flow-cytometry-analysis

Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.

Quality

73%

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 ./backend/cli/skills/biology/flow-cytometry-analysis/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, well-organized body with complete executable code for every capability. Its weaknesses are token inefficiency from duplicating bundled-script logic inline and a lack of explicit validation checkpoints in the workflows plus missing navigation to the existing scripts.

Suggestions

Reference the bundled scripts instead of re-implementing them inline (e.g. "For full implementations see scripts/cell_cycle.py, scripts/cfse_proliferation.py") to reduce duplication and improve progressive disclosure.

Add explicit validation checkpoints to the workflows (e.g. verify event counts after each gate, confirm curve-fit convergence for cell cycle) to support validate→fix→retry loops.

Trim the duplicated gating logic between Quick Start, Core Capabilities, and Typical Workflows to tighten conciseness.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-driven with little conceptual fluff, but at ~530 lines it re-implements logic inline that already exists in the bundled scripts and repeats gating logic across Quick Start, Core Capabilities, and Workflows, so it could be tightened.	2 / 3
Actionability	Every capability ships as complete, executable, copy-paste-ready Python (e.g. parse_spillover_matrix, compensate, dean_jett_fox, annexin_v_pi_analysis), matching the anchor for fully executable guidance.	3 / 3
Workflow Clarity	The Typical Workflows are clearly sequenced (compensate → gate → phenotype) and Troubleshooting aids recovery, but the workflows lack explicit validation checkpoints or validate→fix→retry feedback loops, leaving checkpoints implicit.	2 / 3
Progressive Disclosure	Section structure is clear, but four bundled scripts (cell_cycle.py, cfse_proliferation.py, gate_fcs.py, immunophenotype.py) are never referenced from the body while their functionality is re-implemented inline — content that should be separate is inline and references are not signaled.	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-scoped description with strong trigger terms and clear distinctiveness from flowio. It is held back only by the absence of an explicit positive "Use when…" clause, which caps completeness at 2.

Suggestions

Add an explicit positive trigger clause, e.g. "Use when analyzing flow cytometry data, building gating hierarchies, or running immunophenotyping/CFSE/cell-cycle/apoptosis assays."

Keep the existing flowio disambiguation but pair it with the positive trigger so both "when to use" and "when not to use" are explicit.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays" — matching the anchor for enumerating specific capabilities.	3 / 3
Completeness	The "what" is thorough, but there is no positive "Use when…" trigger clause — only a negative one ("For raw FCS parsing only use flowio"); per the guideline, a missing explicit trigger caps completeness at 2.	2 / 3
Trigger Term Quality	Uses natural domain terms a cytometry user would actually say ("flow cytometry", "gating", "compensation", "immunophenotyping", "CFSE", "apoptosis"), giving good coverage rather than abstract jargon.	3 / 3
Distinctiveness Conflict Risk	Carves a clear niche ("Complete flow cytometry analysis pipeline") and explicitly disambiguates from flowio ("Extends flowio… For raw FCS parsing only use flowio"), making mis-triggering unlikely.	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 (540 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/flow-cytometry-analysis/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.