// Analytics synthesis with visualizations from engineering and product data sources
| name | data-analysis |
| description | Analytics synthesis with visualizations from engineering and product data sources |
| version | 0.1.0 |
| author | pmx |
| tags | ["analytics","data","charts","visualization","metrics"] |
| tools | ["dx-data-cloud","jira/linear","github","workiq"] |
| inputs | [{"name":"question","description":"The analysis question to answer (e.g. \"How has PR cycle time changed over the last quarter?\")","required":true},{"name":"data_sources","description":"Array of MCP server names to query for data","required":false,"default":["dx-data-cloud"]},{"name":"format","description":"Output format for the analysis","required":false,"default":"markdown","enum":["markdown","html"]},{"name":"include_charts","description":"Whether to include visual charts in the output","required":false,"default":true}] |
| outputs | [{"name":"analysis","description":"Data analysis with findings, tables, charts (if HTML), interpretation, and recommendations"}] |
Perform analytics synthesis across engineering and product data sources. Answer questions about trends, correlations, comparisons, and distributions by querying MCP servers, analyzing the data, and presenting findings with visualizations.
In pmx-canvas, prefer canvas_add_graph_node for charts and trend lines and
canvas_add_json_render_node when the analysis should land as a richer dashboard or table inside
the canvas.
Analyze the user's question to determine:
If the question is ambiguous, ask clarifying questions before proceeding.
Query the relevant MCP servers:
queryData tool in current session)Primary source for engineering metrics:
Query using SQL against the DX Data Cloud PostgreSQL database. Tool prefixes vary by deployment (dx-data-cloud vs dx-mcp), so select the available DX queryData tool at runtime. Start with information_schema if unsure of available tables and columns.
Issue and sprint data:
Code and collaboration data:
Collaboration and time data:
Based on the analysis type identified in Step 1:
# Analysis: [Question Restated]
**Period**: [time range]
**Data Sources**: [list of sources queried]
**Generated**: [timestamp]
## Key Findings
1. [Finding 1 - the most important insight]
2. [Finding 2]
3. [Finding 3]
## Data
### [Metric Name] Over Time
| Period | Value | Change |
|--------|-------|--------|
| Week 1 | X | - |
| Week 2 | Y | +Z% |
| ... | ... | ... |
### Summary Statistics
- Mean: X
- Median: Y
- Min: Z (period)
- Max: W (period)
- Standard Deviation: V
## Interpretation
[What the data means in plain language. Connect findings to
business context. Explain why the trends matter.]
## Recommendations
1. [Actionable recommendation based on the data]
2. [Another recommendation]
## Methodology
[Brief note on data sources, time ranges, and any caveats
about data quality or completeness]
Generate a self-contained HTML file with:
Include Chart.js via CDN link in the HTML head.
Following the PMX vibe rules, always present data in this order:
Never lead with interpretation. Let the numbers speak first.
Prompt: "How has our PR cycle time changed over the last quarter?"
Expected behavior:
Output includes:
Prompt: "Analyze the correlation between meeting load and developer productivity"
Expected behavior:
Prompt: "Compare velocity across the last 4 sprints"
Expected behavior:
Prompt: "What is the breakdown of issues by type this quarter?"
Expected behavior:
Prompt: "Is there a relationship between our deployment frequency and bug reports?"
Expected behavior:
Prompt: "Give me an overview of engineering health metrics"
Expected behavior:
If unsure which tables or columns are available in DX Data Cloud, start with:
SELECT table_name, column_name, data_type
FROM information_schema.columns
WHERE table_schema = 'public'
ORDER BY table_name, ordinal_position;
This helps discover the available data before writing analysis queries.
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