Menu
Guided requirements template for data storytelling projects -- walks users through audience, Big Idea, questions, data sources, and delivery target before any chart is created
| type | skill |
| lifecycle | stable |
| inheritance | inheritable |
| name | storytelling-requirements |
| description | Guided requirements template for data storytelling projects -- walks users through audience, Big Idea, questions, data sources, and delivery target before any chart is created |
| tier | standard |
| applyTo | **/*story*,**/*requirements*,**/*brief*,**/*dashboard*,**/*report* |
| currency | "2026-05-02T00:00:00.000Z" |
| lastReviewed | "2026-05-02T00:00:00.000Z" |
Every data storytelling project starts with a brief. This plugin provides the template and guides the user through it. No chart should be created before this document is filled out.
The brief is the contract between the analyst and the audience. It answers: who is reading this, what decision does it support, and what does the data need to show?
visual-vocabulary, data-visualization, or any delivery pluginCopy this template to your project and fill it out with the user. Each section maps to a step in the storytelling pipeline.
| Field | Answer |
|---|---|
| Primary audience | (e.g., "CFO and finance leadership team") |
| Audience expertise | Executive / Manager / Analyst / General |
| Time budget | 30 seconds / 2 minutes / Unlimited |
| Decision this supports | (e.g., "Whether to increase Q4 marketing spend") |
| How they will consume it | Screen / Print / Presentation / Email / Mobile |
Write one sentence that captures the argument:
[Audience] should [action/decision] because [evidence from data].
Example: "The exec team should double Q4 email marketing spend because every $1 generates $3.20 in pipeline."
If you cannot write this sentence, the analysis is not done yet. Go back to exploration.
List 3-5 questions, ranked by priority. Each question becomes one visual.
| # | Question | Communication Goal | Priority |
|---|---|---|---|
| 1 | (e.g., "How has revenue trended quarterly?") | Change Over Time | Must-have |
| 2 | (e.g., "Which regions contribute most?") | Comparison | Must-have |
| 3 | (e.g., "What share does each product line hold?") | Proportion | Should-have |
| 4 | |||
| 5 |
Communication goals map to chart types via visual-vocabulary Module 1.
| Source | Format | Location | Refresh Cadence | Notes |
|---|---|---|---|---|
| (e.g., "Sales CRM export") | CSV | SharePoint folder | Monthly | Filter: last 8 quarters |
| Concern | Impact | Mitigation |
|---|---|---|
| (e.g., "Missing region codes for 12% of records") | Proportions will undercount | Map nulls to "Unknown" region |
| Field | Answer |
|---|---|
| Output format | SVG in Markdown / HTML dashboard / Power BI report / Slides / Email |
| Branding | Dark slate / Light / Custom palette |
| Interactivity | Static / Filters / Drill-through / Cross-filter |
| Hosting | GitHub README / SharePoint / Power BI Service / Local file |
| Update frequency | One-time / Weekly refresh / Real-time |
| Constraint | Detail |
|---|---|
| 5-visual rule | Executive audience: max 5 visuals per page (3 KPIs + hero + supporting) |
| Accessibility | WCAG 2.1 AA: 4.5:1 contrast, no color-only encoding |
| Token budget | (if rendering in AI context: max tokens for the output artifact) |
| Deadline | (when is this needed?) |
When a user says "build me a dashboard" or "make a report from this data", walk them through the template:
Start with Section 2 (Big Idea). If they can write the sentence, the rest flows. If they cannot, they need exploration first -- invoke data-analysis or data-storytelling Module 1 (Discover).
Fill Section 1 (Audience). This determines the 5-visual rule budget, chart complexity, and delivery format.
Fill Section 3 (Questions). Map each question to a communication goal using the heuristic table below. This is where visual-vocabulary fires.
Fill Sections 4-5 (Data). This determines which datasource-connectors and data-preparation steps are needed.
Fill Sections 6-7 (Delivery). This determines which delivery-* plugin to invoke.
When users struggle to name the communication goal for a question, use these trigger words:
| Trigger Words in the Question | Communication Goal |
|---|---|
| "rank", "compare", "versus", "top", "best/worst" | Comparison |
| "over time", "trend", "growth", "decline", "monthly" | Change Over Time |
| "share", "proportion", "breakdown", "percent of" | Proportion |
| "spread", "range", "outlier", "distribution" | Distribution |
| "correlation", "relationship", "predict", "affects" | Relationship |
| "flow", "path", "conversion", "funnel", "from X to Y" | Flow |
| "deviation", "variance", "above/below target" | Deviation |
After filling the brief and before building, run one CSAR pass:
storytelling-requirements (this plugin -- the brief)
|
v
datasource-connectors (ingest data per Section 4)
|
v
data-preparation (clean per Section 5 concerns)
|
v
visual-vocabulary (select charts per Section 3 goals)
|
v
delivery-* (render per Section 6 target)
| Anti-pattern | Fix |
|---|---|
| Building charts before writing the Big Idea | The Big Idea is the filter. Without it, you build 20 charts and keep 5. |
| Skipping the audience section | Executive dashboards and analyst reports are different artifacts. The audience determines everything. |
| Listing 10+ questions for an executive dashboard | 5-visual rule. Cut to 3-5 questions or split into multiple pages. |
| No data quality section | Surprises in the data surface during the demo, not the build. Document concerns early. |
| Filling the template alone (without the stakeholder) | The brief is a conversation tool. Fill it together. |
visual-vocabulary -- chart selection by communication goal (fires after Section 3)data-storytelling -- narrative arc and orchestration (fires after the brief is complete)data-visualization -- chart design: color, annotation, decluttering (fires after chart selection)data-preparation -- cleaning and profiling (fires after Section 4-5)datasource-connectors -- data ingestion (fires after Section 4)delivery-* -- rendering to target format (fires after Section 6)Bundle plugin: installs the complete Visual Storytelling pipeline (brief, ingest, clean, select, deliver). See component SKILLs for detailed specs.
Data cleaning, profiling, transformation, and quality gates -- prepares raw data for visualization and analysis
Ingestion patterns for CSV, JSON, REST API, SQL, Excel, and Parquet -- guides an LLM through loading data from any common source
Render data dashboards as pure ASCII art in monospace text -- the cheapest, most portable delivery method. No rendering engine, no SVG, no browser. LLM-native output with predictable character geometry.
Render data dashboards as self-contained HTML files using Apache ECharts v6. Single file, zero build step, interactive charts with tooltips and data zoom. Declarative JSON option config optimized for AI generation.
Render data dashboards as static SVG panels embeddable in Markdown. Uses D3.js v7 mental model for scales, shapes, and axes. No runtime JS; output is pure SVG with inline styles for GitHub compatibility.