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.
75
66%
Does it follow best practices?
Impact
91%
2.27xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/flowio/SKILL.mdMemory-efficient metadata extraction
uv install command
100%
0%
only_text parameter used
100%
100%
pnn_labels access
0%
100%
pns_labels access
0%
100%
event_count and channel_count
0%
100%
Standard metadata keywords
100%
100%
scatter_indices access
0%
100%
fluoro_indices access
0%
100%
time_index access
0%
100%
Error handling present
0%
0%
FlowKit recommendation
100%
100%
FCS file creation and data modification
create_fcs import and use
0%
100%
opt_channel_names parameter
0%
100%
metadata dict passed to create_fcs
25%
100%
preprocess=False for modification
0%
100%
Extract-modify-recreate pattern
50%
100%
Original metadata preserved
30%
100%
pns_labels preserved
0%
100%
pnn_labels as channel names
0%
100%
FCS 3.1 float output acknowledged
50%
100%
uv install used
0%
100%
Output file produced
100%
100%
Error handling and multi-dataset support
FCSParsingError import
100%
100%
DataOffsetDiscrepancyError import
100%
100%
MultipleDataSetsError import
0%
100%
ignore_offset_discrepancy recovery
50%
100%
FCSParsingError recovery
50%
100%
read_multiple_data_sets used
83%
100%
nextdata_offset mechanism
50%
100%
Distinct except blocks
0%
100%
Result output produced
100%
100%
uv install used
0%
0%
read_multiple_data_sets iterates all datasets
100%
100%
b58ad7e
Table of Contents
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.