Claude Stats
by bigdra50
|
Skill Details
Repository Files
2 files in this skill directory
name: claude-stats description: | Claude Code使用統計を集計。Usage: /claude-stats [period] [type] period: today|week|month|all | type: summary|full|tools|skills|tokens|hourly|models|web|projects allowed-tools: Bash user-invocable: true
Claude Code Usage Statistics
~/.claude/projects/ 配下の transcript JSONL から統計を集計して表示する。
Usage
/claude-stats [period] [type]
period: today | week | month | all (default)
type: summary (default) | full | tools | skills | subagents | sessions | files | mcp | models | tokens | web | projects | thinking | hourly
Examples
/claude-stats # 全期間サマリー
/claude-stats today tools # 今日のツール別
/claude-stats week skills # 過去7日間のSkill別
/claude-stats week full # 過去7日間の全統計
/claude-stats month tokens # 過去30日間のトークン使用量
/claude-stats week hourly # 過去7日間の時間帯別使用
Execution
引数をパースし、以下を実行:
python3 scripts/aggregate.py --period {period} --type {type}
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.