Analytics Reporting
by pluginagentmarketplace
DevRel metrics, analytics dashboards, and program reporting
Skill Details
Repository Files
4 files in this skill directory
name: analytics-reporting description: DevRel metrics, analytics dashboards, and program reporting sasmp_version: "1.4.0" version: "2.0.0" updated: "2025-01" bonded_agent: 07-metrics-analyst bond_type: PRIMARY_BOND
DevRel Analytics & Reporting
Measure program impact with data-driven metrics and reporting.
Skill Contract
Parameters
parameters:
required:
- report_type: enum[pulse, monthly, quarterly, annual]
- metrics_focus: array[string]
optional:
- date_range: object{start, end}
- comparison_period: enum[wow, mom, yoy]
Output
output:
report:
summary: object
visualizations: array[Chart]
insights: array[string]
recommendations: array[Action]
Key Metrics Framework
AAARRRP Model for DevRel
| Stage | Metrics |
|---|---|
| Awareness | Impressions, reach, brand mentions |
| Acquisition | Signups, registrations, first visits |
| Activation | First API call, tutorial completion |
| Retention | MAU, DAU, repeat usage |
| Revenue | Conversions, upgrades, pipeline |
| Referral | NPS, word-of-mouth, shares |
| Product | Feature adoption, feedback quality |
Metrics by Category
Community Metrics
growth:
- total_members
- new_members_per_week
- member_retention_30d
engagement:
- daily_active_users
- messages_per_day
- questions_answered
- response_time_avg
Content Metrics
reach:
- page_views
- unique_visitors
- social_impressions
engagement:
- time_on_page
- scroll_depth
- shares_and_saves
conversion:
- cta_clicks
- signups_from_content
- doc_to_api_calls
Event Metrics
attendance:
- registrations
- show_up_rate
- session_attendance
satisfaction:
- nps_score
- session_ratings
- feedback_sentiment
business:
- leads_generated
- pipeline_influenced
Dashboard Structure
Executive Dashboard
├── North Star Metric (primary KPI)
├── Funnel Overview
├── Weekly Trends
└── Key Highlights
Detailed Dashboards
├── Community Health
├── Content Performance
├── Event Analysis
└── Developer Journey
Reporting Cadence
| Report | Frequency | Audience |
|---|---|---|
| Weekly pulse | Weekly | DevRel team |
| Monthly review | Monthly | Leadership |
| Quarterly OKR | Quarterly | Executives |
| Annual summary | Yearly | Company-wide |
Attribution Challenges
Common issues:
- Multi-touch attribution
- Long sales cycles
- Indirect influence
- Data silos
Solutions:
- UTM parameters
- Developer surveys
- CRM integration
- Cohort analysis
Retry Logic
retry_patterns:
data_incomplete:
strategy: "Extend collection window"
fallback: "Use available data with disclaimer"
dashboard_error:
strategy: "Refresh data sources"
fallback: "Manual data pull"
metric_anomaly:
strategy: "Verify data integrity"
fallback: "Flag for review"
Failure Modes & Recovery
| Failure Mode | Detection | Recovery |
|---|---|---|
| Missing data | Gaps in metrics | Backfill or document |
| Wrong calculations | Audit reveals errors | Fix formula, rerun |
| Outdated dashboard | Stale data shown | Refresh pipeline |
Debug Checklist
□ Data sources connected?
□ Date ranges correct?
□ Calculations verified?
□ Comparison periods aligned?
□ Visualizations rendering?
□ Insights actionable?
Test Template
test_analytics_reporting:
unit_tests:
- test_data_accuracy:
assert: "Matches source systems"
- test_calculations:
assert: "Formulas correct"
integration_tests:
- test_dashboard_load:
assert: "<5s load time"
Observability
metrics:
- reports_generated: integer
- dashboard_views: integer
- data_freshness: duration
- insight_accuracy: float
See assets/ for dashboard templates.
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.
