Is roc-diagnostic-performance safe to install?

SaferSkills independently audited roc-diagnostic-performance (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

awesome-med-research-skills/Data Analysis/roc-diagnostic-performance/SKILL.md · 1 file

awesome-med-research-skills/Data Analysis/roc-diagnostic-performance/SKILL.md10.3 KB · Markdown

name: roc-diagnostic-performance

description: "Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction."

ROC Diagnostic Performance

When to Use

Use this skill when you need to:

evaluate one or more diagnostic marker genes in a case-control cohort;
build a multivariable logistic regression diagnostic model from marker expression values;
compare the ROC performance of the full model against individual markers.

Typical user requests:

"Use these genes to build a diagnostic ROC model for case vs control samples."
"Evaluate the AUC of FOXP3, CD45, and CD3E and plot all ROC curves together."
"Run logistic regression on biomarker expression and export ROC results."

When Not to Use

Do not use this skill for:

survival or prognostic analysis with time-to-event outcomes;
multiclass classification tasks;
calibration plots, nomograms, or decision-curve analysis;
non-expression diagnostic inputs such as imaging, clinical scores, or mutation-only tables.

When to Read External Files

Situation	File to Read	Purpose
Need algorithm details	`references/algorithm.md`	Logistic regression, ROC, AUC, and modeling assumptions
Need to run analysis	`scripts/main.R`	Execute `Rscript scripts/main.R --expression_file ... --group_file ...`
Encounter errors	`references/troubleshooting.md`	Common `SKILL_*` errors and solutions
Need CLI examples	`references/cli-guide.md`	Detailed command-line examples
Need test data	`tests/data/`	Example expression matrix and group file

Usage

Rscript scripts/main.R \
  --expression_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --marker_genes FOXP3,CD45,CD3E \
  --case_group Disease \
  --output_dir ./output/ \
  --seed 42

Arguments

Short	Long	Type	Default	Description
`-e`	`--expression_file`	character	required	Expression matrix file in CSV/TSV format
`-g`	`--group_file`	character	required	Group file with sample IDs and labels
`-m`	`--marker_genes`	character	required	Comma-separated marker genes
`-c`	`--case_group`	character	required	Case group label in the group file
	`--group_col`	character	`NULL`	Optional group column name; auto-detected if omitted
`-o`	`--output_dir`	character	`./output/`	Output directory
	`--overwrite`	flag	`FALSE`	Allow writing into a non-empty output directory
`-s`	`--seed`	integer	`42`	Random seed for reproducibility
`-T`	`--timeout_seconds`	integer	`0`	Elapsed time limit in seconds; `0` disables timeout
	`--plot_width`	double	`6`	ROC plot width in inches
	`--plot_height`	double	`6`	ROC plot height in inches
	`--font_family`	character	`sans`	PDF font family
	`--line_colors`	character	`#E64B35,#4DBBD5,#00A087,#3C5488,#F39B7F`	Comma-separated ROC line colors
	`--line_width`	double	`1.2`	ROC curve line width
	`--show_diagonal`	character	`true`	Show diagonal reference line: `true` or `false`
	`--diagonal_color`	character	`#7F7F7F`	Diagonal line color
	`--diagonal_lty`	integer	`2`	Diagonal line type
	`--plot_title`	character	`ROC Diagnostic Performance`	ROC plot title
	`--x_label`	character	`1 - Specificity`	X-axis label
	`--y_label`	character	`Sensitivity`	Y-axis label
	`--base_cex`	double	`0.9`	Base text-size multiplier
	`--legend_position`	character	`bottomright`	Legend position
	`--legend_cex`	double	`0.8`	Legend text size

Input Format

Expression Matrix (`expression_file`)

CSV or TSV file with genes as rows and samples as columns. The first column must store unique gene identifiers.

gene,Sample1,Sample2,Sample3
FOXP3,8.4,7.1,3.8
CD45,2.1,1.9,5.4
CD3E,5.8,6.2,4.0

Requirements

File extension must be .csv, .tsv, or .txt.
The first column must contain non-missing, unique gene identifiers.
Remaining columns must be sample IDs.
Selected marker genes must have numeric finite expression values across matched samples.

Group File (`group_file`)

CSV or TSV file with sample IDs in the first column and at least one group-label column.

sample,group
Sample1,Disease
Sample2,Disease
Sample3,Control

Requirements

File extension must be .csv, .tsv, or .txt.
The first column must contain non-missing, unique sample IDs.
At least one group column must be present.
The case_group value must appear in the selected group column.
At least 10 matched samples, 2 case samples, and 2 control samples are required.

Output Files

File	Description
`data/analysis_data.rds`	Matched sample-level analysis dataset used for model fitting
`data/roc_model.rds`	Saved logistic regression model bundle with data and selected genes
`table/model_coefficients.csv`	Logistic regression coefficients, z statistics, p-values, and odds ratios
`table/roc_auc_summary.csv`	AUC values for the full model and each marker
`plot/roc_curve.pdf`	ROC curves for the full model and individual markers
`session_info.txt`	Session information and run parameters

model_coefficients.csv

Column	Description
`term`	Model term name
`estimate`	Logistic regression coefficient
`std_error`	Standard error of the coefficient
`z_value`	Wald z statistic
`p_value`	Wald test p-value
`odds_ratio`	Exponentiated coefficient
`odds_ratio_95_ci`	Odds ratio with 95% confidence interval

roc_auc_summary.csv

Column	Description
`model`	Full model or marker name
`auc`	Area under the ROC curve

Workflow

Step 1: Validate Input

Check that the expression matrix and group file exist and have supported formats.
Validate unique gene identifiers and sample IDs.
Match samples shared by both files.

Step 2: Prepare Analysis Dataset

Keep only the requested marker genes that exist in the expression matrix.
Merge matched expression values with group labels.
Convert the selected case group to binary outcome labels.

Step 3: Fit Logistic Regression

Fit a multivariable logistic regression model using the selected markers.
Extract coefficient estimates, standard errors, p-values, and odds ratios.

Step 4: Compute ROC Performance

Generate the ROC curve of the full logistic model.
Generate ROC curves for each individual marker.
Calculate AUC values for the full model and each marker.

Step 5: Save Outputs

Save the matched analysis dataset and model bundle as .rds files.
Save coefficient and AUC summary tables as .csv files.
Save the combined ROC plot as a PDF.

Examples

Basic Usage

Rscript scripts/main.R \
  -e expression_matrix.csv \
  -g group_info.csv \
  -m FOXP3,CD45,CD3E \
  -c Disease \
  -o ./output/

With Explicit Group Column and Custom Plot

Rscript scripts/main.R \
  -e expression_matrix.csv \
  -g group_info.csv \
  -m FOXP3,CD45,CD3E \
  -c Disease \
  --group_col diagnosis \
  --plot_width 8 \
  --plot_height 6 \
  --plot_title "Biomarker ROC Comparison" \
  --legend_position topright \
  -o ./output/

With Test Data

Rscript scripts/main.R \
  -e tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_group_info.csv \
  -m FOXP3,CD45,CD3E \
  -c Disease \
  -o tests/expected_output/ \
  --overwrite

Error Handling

Error	Cause	Solution
`SKILL_INVALID_PARAMETER`	Missing required argument, invalid option value, invalid matrix/group structure, invalid case label, insufficient case-control counts, or logistic fitting failure	Check argument names, input content, class balance, and model stability
`SKILL_FILE_NOT_FOUND`	Input file does not exist	Verify the file path
`SKILL_EMPTY_DATA`	Input file contains no usable rows, or no requested markers remain after filtering	Check file content, delimiter, and marker names
`SKILL_MISSING_COLUMNS`	Requested group column is absent	Verify `--group_col` and the group file header
`SKILL_SAMPLE_MISMATCH`	Expression matrix and group file do not share sample IDs	Verify that sample IDs match exactly between files
`SKILL_PACKAGE_NOT_FOUND`	Required R package is not installed	Install the missing CRAN package

IF error persists, READ: references/troubleshooting.md

Testing

Smoke Test With Included Data

Rscript scripts/main.R --help

Rscript scripts/main.R \
  -e tests/data/sample_expression_matrix.csv \
  -g tests/data/sample_group_info.csv \
  -m FOXP3,CD45,CD3E \
  -c Disease \
  -o tests/expected_output/ \
  --overwrite

Automated Smoke Test Script

Rscript tests/run_smoke_test.R

Optional shell wrapper:

bash tests/run_smoke_test.sh

Expected Output

tests/expected_output/
|-- data/analysis_data.rds
|-- data/roc_model.rds
|-- plot/roc_curve.pdf
|-- session_info.txt
|-- table/model_coefficients.csv
`-- table/roc_auc_summary.csv

References

Hosmer DW, Lemeshow S, Sturdivant RX (2013). Applied Logistic Regression.
Fawcett T (2006). An Introduction to ROC Analysis. Pattern Recognition Letters.
Robin X et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics.

For detailed algorithm, READ: references/algorithm.md

Implementation Checklist

[x] CLI parsing with optparse
[x] set.seed() for reproducibility
[x] requireNamespace() dependency checks
[x] Session info recording
[x] Timeout parameter exposed as CLI option
[x] File reading instructions in SKILL.md
[x] Modular script structure in scripts/
[x] Test data provided in tests/data/
[x] Error handling with SKILL_* codes
[x] References documented in references/

Last updated: 2026-04-17 | Version: 2.1.0

roc-diagnostic-performance— agent skill

Is roc-diagnostic-performance safe to install?

ROC Diagnostic Performance

When to Use

When Not to Use

When to Read External Files

Usage

Arguments

Input Format

Expression Matrix (`expression_file`)

Group File (`group_file`)

Output Files

model_coefficients.csv

roc_auc_summary.csv

Workflow

Step 1: Validate Input

Step 2: Prepare Analysis Dataset

Step 3: Fit Logistic Regression

Step 4: Compute ROC Performance

Step 5: Save Outputs

Examples

Basic Usage

With Explicit Group Column and Custom Plot

With Test Data

Error Handling

Testing

Smoke Test With Included Data

Automated Smoke Test Script

Expected Output

References

Implementation Checklist

Audit the pieces. Scan the whole. Decide.

Is roc-diagnostic-performance safe to install?

ROC Diagnostic Performance

When to Use

When Not to Use

When to Read External Files

Usage

Arguments

Input Format

Expression Matrix (expression_file)

Group File (group_file)

Output Files

model_coefficients.csv

roc_auc_summary.csv

Workflow

Step 1: Validate Input

Step 2: Prepare Analysis Dataset

Step 3: Fit Logistic Regression

Step 4: Compute ROC Performance

Step 5: Save Outputs

Examples

Basic Usage

With Explicit Group Column and Custom Plot

With Test Data

Error Handling

Testing

Smoke Test With Included Data

Automated Smoke Test Script

Expected Output

References

Implementation Checklist

Audit the pieces. Scan the whole. Decide.

Expression Matrix (`expression_file`)

Group File (`group_file`)