Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Use when constructing a prognosis nomogram from survival-related clinical predictors, exporting the nomogram bundle and C-index table, and optionally rendering the final nomogram PDF. NOT for: univariate/multivariable Cox feature screening, calibration curves, ROC analysis, decision-curve analysis, or non-survival outcomes.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill when you need to:
Typical user requests:
Do not use this skill for:
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md | Cox-based nomogram workflow, C-index, and assumptions |
| Need to run analysis | scripts/main.R | Execute Rscript scripts/main.R --mode build ... or --mode plot ... |
| 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 clinical CSV for smoke testing |
This skill accepts:
.qs bundle (plot mode)If the user's request does not involve nomogram construction from survival data — for example, asking to screen features with Cox regression, generate calibration curves, perform ROC analysis, or analyze non-survival binary outcomes — do not proceed with this workflow. Instead respond:
"nomogram-construction is designed to build a prognosis nomogram from pre-selected survival predictors and export the nomogram bundle with C-index table. Your request appears to be outside this scope. Please use a Cox feature-screening skill for variable selection, or a calibration-curve/ROC skill for model validation."
R packages required: rms, openxlsx, qs, optparse.
Install with:
install.packages(c("rms", "openxlsx", "qs", "optparse"), repos = "https://cloud.r-project.org")
Or run the bootstrap installer:
Rscript scripts/install_dependencies.R
Note:
--helprequiresoptparseto be loaded. If the package check fires before option parsing, installoptparsefirst, then run--help.
Rscript scripts/main.R \
--mode build \
--data_file ./clinical_data.csv \
--features age,stage,risk \
--time_col futime \
--event_col fustat \
--years 1,2,3 \
--output_dir ./output/ \
--seed 42
Rscript scripts/main.R \
--mode plot \
--nomo_data_file ./output/data/Nomogram_list.qs \
--plot_save ./output/plot/nomogram_plot.pdf
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-m | --mode | character | build | Execution mode: build or plot |
-d | --data_file | character | required for build | Clinical CSV file with sample IDs as row names |
-f | --features | character | required for build | Comma-separated prognostic features |
-t | --time_col | character | futime | Survival time column |
-e | --event_col | character | fustat | Event column encoded as 1=event, 0=censored |
-y | --years | character | 1,2,3 | Prediction time points in years |
-o | --output_dir | character | ./output/ | Output directory for build mode |
--overwrite | flag | FALSE | Allow writing into a non-empty output directory | |
-n | --nomo_data_file | character | required for plot | Nomogram bundle in .qs format |
-p | --plot_save | character | required for plot | Output PDF path |
-w | --plot_width | double | 11 | Plot width in inches |
-H | --plot_height | double | 8 | Plot height in inches |
-F | --font_size | double | 8 | Plot font size |
-l | --line_width | double | 5 | Plot line width |
--font_family | character | sans | Font family for PDF output | |
-s | --seed | integer | 42 | Random seed for reproducibility |
-T | --timeout_seconds | integer | 0 | Elapsed time limit in seconds; 0 disables timeout |
data_file)CSV file with sample IDs as row names and one column per feature/end-point variable.
",age,stage,risk,futime,fustat
SAMPLE_001,65,StageIII,high,365,1
SAMPLE_002,52,StageII,low,730,0
SAMPLE_003,78,StageIV,high,180,1
Requirements
time_col must contain finite numeric values greater than 0.event_col must contain only 0 and 1.nomo_data_file)The plot mode reads the .qs bundle produced by build mode.
Required bundle objects:
nomogramc_indexmodeldatafeaturestime_points| File | Description |
|---|---|
data/Nomogram_list.qs | Serialized nomogram bundle |
data/analysis_data.rds | Complete-case dataset used for modeling |
table/nomogram_c_index.xlsx | Nomogram discrimination summary |
session_info.txt | Session information and build parameters |
| File | Description |
|---|---|
plot/nomogram_plot.pdf | Rendered nomogram PDF |
plot/session_info.txt | Plotting session information and parameters |
nomogram_c_index.xlsx| Column | Description |
|---|---|
metric | Reported metric name |
value | Metric value |
rms::cph survival model.Rscript scripts/main.R \
--mode build \
-d clinical_data.csv \
-f age,stage,risk \
-o ./output/
Rscript scripts/main.R \
--mode build \
-d clinical_data.csv \
-f age,stage,risk,treatment \
-y 1,3,5 \
-o ./output/
Rscript scripts/main.R \
--mode plot \
-n ./output/data/Nomogram_list.qs \
-p ./output/plot/nomogram_plot.pdf \
-w 12 -H 9 -F 10
| Error | Cause | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND | Input file does not exist | Verify the file path |
SKILL_EMPTY_DATA | Input file is empty or has no usable rows and columns | Re-export the input file with valid rows and columns |
SKILL_MISSING_COLUMNS | Required columns are absent from the clinical data | Check column names and spelling |
SKILL_INVALID_DATA | Invalid time/event encoding, malformed bundle, or unreadable CSV/QS file | Check input values and file integrity |
SKILL_INSUFFICIENT_DATA | Too few features, complete samples, or events | Provide more valid predictors or samples |
SKILL_ANALYSIS_ERROR | cph() fitting, nomogram construction, or output writing failed | Check data quality, factor levels, and event distribution |
SKILL_INVALID_PARAMETER | Missing required CLI value, invalid mode, invalid years, or overwrite conflict | Review command-line arguments |
SKILL_PACKAGE_NOT_FOUND | Required R package is not installed | Install with: Rscript -e "install.packages(c('rms', 'openxlsx', 'qs'), repos='https://cloud.r-project.org')" |
SKILL_TIMEOUT | The configured timeout was exceeded | Increase --timeout_seconds or reduce workload |
IF error persists, READ: references/troubleshooting.md
Rscript scripts/main.R --help
Rscript scripts/main.R \
--mode build \
-d tests/data/yuhou_cli_data.csv \
-f age,gender,risk \
-o tests/expected_output/ \
--overwrite
Rscript scripts/main.R \
--mode plot \
-n tests/expected_output/data/Nomogram_list.qs \
-p tests/expected_output/plot/nomogram_plot.pdf
Rscript tests/run_smoke_test.R
Optional shell wrapper:
bash tests/run_smoke_test.sh
tests/expected_output/
|-- data/analysis_data.rds
|-- data/Nomogram_list.qs
|-- plot/nomogram_plot.pdf
|-- plot/session_info.txt
|-- session_info.txt
`-- table/nomogram_c_index.xlsx
Historical development artifacts may still exist in tests/output/, but standardized validation uses tests/expected_output/.
For detailed algorithm, READ: references/algorithm.md
optparseset.seed() for reproducibilityrequireNamespace() dependency checksSKILL.mdscripts/tests/data/SKILL_* codesreferences/Last updated: 2026-04-27 | Version: 2.1.0
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
日本語の概要は準備中です。原文の説明を表示しています。
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
日本語の概要は準備中です。原文の説明を表示しています。
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
日本語の概要は準備中です。原文の説明を表示しています。
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
日本語の概要は準備中です。原文の説明を表示しています。