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Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
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Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/aipoch/medical-research-skills --skill forest-plot-styler명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SOC 직업 분류 기준
SKILL.md 표시 중
| name | forest-plot-styler |
| description | Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation. |
| license | MIT |
| author | AIPOCH |
ID: 157
Beautifies Meta-analysis or subgroup analysis forest plots, customizes Odds Ratio point sizes and confidence interval line styles.
See ## Features above for related details.
forest-plot-styler using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.scripts/main.py.references/ for task-specific guidance.See ## Usage above for related details.
cd "20260318/scientific-skills/Data Analytics/forest-plot-styler"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/main.py with the validated inputs.See ## Workflow above for related details.
scripts/main.py.references/ contains supporting rules, prompts, or checklists.Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json
python -m py_compile scripts/main.py
# Example invocation: python scripts/main.py --input <data.csv> [options]
| Parameter | Type | Default | Required | Description |
|---|---|---|---|---|
--input, -i | string | - | Yes | Input data file (CSV or Excel) |
--output, -o | string | forest_plot.png | No | Output file path |
--format, -f | string | png | No | Output format (png/pdf/svg) |
--point-size | int | 8 | No | OR point size |
--point-color | string | #2E86AB | No | OR point color |
--ci-color | string | #2E86AB | No | Confidence interval line color |
--ci-linewidth | int | 2 | No | Confidence interval line thickness |
--ci-capwidth | int | 5 | No | Confidence interval endpoint width |
--summary-color | string | #A23B72 | No | Pooled effect point color |
--summary-shape | string | diamond | No | Pooled effect point shape |
--subgroup | string | - | No | Subgroup analysis column name |
--title, -t | string | Forest Plot | No | Chart title |
--xlabel, -x | string | Odds Ratio (95% CI) | No | X-axis label |
--reference-line | float | 1.0 | No | Reference line position |
--width, -W | int | 12 | No | Image width (inches) |
--height, -H | int | auto | No | Image height (inches) |
--dpi | int | 300 | No | Image resolution |
--font-size | int | 10 | No | Font size |
--style, -s | string | default | No | Preset style (default/minimal/dark) |
CSV/Excel files must contain the following columns:
| Column Name | Description | Type |
|---|---|---|
study | Study name | Text |
or | Odds Ratio value | Numeric |
ci_lower | Confidence interval lower bound | Numeric |
ci_upper | Confidence interval upper bound | Numeric |
weight | Weight (optional, for point size) | Numeric |
subgroup | Subgroup label (optional) | Text |
study,or,ci_lower,ci_upper,weight,subgroup
Study A,0.85,0.65,1.12,15.2,Drug A
Study B,0.72,0.55,0.94,18.5,Drug A
Study C,1.15,0.88,1.50,12.3,Drug B
Study D,0.95,0.75,1.20,14.8,Drug B
python scripts/main.py -i meta_data.csv
python scripts/main.py -i meta_data.csv \
--point-color="#E63946" \
--ci-color="#457B9D" \
--point-size=10 \
--ci-linewidth=3 \
-t "Meta-Analysis of Treatment Effects"
python scripts/main.py -i meta_data.csv \
--subgroup subgroup_column \
--summary-color="#F4A261" \
-o subgroup_forest.png
python scripts/main.py -i meta_data.csv \
-f pdf \
-o forest_plot.pdf
Generated forest plot contains:
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
# Python dependencies
pip install -r requirements.txt
Every final response should make these items explicit when they are relevant:
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.This skill accepts requests that match the documented purpose of forest-plot-styler and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
forest-plot-styleronly handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Use the following fixed structure for non-trivial requests:
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.