Generate executive-ready insights with trend analysis and visualizations. Creates professional PowerPoint presentations and Excel data books.
How to install insights?
datarails/dr-claude-code-plugins-re/insights
$npx -y skills add datarails/dr-claude-code-plugins-re --skill insightsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/datarails/dr-claude-code-plugins-re" --skill "datarails/dr-claude-code-plugins-re/insights"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/datarails/dr-claude-code-plugins-re" that are relevant to the current task. Run `npx skills add "https://github.com/datarails/dr-claude-code-plugins-re"` and select the relevant skills, then follow their instructions.
SKILL.md
| 1 | # Insights Agent |
| 2 | |
| 3 | A specialized agent for generating executive-ready trend analysis and business insights with professional visualizations. |
| 4 | |
| 5 | ## Description |
| 6 | |
| 7 | This agent transforms raw financial data into actionable business insights, automatically analyzing trends, calculating key metrics, and generating professional presentations suitable for executive review and board presentations. |
| 8 | |
| 9 | **Purpose**: Executive visibility and decision-making support |
| 10 | |
| 11 | **Audience**: C-suite executives, board members, investors, department heads |
| 12 | |
| 13 | ## Role & Capabilities |
| 14 | |
| 15 | **Role**: Financial analyst and insights generator |
| 16 | |
| 17 | **Key Capabilities**: |
| 18 | - Autonomous trend analysis (MoM, QoQ, YoY growth) |
| 19 | - KPI computation and benchmarking |
| 20 | - Ratio analysis (margins, efficiency metrics, unit economics) |
| 21 | - Anomaly detection in trends |
| 22 | - Business recommendation generation |
| 23 | - Professional presentation creation |
| 24 | - Executive summary generation |
| 25 | |
| 26 | ## When to Use |
| 27 | |
| 28 | Use this agent when you need: |
| 29 | - **Executive briefings** - Board meetings, investor presentations |
| 30 | - **Quarterly business reviews** - Stakeholder visibility |
| 31 | - **Trend analysis** - Understanding business momentum |
| 32 | - **Decision support** - Data-driven decision making |
| 33 | - **Investor communications** - Professional insights for external audiences |
| 34 | - **Management dashboards** - KPI trend tracking |
| 35 | - **Strategic planning** - Historical context and projections |
| 36 | |
| 37 | ## Workflow |
| 38 | |
| 39 | > **Async fetch — aggregations and distinct values run as start → poll.** `start_aggregation_by_id`/`_by_alias` and `start_distinct_values_by_id`/`_by_alias` take the same arguments as the retired blocking calls (dimensions/metrics/filters; table id + field id, or alias + field alias) and return immediately with `{"status": "pending", "handle": {...}}`. Echo that `handle` back verbatim to the matching `get_aggregation_result_by_*` / `get_distinct_values_result_by_*` tool: a `{"status": "running", "retry_after_seconds": N}` response means poll again with the same handle after ~N seconds (≈5s) — it is not an error, and large jobs may take several polls; when ready, the result arrives in the familiar shape (for distinct values, pass `limit` to the result tool). An expired/unknown-handle error means restart with the `start_*` tool. *Transitional fallback:* if the `start_*` tools aren't available on the connector (older server), the blocking twins `get_aggregated_data_by_*` / `get_distinct_values_by_*` still work with the same arguments. |
| 40 | |
| 41 | ### Adaptive Workflow |
| 42 | |
| 43 | 1. **Data Gathering** |
| 44 | - Verify authentication. If a Datarails tool errors with auth, tell |
| 45 | the user to connect via the Connectors UI and stop. |
| 46 | - **Discover the financials table and its fields inline** (see |
| 47 | "Inline Data Discovery" below). This agent is self-contained — no |
| 48 | saved profile or setup step. **If you already discovered the table, |
| 49 | field names, and categories earlier in THIS conversation, reuse |
| 50 | them.** |
| 51 | - Fetch P&L trends (12+ months) via `start_aggregation_by_alias` |
| 52 | (preferred) or `start_aggregation_by_id` grouped by the date and |
| 53 | account dimensions → poll the matching |
| 54 | `get_aggregation_result_by_*(handle)` until ready (async-fetch |
| 55 | pattern). |
| 56 | - Scope every aggregate to the latest complete fiscal year or trailing |
| 57 | 12 closed months — never an unscoped all-time total (financials |
| 58 | tables are multi-year cumulative) — and **label every output with |
| 59 | the period + scenario it covers**. |
| 60 | - Fetch KPI metrics (4+ quarters). Discover named KPIs with |
| 61 | `list_business_metrics`, then compute their values via the same |
| 62 | aggregation tools. Drop any KPI you cannot source (see "Render only |
| 63 | KPIs you can source" under Analysis Components). |
| 64 | |
| 65 | #### Inline Data Discovery |
| 66 | |
| 67 | Every Datarails environment names its financials table and fields |
| 68 | differently, so discover them |
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