Save Play Library
by gtmagents
Use to catalog churn/expansion plays tied to specific signals, cohorts,
Skill Details
Repository Files
1 file in this skill directory
name: save-play-library description: Use to catalog churn/expansion plays tied to specific signals, cohorts, and owners.
Save Play Library Skill
When to Use
- Translating retention signals into specific remediation actions.
- Coordinating lifecycle marketing and CS teams on who does what.
- Measuring effectiveness of save plays over time.
Framework
- Signal Definition – map triggers (drop in usage, support surge, NPS drop) to play categories.
- Play Blueprint – outline objective, audience, messaging, channels, and required assets.
- Owner Matrix – assign owners (CSM, lifecycle marketer, exec sponsor) with SLAs.
- Measurement Plan – specify KPIs, experiment windows, and success criteria.
- Feedback Loop – capture results, learnings, and iterate on play details.
Templates
- Play card (trigger, action steps, assets, KPI, owner).
- Experiment tracker with control/test cohorts.
- Post-play retrospective form.
Tips
- Keep plays modular so they can stack (email + exec outreach + offer).
- Include links to enablement assets for quick execution.
- Pair with
retention-dashboardto trigger plays automatically when thresholds trip.
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.