to reduce inlined duplication.

Trim the 'Implementation Details' section to the essentials Claude cannot infer, since the same API detail already lives in api_reference.md.

Add a verification step after writing FCS/CSV outputs (e.g., re-read 'filtered.fcs' with FlowData and assert event_count) to close the workflow's feedback loop.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with structured sections and executable code, but the 'Implementation Details' segment re-explains FCS segment structure and transform formulas that partly duplicate the bundled api_reference.md and could be trimmed.	4 / 5
Actionability	Provides fully executable, copy-paste-ready code covering the common cases — reading, DataFrame/CSV export, filtering and writing FCS, multi-dataset handling, and metadata-only reads — with concrete imports and calls.	5 / 5
Workflow Clarity	The example lays out a clear numbered end-to-end sequence (read → export → filter/write → multi-dataset) with try/except error-recovery fallbacks, but lacks an explicit verification checkpoint after writing the new FCS/CSV outputs.	4 / 5
Progressive Disclosure	Section structure is present, but the bundled references/api_reference.md is never linked from the body and detailed API material (segments, preprocessing, channel indices) is inlined instead of deferred to that reference, so navigation is not clearly signaled.	3 / 5
	Total	16 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that concretely names the domain, capabilities, and trigger conditions with good synonym coverage. The only gap is the missing '.fcs' file extension as an explicit trigger term.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata', 'NumPy arrays, channel info, or CSV/DataFrame export' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Clearly states what it does ('Parse ... and extract events/metadata for preprocessing workflows') and gives an explicit trigger via 'e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files', answering both what and when with concrete triggers.	5 / 5
Trigger Term Quality	Good keyword coverage with synonyms ('FCS files', 'Flow Cytometry Standard', 'cytometry files', 'NumPy arrays', 'CSV/DataFrame'), but the natural '.fcs' file extension is missing, keeping it just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	The FCS / flow cytometry niche is highly specific with distinct triggers ('FCS files', 'cytometry files'), giving minimal conflict risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Data Analysis/flowio/SKILL.md
Commit: 63c61d3

Reviewed: 6 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.