List
by lis186
List saved SourceAtlas analysis results
Skill Details
Repository Files
1 file in this skill directory
name: list description: List saved SourceAtlas analysis results model: haiku allowed-tools: Bash
SourceAtlas: List Saved Results
Your Task
List all saved analysis results in the .sourceatlas/ directory.
Step 1: Check directory exists
ls -la .sourceatlas/ 2>/dev/null || echo "NOT_FOUND"
If output contains NOT_FOUND or directory is empty:
📁 No saved analyses yet
Use the `--save` parameter to save analysis results:
- `/sourceatlas:overview --save`
- `/sourceatlas:pattern "api" --save`
- `/sourceatlas:history --save`
End.
Step 2: List all files with details
find .sourceatlas -type f -exec ls -lh {} \; 2>/dev/null | sort
Step 3: Format output
Format results into a table, calculate days since modification, and mark expired status (>30 days):
📁 .sourceatlas/ saved analyses:
| Type | File | Size | Modified | Status |
|------|------|------|----------|--------|
| overview | overview.yaml | 2.3 KB | 3 days ago | ✅ |
| pattern | patterns/api.md | 1.5 KB | 45 days ago | ⚠️ |
| pattern | patterns/repository.md | 2.1 KB | 5 days ago | ✅ |
| history | history.md | 4.2 KB | 60 days ago | ⚠️ |
| flow | flows/checkout.md | 3.1 KB | 2 days ago | ✅ |
| impact | impact/user-model.md | 1.8 KB | 4 days ago | ✅ |
| deps | deps/react.md | 2.5 KB | 6 days ago | ✅ |
📊 Stats: 7 cached, 2 expired (>30 days)
💡 Tips:
- Clear cache: `/sourceatlas:clear`
- Clear specific type: `/sourceatlas:clear patterns`
Step 4: List expired items with refresh commands
If there are expired items (>30 days), output copyable re-analysis commands:
⚠️ Expired items (recommend re-analysis):
| File | Days | Re-analyze Command |
|------|------|-------------------|
| patterns/api.md | 45 days | `/sourceatlas:pattern "api" --force --save` |
| history.md | 60 days | `/sourceatlas:history --force --save` |
💡 Copy the commands above to re-analyze
Command generation rules:
| Type | Command Format |
|---|---|
| overview | /sourceatlas:overview --force --save |
| overview-{dir} | /sourceatlas:overview {dir} --force --save |
| patterns/{name}.md | /sourceatlas:pattern "{name}" --force --save |
| history.md | /sourceatlas:history --force --save |
| flows/{name}.md | /sourceatlas:flow "{name}" --force --save |
| impact/{name}.md | /sourceatlas:impact "{name}" --force --save |
| deps/{name}.md | /sourceatlas:deps "{name}" --force --save |
Note: Convert - in filenames back to spaces for parameters (e.g., api-endpoint.md → "api endpoint")
Type Detection Rules
| File Path | Type |
|---|---|
overview.yaml or overview-*.yaml |
overview |
patterns/*.md |
pattern |
flows/*.md |
flow |
history.md |
history |
impact/*.md |
impact |
deps/*.md |
deps |
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.
