catalyst-activity-analysis-with-outlier-removal-inactivation-labeling-and-control-anchoring
Automates catalyst activity analysis from CSV: computes per-sample mean conversion or degradation rate after 3-sigma outlier removal within each group, anchors 'Control' (case-insensitively detected) at the first bar position with distinct gray styling, sorts remaining samples by descending mean activity, generates a publication-ready bar chart with Chinese/English label support, and annotates samples with mean <5% as 'INACTIVATION ZONE'. Supports catalytic conversion and photocatalytic degradation data.
npx skills add ECNU-ICALK/AutoSkill --skill catalyst-activity-analysis-with-outlier-removal-inactivation-lab --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# catalyst-activity-analysis-with-outlier-removal-inactivation-labeling-and-control-anchoring Automates catalyst activity analysis from CSV: computes per-sample mean conversion or degradation rate after 3-sigma outlier removal within each group, anchors 'Control' (case-insensitively detected) at the first bar position with distinct gray styling, sorts remaining samples by descending mean activity, generates a publication-ready bar chart with Chinese/English label support, and annotates samples with mean <5% as 'INACTIVATION ZONE'. Supports catalytic conversion and photocatalytic degradation data. ## Prompt # Goal Given a CSV file containing catalyst screening or photocatalytic degradation data with sample/group identifiers and numeric activity measurements (e.g., conversion, degradation, removal rate in %), compute the mean activity per sample/group after removing outliers (values outside [μ−3σ, μ+3σ] *within each group*), then generate a labeled bar chart where: (1) the 'Control' sample (detected case-insensitively via 'control', 'ctrl', '对照', '空白') is fixed at position 0 with gray fill (#808080); (2) all other samples are sorted descending by their cleaned mean activity; and (3)
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What does the catalyst-activity-analysis-with-outlier-removal-inactivation-labeling-and-control-anchoring skill do?
Automates catalyst activity analysis from CSV: computes per-sample mean conversion or degradation rate after 3-sigma outlier removal within each group, anchors 'Control' (case-insensitively detected) at the first bar position with distinct gray styling, sorts remaining samples by descending mean activity, generates a publication-ready bar chart with Chinese/English label support, and annotates samples with mean <5% as 'INACTIVATION ZONE'. Supports catalytic conversion and photocatalytic degradation data.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill catalyst-activity-analysis-with-outlier-removal-inactivation-lab --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.