Report Generator
by u9401066
|
Skill Details
Repository Files
1 file in this skill directory
name: report-generator description: | Generate structured reports from collected data. LOAD THIS SKILL WHEN: User says "寫報告", "產出報告", "撰寫報告", "generate report" | after research/analysis | need structured documentation. CAPABILITIES: Research/Technical/Project reports, Markdown format, auto date stamps, structured sections (abstract, methods, results, discussion).
報告產出技能
描述
根據收集的資料生成專業的結構化報告。
觸發條件
- 「產出報告」
- 「撰寫 XXX 報告」
- 「整理成報告」
- Workflow 中的報告生成步驟
報告類型
1. 研究報告
# [標題]
## 摘要
[簡短摘要 200-300 字]
## 背景
[研究背景與動機]
## 方法
[研究方法說明]
## 結果
[研究發現]
## 討論
[分析與討論]
## 結論
[總結與建議]
## 參考資料
[引用文獻列表]
2. 技術報告
# [技術報告標題]
## 概述
[技術概述]
## 技術規格
[詳細規格]
## 實作細節
[實作說明]
## 測試結果
[測試數據]
## 建議
[改進建議]
3. 專案報告
# [專案名稱] 進度報告
## 專案狀態
[整體狀態摘要]
## 完成項目
[✅ 已完成列表]
## 進行中
[🔄 進行中列表]
## 待辦事項
[📋 待辦列表]
## 風險評估
[⚠️ 風險項目]
## 下一步行動
[下一階段計畫]
輸出格式
- Markdown 格式
- 支援匯出 PDF (需額外工具)
- 自動加入日期時間戳記
使用範例
「根據剛才的搜尋結果產出報告」
「撰寫專案進度報告」
「整理成技術評估報告」
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.