flowio

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.

Quality

92%

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

Quality

Content

92%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.

A well-structured, highly actionable skill body with executable examples, explicit validation feedback loops, and clean one-level-deep reference navigation. The main detractor is repeated inline version/date pinning that slightly inflates the token budget.

Suggestions

Consolidate the 'FlowIO 1.4.0' and 'verified on 2026-07-23' pins into a single version note (e.g., in the Install or Purpose section) and reference it once, removing scattered repetitions, to improve conciseness.

Consider adding the literal '.fcs' extension and a few plain-language trigger phrases ('.fcs files', 'flow cytometry data') so the description captures every natural user phrasing.

If version-specific behavior may drift over time, move legacy or version-bound caveats into a dedicated 'Version notes' subsection so time-sensitive detail does not penalize the lean core.

Dimension	Reasoning	Score
Conciseness	The body is dense and assumes Claude's competence (no explaining what FCS/NumPy are), but time-sensitive version pins ('FlowIO 1.4.0' repeated across sections, 'verified on 2026-07-23') appear inline rather than in a deprecated/old-patterns section, which the rubric flags as a conciseness cost.	4 / 5
Actionability	Fully executable, copy-paste-ready code blocks cover read, multi-dataset, create, and copy/rewrite flows, backed by specific install/inspect shell commands and concrete signatures like as_array(preprocess=True).	5 / 5
Workflow Clarity	A six-step numbered Operating Workflow pairs with explicit validation checkpoints ('Validate writes by reopening them', 'Validate both raw and preprocessed round-trips'), a strict/relax feedback loop, and a Non-Negotiable Checks checklist, matching the top anchor.	5 / 5
Progressive Disclosure	SKILL.md is an overview with quick starts, while a clearly signaled References section points one level deep to five real bundled files (api_reference, workflows, fcs_semantics, troubleshooting, sources) plus scripts/inspect_fcs.py; navigation is easy and content is appropriately split.	5 / 5
	Total	19 / 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 precise, third-person description that states concrete capabilities and explicit use triggers while cleanly boundary-flagging FlowKit for analysis tasks. Its only gap is the absence of the literal '.fcs' extension among the trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Read, inspect, and write', 'low-level FCS metadata and channel inspection', 'NumPy event extraction', 'multi-dataset files', 'table export', and 'FCS 3.1 creation' — giving comprehensive coverage rather than generic verbs.	5 / 5
Completeness	Explicitly answers both 'what' ('Read, inspect, and write FCS 2.0, 3.0, and 3.1 files with FlowIO') and 'when' ('Use for low-level FCS metadata and channel inspection, NumPy event extraction...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural terms with a synonym pair ('Flow Cytometry Standard (FCS)') and version variants (2.0/3.0/3.1), plus 'metadata', 'event extraction', and 'table export'; the explicit '.fcs' file extension and a few common phrasings are missing, keeping it just below the top anchor.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (low-level FCS I/O) and explicitly redirects adjacent work to 'use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces', minimizing conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/flowio/SKILL.md
Commit: b2a92ba

Reviewed: 3 days 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.