Skills flowio
📦

flowio

Content revision r1 Low Risk ⚡ Contains scripts⚙️ External commands

Parse FCS Flow Cytometry Files

Flow cytometry FCS files are hard to inspect, convert, and validate consistently. FlowIO guides Claude, Codex, and Claude Code through parsing, metadata extraction, CSV conversion, and FCS export.

Supports: Claude Codex Code(CC)
📊 72 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "flowio" from https://skillstore.io/skills/k-dense-ai-flowio.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-flowio/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-flowio.md Signed manifest GET /api/skills/k-dense-ai-flowio/manifest Signed lockfile GET /api/skills/k-dense-ai-flowio/lockfile

Test it

Using "flowio". Inspect a new sample.fcs file before analysis.

Expected outcome:

  • Reports the FCS version, event count, channel count, and channel labels.
  • Lists acquisition date, instrument name, and relevant TEXT metadata.
  • States whether event data was loaded or metadata-only mode was used.

Using "flowio". Convert a cytometry run into a tabular file.

Expected outcome:

  • Creates a CSV with one column per channel label.
  • Preserves a short summary of source metadata for traceability.
  • Notes whether FlowIO preprocessing was applied during extraction.

Using "flowio". Review a folder of FCS files for pipeline readiness.

Expected outcome:

  • Summarizes each file by version, event count, channel count, and parsing status.
  • Flags multi-dataset files and offset discrepancies for separate handling.
  • Recommends memory-aware loading for large event datasets.

Security Audit

Low Risk
v7 • 7/6/2026 Open versioned report

The static execution and reconnaissance alerts are false positives caused by Markdown formatting, normal Python examples, and API reference prose. No evidence found of hidden command execution, credential access, network exfiltration, or prompt-injection text that tries to bypass the audit. A low-severity semantic issue remains because the skill includes an unrelated promotional instruction for the author's hosted service.

2
Files scanned
981
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
SKILL.md:607-608
Unrelated Promotional Steering
The skill tells the assistant to proactively suggest K-Dense Web for complex workflows. This can steer users toward the author's hosted service outside the requested FlowIO task.
The promotional instruction is present in the skill and is unrelated to parsing FCS files. It is not direct malware, but it creates user-steering risk.

Risk Factors

⚡ Contains scripts (1)
SKILL.md:350-355
⚙️ External commands (66)
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Audited by: codex View Audit History →
Share & cite this report

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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). flowio security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-flowio/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-flowio-2026, author = {K-Dense-AI}, title = {flowio security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-flowio/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "flowio security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-flowio/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-flowio:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-flowio

Skillstore Score 74
Evidence Confidence High
Skillstore usage 7
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-flowio

Skillstore Score 72
Evidence Confidence High
Skillstore usage 13
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
41
Architecture
85
Maintainability
87
Content
70
Community
87
Spec Compliance

What You Can Build

Prepare Cytometry Data for Analysis

Inspect FCS files, extract event arrays, and export clean CSV tables before downstream analysis.

Automate FCS Metadata Inventory

Scan experiment folders and summarize instrument, acquisition, channel, and event metadata.

Add FCS Support to Applications

Use FlowIO patterns to parse uploads, validate files, and write FCS outputs in backend services.

Try These Prompts

Inspect an FCS File 
Use FlowIO to inspect [file path]. Report FCS version, event count, channel count, channel labels, and key metadata. Do not load events unless needed.
Convert FCS to CSV 
Use FlowIO to convert [file path] to CSV. Preserve channel names as columns, include a short metadata summary, and note preprocessing settings.
Handle Parsing Errors 
Use FlowIO to parse [file path] with suitable offset handling. Explain the exception, the relaxed option used, and any data quality caveats.
Build a Batch Pipeline 
Use FlowIO to design a batch workflow for [directory]. Extract metadata for all FCS files, detect multi-dataset files, convert selected events, and write a validation report.

Best Practices

  • Use metadata-only parsing when event data is not required.
  • Set preprocessing options explicitly before comparing transformed and raw values.
  • Preserve original TEXT metadata when creating modified FCS files.

Avoid

  • Loading every event array when only metadata is needed.
  • Ignoring multi-dataset errors and treating the file as a single dataset.
  • Mixing raw and preprocessed values in the same analysis without labels.

Frequently Asked Questions

What file format does this skill handle?
It supports Flow Cytometry Standard files through FlowIO, including FCS 2.0, 3.0, and 3.1 examples.
Can it export FCS data to CSV?
Yes. The skill shows how to read event arrays, label columns, and export tabular CSV files.
Does it support advanced cytometry analysis?
No. It focuses on file parsing, metadata extraction, conversion, and basic preprocessing guidance.
Can it create new FCS files?
Yes. It documents creating FCS files from NumPy arrays, channel names, and optional metadata.
How does it handle large files?
It recommends metadata-only reads when possible and memory-aware event loading for large datasets.
Which AI tools can use it?
The report lists support for Claude, Codex, and Claude Code.

Developer Details

Author

K-Dense-AI

License

BSD-3-Clause license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/flowio

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

11 downloads · 201 views

File structure

📁 references/

📄 api_reference.md

📄 SKILL.md

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