Readability Report
by warmeaf
生成可读性报告。当用户请求为项目源代码生成可读性报告时,使用此功能。输出名为 readability-report.html 的单页 HTML 文件作为可读性报告,该报告集成了 Node.js 脚本和 HTML 模板。
Skill Details
Repository Files
5 files in this skill directory
name: readability-report description: 生成可读性报告。当用户请求为项目源代码生成可读性报告时,使用此功能。输出名为 readability-report.html 的单页 HTML 文件作为可读性报告,该报告集成了 Node.js 脚本和 HTML 模板。 license: Complete terms in LICENSE.txt
报告生成步骤
-
确定路径和输出目录
- 确定源码文件夹路径(通常为
src/) - 确定报告输出文件夹:默认为项目根目录下的
.cqm/readability/ - 如果输出文件夹不存在,则创建该目录
- 确定源码文件夹路径(通常为
-
初始化文件结构数据
- 执行脚本:
node .claude/skills/readability-report/script/generate-file-structure.js <源码文件夹路径> .cqm/readability/readability.json - 脚本会在输出文件夹(
.cqm/readability/)下生成readability.json文件 - 该 JSON 文件包含源码目录结构,每个代码文件的
readability字段初始值为0
- 执行脚本:
-
评估代码可读性
- 读取
.claude/skills/readability-report/code-readability.md作为可读性评估标准 - 读取
.cqm/readability/readability.json获取所有代码文件列表 - 对于 JSON 中的每个代码文件:
- 读取文件内容
- 根据
code-readability.md的标准,运用 AI 能力评估其可读性 - 将评估结果(0-100 的分数)更新到该文件对应的
readability字段
- 将更新后的数据写回
readability.json
- 读取
-
生成 HTML 报告
- 读取模板文件:
.claude/skills/readability-report/template/readability-report-template.html - 读取最终的
readability.json数据 - 在模板文件中找到
rawData变量(通常在 JavaScript 代码中) - 将
readability.json的内容赋值给rawData变量 - 将更新后的模板内容保存为
.cqm/readability/readability-report.html
- 读取模板文件:
-
完成
- 最终报告文件位于:
.cqm/readability/readability-report.html
- 最终报告文件位于:
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
