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?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/biology/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.

A highly actionable, code-rich skill body with well-organized sections and a properly signaled one-level reference. It is somewhat verbose and could improve workflow clarity by adding validation/verification checkpoints for batch and export operations.

Suggestions

Add an explicit verification step to the batch-processing and create/export workflows (e.g. re-open the written FCS file and assert event_count/channel_count match expectations).

De-duplicate the Quick Start and 'Reading and Parsing FCS Files' sections, or shorten the Quick Start to a single minimal example that defers detail to Core Workflows.

Consider moving the longer 'Common Use Cases' examples into a separate reference file to reduce the main body's inline bulk.

Dimension	Reasoning	Score
Conciseness	The body is mostly code-driven and efficient, but the Quick Start repeats the FlowData reading shown again in Core Workflows, and the 'Inspecting FCS File Contents' example is verbose; it could be tightened without losing clarity.	2 / 3
Actionability	Nearly every section provides fully executable, copy-paste-ready Python with imports shown and concrete attribute/method calls, matching the copy-paste-ready anchor.	3 / 3
Workflow Clarity	Workflows are well-sequenced with a genuine error-recovery feedback loop, but the batch-processing example lacks a verification checkpoint and the create/export workflows omit output validation, so workflow clarity is capped at 2.	2 / 3
Progressive Disclosure	A real, clearly signaled one-level reference (references/api_reference.md) exists for detailed API docs, but the ~600-line body keeps many examples and use cases inline that could be split out, so structure is good but not optimal.	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.

A specific, well-targeted description with concrete actions and natural trigger terms. Its only weakness is the absence of an explicit 'Use when...' trigger clause, leaving the 'when to use' guidance implicit.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when parsing FCS/flow cytometry files or when the user mentions FCS, .fcs files, or flow cytometry data preprocessing.'

Include the '.fcs' file extension as an explicit trigger term to broaden natural keyword coverage.

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 multiple-specific-actions anchor.	3 / 3
Completeness	Clearly answers 'what' but lacks an explicit 'Use when...' trigger clause; 'for flow cytometry data preprocessing' is a purpose statement, not explicit trigger guidance, so completeness is capped at 2.	2 / 3
Trigger Term Quality	Covers natural terms a flow cytometry user would say — 'FCS (Flow Cytometry Standard) files', 'flow cytometry data', 'NumPy arrays', 'CSV/DataFrame' — including both the acronym and spelled-out name.	3 / 3
Distinctiveness Conflict Risk	The FCS / flow cytometry niche is distinct and specific, making it unlikely to trigger for 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/flowio/SKILL.md
Commit: 52845c3

Reviewed: about 11 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.