facs-gating-viz-style

Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Quality

30%

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

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/facs-gating-viz-style/SKILL.md

Quality

Content

42%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 mostly generic skill boilerplate wrapped around a placeholder script that does not perform the stated beautification task; concrete commands exist but the actual domain work is missing, and much of the content is duplicated process scaffolding.

Suggestions

Replace scripts/main.py's placeholder print() with real plot-beautification code (e.g. matplotlib/FlowCytometryTools styling) so the documented commands actually produce gated plots.

Remove the duplicated generic sections (Output Requirements, Error Handling, Input Validation, Response Template) that add no skill-specific value, or collapse them into a short shared reference.

Fix the dangling "See ## Features / Prerequisites / Usage above" pointers and provide concrete styling guidance (contour/density/dot parameters, color schemes, gate-drawing steps) instead of scope-only workflow scaffolding.

Dimension	Reasoning	Score
Conciseness	The body is padded with repetitive generic boilerplate (Output Requirements, Error Handling, Input Validation, Response Template, generic security/risk checklists) and "See ## X above" pointers that restate the description, matching the score-2 anchor of noticeable verbosity with several unnecessary padded sections.	2 / 5
Actionability	Commands are concrete and the usage example is executable, but scripts/main.py only prints placeholder text and does not actually beautify plots, with no real styling guidance, plotting code, or parameters beyond path/style, matching the score-3 anchor of some concrete but incomplete guidance missing key details.	3 / 5
Workflow Clarity	Generic process steps with scope/fallback checkpoints are present, but the actual core operation (beautifying plots) has no real sequenced steps and no feedback loop verifying the plot output itself, matching the score-3 anchor of a present sequence with missing validation checkpoints on the real task.	3 / 5
Progressive Disclosure	There is a single SKILL.md pointing one level deep to a real scripts/main.py, but substantial generic content is inlined and several "See ## X above" intra-file pointers are awkward rather than clearly signaled, matching the score-3 anchor of some structure with inline content that could be better organized.	3 / 5
	Total	11 / 20 Passed

Description

17%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.

The description is generic process boilerplate that does not mention flow cytometry, gating, FCS files, or plot beautification, so it would neither attract the right users nor distinguish this skill from other data-analysis skills.

Suggestions

Rewrite the description to name the concrete task first, e.g. "Beautify flow cytometry (FACS) gating plots for publication with contour, density, or dot styling."

Add an explicit "Use when..." trigger clause listing natural terms users would say: "Use when the user mentions flow cytometry, FACS gating, FCS files, or wants publication-ready gate plots."

Drop the abstract process language ("structured execution, explicit assumptions") from the description; keep the skill focused on its distinctive visualization capability.

Dimension	Reasoning	Score
Specificity	The description names a vague domain ("data analysis workflows") with only abstract process qualities ("structured execution, explicit assumptions, and clear output boundaries") and no concrete actions like flow cytometry gating or plot beautification, matching the score-2 anchor of domain-named but minimal/generic actions.	2 / 5
Completeness	The "what" is vague (no mention of beautifying gating plots) and the "when" is only weakly implied with no explicit "Use when..." trigger clause; capping at the score-2 level where one of what/when is weak and the other vague.	2 / 5
Trigger Term Quality	No natural user keywords appear (no "flow cytometry," "gating," "FACS," "FCS," or "plot"); the text is entirely generic process language, matching the score-1 anchor of no natural keywords.	1 / 5
Distinctiveness Conflict Risk	"Data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries" is extremely broad and would overlap with nearly any data-analysis skill, matching the score-2 anchor of very broad high-overlap-risk descriptions.	2 / 5
	Total	7 / 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/facs-gating-viz-style/SKILL.md
Commit: f5ef65b

Reviewed: ~2 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.