(<Language>) ## Category <Category name> ## When to Use <1–3 sentence description of what this visualization shows and when a researcher should choose it.> ## Required <Language> Packages - package1 - package2 - ... ## Minimal Reproducible Code ```<language> <Complete, self-contained code that produces the visualization.> ``` ## Key Parameters - `param1`: <brief description of what it controls> - `param2`: <brief description> ## Tips - <Practical tip 1 for better results> - <Practical tip 2> ## Full Tutorial <URL to the rendered tutorial page> ``` --- ## Section-by-Section Guidelines ### 1. Title Line ```markdown # Skill: <Title> (<Language>) ``` - Use the exact tutorial title from the YAML `title:` field - Append the primary language in parentheses: `(R)`, `(Python)`, or `(Julia)` - Example: `# Skill: Volcano Plot (R)` ### 2. Category ```markdown ## Category Distribution ``` Use one of the official Bizard categories: `Distribution`, `Correlation`, `Ranking`, `Composition`, `Proportion`, `DataOverTime`, `Animation`, `Omics`, `Clinics`, `Hiplot`, `Python`, `Julia` For tutorials in the `Python/` or `Julia/` directories, set the category to the visualization type if identifiable (e.g., `Distribution` for a Python Violin Plot), or fall back to `Python` / `Julia`. ### 3. When to Use ```markdown ## When to Use <1–3 sentences> ``` Write a clear, practical description that answers: - What does this visualization display? - What type of data does it work with? - In what research context should one choose this chart? **Good**: "A volcano plot displays statistical significance (−log₁₀ p-value) against fold change (log₂ FC) for differential expression analysis. Use it to identify significantly up- or down-regulated genes after a two-group comparison." **Bad**: "This is a plot." / "Volcano plot for data." ### 4. Required Packages ```markdown ## Required <Language> Packages - ggplot2 - dplyr ``` - List **only** the packages explicitly loaded in the tutorial - Exclude base/standard library modules: - **R**: `base`, `stats`, `utils`, `grDevices`, `graphics`, `methods`, `datasets` - **Python**: `os`, `sys`, `re`, `math`, `json`, `csv`, `collections`, `itertools`, `functools`, `pathlib`, `typing`, `datetime`, `random`, `copy`, `io`, `string`, `warnings`, `abc`, `enum`, `contextlib`, `textwrap`, `operator`, `subprocess`, `tempfile`, `shutil`, `glob`, `time`, `hashlib`, `urllib` - **Julia**: `Base`, `Core`, `Main`, `Printf`, `Random`, `Statistics`, `LinearAlgebra`, `Dates`, `Test`, `Pkg` - Sort alphabetically - Use the package name as imported (e.g., `scikit-learn` → list as `sklearn` if that is how it is imported, or note the pip name) ### 5. Minimal Reproducible Code ````markdown ## Minimal Reproducible Code ```r # Load packages library(ggplot2) # Prepare sample data data <- data.frame( group = rep(c("A", "B"), each = 50), value = c(rnorm(50, 5), rnorm(50, 7)) ) # Create visualization ggplot(data, aes(x = group, y = value, fill = group)) + geom_violin() + theme_minimal() ``` ```` Requirements: - **Self-contained**: The code must run from a clean R/Python/Julia session with only the listed packages installed - **Uses sample data**: Prefer built-in datasets (e.g., `iris`, `mtcars`, `palmerpenguins::penguins`) or generate synthetic data inline. Never reference external files unless absolutely necessary - **Produces output**: The code must produce a visible plot when executed - **Includes comments**: Brief comments explaining each logical step - **Represents the tutorial**: Choose the most representative or visually impressive example from the tutorial, not necessarily the simplest - **Length**: Aim for 10–40 lines; include enough to be useful but not overwhelming ### 6. Key Parameters ```markdown ## Key Parameters - `fill`: Maps a variable to violin fill colors for group comparison - `trim`: If `TRUE`, trims violin tails to the data range - `scale`: Controls violin width; `"area"` (default), `"count"`, or `"width"` ``` - List 3–8 of the most important parameters that control the visualization - Focus on parameters that researchers commonly adjust - Include default values when helpful ### 7. Tips ```markdown ## Tips - Combine with `geom_boxplot(width = 0.1)` inside the violin for summary statistics - Use `coord_flip()` for horizontal violins when group labels are long - Set `alpha < 1` for transparency when overlaying multiple layers ``` - Include 2–5 practical tips - Focus on customization, best practices, and common pitfalls - Draw from the tutorial's advanced sections and beautification steps ### 8. Full Tutorial Link ```markdown ## Full Tutorial https://openbiox.github.io/Bizard/<Category>/<TutorialName>.html ``` Use the canonical URL on the Bizard GitHub Pages site. --- ## Conversion Process (for LLMs) When converting a QMD tutorial to a skill file, follow these steps: 1. **Read the YAML frontmatter** → extract `title` 2. **Identify the language** → scan for `{r}`, `{python}`, or `{julia}` code blocks 3. **Determine the category** → use the parent directory name 4. **Extract the description** → take the first paragraph after the YAML frontmatter (before any `##` heading) 5. **Collect packages** → scan all code blocks for `library()`, `require()`, `import`, `from ... import`, `using` statements 6. **Select the best code example** → prefer figure-producing blocks (`fig-` label), ideally one that is visually appealing and self-contained. If the tutorial has multiple examples, pick the one that best showcases the visualization type 7. **Identify key parameters** → look at `aes()`, `geom_*()`, and theme function arguments that are commonly customized 8. **Extract tips** → look at beautification steps, advanced sections, and notes in the tutorial 9. **Compose the skill** → assemble all sections following the specification above 10. **Validate** → ensure the code block is self-contained and the package list is complete --- ## Quality Checklist Before finalizing a skill file, verify: - [ ] Title matches tutorial and includes language tag - [ ] Category is one of the official categories - [ ] "When to Use" is clear, specific, and 1–3 sentences - [ ] Package list is complete and sorted alphabetically - [ ] Code is self-contained and produces a visible plot - [ ] Code includes brief comments - [ ] Key Parameters section has 3–8 relevant parameters - [ ] Tips section has 2–5 practical tips - [ ] Tutorial URL is correct and uses the canonical format - [ ] No trailing whitespace or formatting inconsistencies --- ## JSON Index Format In addition to individual skill files, maintain two JSON files: ### `skills/index.json` (lightweight) ```json [ { "name": "Violin Plot", "category": "Distribution", "language": "R", "packages": ["ggplot2", "dplyr", "viridis"], "use_when": "Visualize data distribution shape across groups...", "tutorial_url": "https://openbiox.github.io/Bizard/Distribution/ViolinPlot.html", "source_file": "Distribution/ViolinPlot.qmd", "skill_file": "skills/Distribution/ViolinPlot_skill.md" } ] ``` ### `skills/bizard_skills.json` (full) Same structure as `index.json` but with an additional `"content"` field containing the full Markdown skill document.

# Bizard Skill Generation Guide > **Purpose**: This document provides a professional specification for LLM models to > convert Bizard tutorial QMD documents into high-quality AI skill files. Each skill > file serves as a concise, actionable reference that enables LLMs and researchers to > quickly reproduce biomedical visualizations. --- ## What Is a Skill? A **skill** is a structured Markdown document that distills a Bizard tutorial into an LLM-friendly reference card. It captures: - **What** the visualization does and when to use it - **How** to produce it (language, packages, data, code) - **Where** to find the full tutorial for deeper learning Skills are consumed by AI assistants, copilots, and researchers who need a quick, self-contained recipe for a specific visualization technique. --- ## Skill File Specification ### File Naming ``` skills//_skill.md ``` - `` matches the tutorial directory (e.g., `Distribution`, `Omics`, `Hiplot`) - `` matches the QMD filename without extension (e.g., `ViolinPlot`) - Always use `_skill.md` suffix ### Document Structure Every skill file **must** contain exactly these sections in order: ```markdown # Skill: (<Language>) ## Category <Category name> ## When to Use <1–3 sentence description of what this visualization shows and when a researcher should choose it.> ## Required <Language> Packages - package1 - package2 - ... ## Minimal Reproducible Code ```<language> <Complete, self-contained code that produces the visualization.> ``` ## Key Parameters - `param1`: <brief description of what it controls> - `param2`: <brief description> ## Tips - <Practical tip 1 for better results> - <Practical tip 2> ## Full Tutorial <URL to the rendered tutorial page> ``` --- ## Section-by-Section Guidelines ### 1. Title Line ```markdown # Skill: <Title> (<Language>) ``` - Use the exact tutorial title from the YAML `title:` field - Append the primary language in parentheses: `(R)`, `(Python)`, or `(Julia)` - Example: `# Skill: Volcano Plot (R)` ### 2. Category ```markdown ## Category Distribution ``` Use one of the official Bizard categories: `Distribution`, `Correlation`, `Ranking`, `Composition`, `Proportion`, `DataOverTime`, `Animation`, `Omics`, `Clinics`, `Hiplot`, `Python`, `Julia` For tutorials in the `Python/` or `Julia/` directories, set the category to the visualization type if identifiable (e.g., `Distribution` for a Python Violin Plot), or fall back to `Python` / `Julia`. ### 3. When to Use ```markdown ## When to Use <1–3 sentences> ``` Write a clear, practical description that answers: - What does this visualization display? - What type of data does it work with? - In what research context should one choose this chart? **Good**: "A volcano plot displays statistical significance (−log₁₀ p-value) against fold change (log₂ FC) for differential expression analysis. Use it to identify significantly up- or down-regulated genes after a two-group comparison." **Bad**: "This is a plot." / "Volcano plot for data." ### 4. Required Packages ```markdown ## Required <Language> Packages - ggplot2 - dplyr ``` - List **only** the packages explicitly loaded in the tutorial - Exclude base/standard library modules: - **R**: `base`, `stats`, `utils`, `grDevices`, `graphics`, `methods`, `datasets` - **Python**: `os`, `sys`, `re`, `math`, `json`, `csv`, `collections`, `itertools`, `functools`, `pathlib`, `typing`, `datetime`, `random`, `copy`, `io`, `string`, `warnings`, `abc`, `enum`, `contextlib`, `textwrap`, `operator`, `subprocess`, `tempfile`, `shutil`, `glob`, `time`, `hashlib`, `urllib` - **Julia**: `Base`, `Core`, `Main`, `Printf`, `Random`, `Statistics`, `LinearAlgebra`, `Dates`, `Test`, `Pkg` - Sort alphabetically - Use the package name as imported (e.g., `scikit-learn` → list as `sklearn` if that is how it is imported, or note the pip name) ### 5. Minimal Reproducible Code ````markdown ## Minimal Reproducible Code ```r # Load packages library(ggplot2) # Prepare sample data data <- data.frame( group = rep(c("A", "B"), each = 50), value = c(rnorm(50, 5), rnorm(50, 7)) ) # Create visualization ggplot(data, aes(x = group, y = value, fill = group)) + geom_violin() + theme_minimal() ``` ```` Requirements: - **Self-contained**: The code must run from a clean R/Python/Julia session with only the listed packages installed - **Uses sample data**: Prefer built-in datasets (e.g., `iris`, `mtcars`, `palmerpenguins::penguins`) or generate synthetic data inline. Never reference external files unless absolutely necessary - **Produces output**: The code must produce a visible plot when executed - **Includes comments**: Brief comments explaining each logical step - **Represents the tutorial**: Choose the most representative or visually impressive example from the tutorial, not necessarily the simplest - **Length**: Aim for 10–40 lines; include enough to be useful but not overwhelming ### 6. Key Parameters ```markdown ## Key Parameters - `fill`: Maps a variable to violin fill colors for group comparison - `trim`: If `TRUE`, trims violin tails to the data range - `scale`: Controls violin width; `"area"` (default), `"count"`, or `"width"` ``` - List 3–8 of the most important parameters that control the visualization - Focus on parameters that researchers commonly adjust - Include default values when helpful ### 7. Tips ```markdown ## Tips - Combine with `geom_boxplot(width = 0.1)` inside the violin for summary statistics - Use `coord_flip()` for horizontal violins when group labels are long - Set `alpha < 1` for transparency when overlaying multiple layers ``` - Include 2–5 practical tips - Focus on customization, best practices, and common pitfalls - Draw from the tutorial's advanced sections and beautification steps ### 8. Full Tutorial Link ```markdown ## Full Tutorial https://openbiox.github.io/Bizard/<Category>/<TutorialName>.html ``` Use the canonical URL on the Bizard GitHub Pages site. --- ## Conversion Process (for LLMs) When converting a QMD tutorial to a skill file, follow these steps: 1. **Read the YAML frontmatter** → extract `title` 2. **Identify the language** → scan for `{r}`, `{python}`, or `{julia}` code blocks 3. **Determine the category** → use the parent directory name 4. **Extract the description** → take the first paragraph after the YAML frontmatter (before any `##` heading) 5. **Collect packages** → scan all code blocks for `library()`, `require()`, `import`, `from ... import`, `using` statements 6. **Select the best code example** → prefer figure-producing blocks (`fig-` label), ideally one that is visually appealing and self-contained. If the tutorial has multiple examples, pick the one that best showcases the visualization type 7. **Identify key parameters** → look at `aes()`, `geom_*()`, and theme function arguments that are commonly customized 8. **Extract tips** → look at beautification steps, advanced sections, and notes in the tutorial 9. **Compose the skill** → assemble all sections following the specification above 10. **Validate** → ensure the code block is self-contained and the package list is complete --- ## Quality Checklist Before finalizing a skill file, verify: - [ ] Title matches tutorial and includes language tag - [ ] Category is one of the official categories - [ ] "When to Use" is clear, specific, and 1–3 sentences - [ ] Package list is complete and sorted alphabetically - [ ] Code is self-contained and produces a visible plot - [ ] Code includes brief comments - [ ] Key Parameters section has 3–8 relevant parameters - [ ] Tips section has 2–5 practical tips - [ ] Tutorial URL is correct and uses the canonical format - [ ] No trailing whitespace or formatting inconsistencies --- ## JSON Index Format In addition to individual skill files, maintain two JSON files: ### `skills/index.json` (lightweight) ```json [ { "name": "Violin Plot", "category": "Distribution", "language": "R", "packages": ["ggplot2", "dplyr", "viridis"], "use_when": "Visualize data distribution shape across groups...", "tutorial_url": "https://openbiox.github.io/Bizard/Distribution/ViolinPlot.html", "source_file": "Distribution/ViolinPlot.qmd", "skill_file": "skills/Distribution/ViolinPlot_skill.md" } ] ``` ### `skills/bizard_skills.json` (full) Same structure as `index.json` but with an additional `"content"` field containing the full Markdown skill document.