Matrix List
by ojowwalker77
This skill should be used when the user asks to "list matrix solutions", "show matrix stats", "display memory contents", "view matrix status", "show failures", "list warnings", or needs to see Matrix memory statistics.
Skill Details
Repository Files
1 file in this skill directory
name: Matrix List description: This skill should be used when the user asks to "list matrix solutions", "show matrix stats", "display memory contents", "view matrix status", "show failures", "list warnings", or needs to see Matrix memory statistics. user-invocable: true agent: haiku allowed-tools:
- mcp__plugin_matrix_matrix__matrix_status
- mcp__plugin_matrix_matrix__matrix_warn
Matrix List
Display Matrix memory contents and statistics.
Use the matrix_status MCP tool to retrieve comprehensive information and present it to the user.
Default View (no arguments)
Display:
- Statistics: Total solutions, failures recorded, warnings count
- By Scope: Solutions breakdown (global/stack/repo)
- Recent Activity: Last 5 solutions
- Top Performers: Solutions with highest success rates
- Database: Size and health status
Argument-Based Filtering
Parse user arguments from the skill invocation. When Claude Code loads this skill, the user's additional text after the trigger phrase is available for parsing.
Based on user input, adjust the listing:
- Contains "stats" or "statistics" - focus on statistics only
- Contains "failure" - focus on recorded failures
- Contains "warn" - use
matrix_warntool with action "list" to show warnings - Contains "solutions" - show detailed solutions list
- Otherwise - show comprehensive overview
Format the output as a clear, organized summary that helps the user understand their accumulated knowledge.
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.
