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Grok Bot+18 agentsData Story Studio Pro
Create distinctive, traceable data stories with 24 scenario playbooks, 18 visual directions, and English or Chinese outputs.
$9.99
Data Story Studio Pro
Example session with this skill installed
Create a complete English product-conversion story from this fictional January 2026 cohort, observed through 28 February 2026. Use a dark technical art direction and four complementary analytical chapters. Return the complete readable report inline in Markdown.
Metric contract: distinct people from one tracked cohort. Each later stage is a subset of the preceding stage; stage populations overlap and must never be added together.
| Source ID | Stage | Distinct people |
|---|---|---|
| visit | Visited | 12,000 |
| signup | Signed up | 3,600 |
| activate | Activated | 2,100 |
| paid | Paid | 840 |
Show original-cohort shares, adjacent conversion with its own denominator, mutually exclusive dispositions, and the final paid/complement partition. Include formulas and source IDs. Recommend an evidence-review priority without claiming causation or guaranteed improvement. Label it as a fictional local creator demonstration; buyer-Agent/Agensi MCP execution remains unverified.
- Read your context and instructions
- Compiled the data story studio
From first visit to paid use
Finding: 840 of 12,000 people reach paid use:
7% of the original visitor cohort. The largest recorded count difference is between visiting and signing up. This identifies a boundary to investigate, rather than a cause of non-conversion.
Scope: One fictional January 2026 cohort, observed through 28 February 2026. Each successive stage is a subset of the preceding stage. Counts are distinct within each stage; the stages overlap in people.
1. A cohort through the product
The four stages describe one population, not four separate populations. Keep the original 12,000-person denominator visible when asking how much of that cohort reaches each stage.
| Stage | Recorded people | Share of original cohort | Source IDs |
|---|---|---|---|
| Visited | 12,000 | 100% | visit |
| Signed up | 3,600 | 30% | visit, signup |
| Activated | 2,100 | 17.5% | visit, activate |
| Paid | 840 | 7% | visit, paid |
Formula: Stage share = stage count / initial cohort × 100%. The stage counts must not be added: doing so would count some people repeatedly.
2. The conversion at each boundary
Adjacent conversion uses the preceding stage, whereas the first chapter used the original cohort. These are different denominators answering different questions.
| Boundary | Exact ratio | Conversion | Count difference | Source IDs |
|---|---|---|---|---|
| Visited → Signed up | 3,600 / 12,000 | 30% | 8,400 | visit, signup |
| Signed up → Activated | 2,100 / 3,600 | 58.3% | 1,500 | signup, activate |
| Activated → Paid | 840 / 2,100 | 40% | 1,260 | activate, paid |
Formula: Adjacent conversion = next stage / preceding stage × 100%. Displayed rates are rounded after calculation; 2,100 / 3,600 is exactly 7/12, rather than exactly 58.3%. A zero preceding count would make its ratio undefined.
3. Where the original cohort is accounted for
Subtracting successive nested counts produces mutually exclusive dispositions. These can be reconciled to the original population; the overlapping source stages cannot.
| Recorded disposition | People | Original-cohort share | Source IDs |
|---|---|---|---|
| Visited, not recorded as signed up | 8,400 | 70% | visit, signup |
| Signed up, not recorded as activated | 1,500 | 12.5% | signup, activate, visit |
| Activated, not recorded as paid | 1,260 | 10.5% | activate, paid, visit |
| Recorded as paid | 840 | 7% | visit, paid |
Formula: Boundary count = preceding count − next count. Reconciliation: 8,400 + 1,500 + 1,260 + 840 =
12,000. The disposition shares sum to 100%.
The 8,400-person visit-to-signup difference is the largest absolute boundary difference. It does not reveal motives, identify a failed feature or establish that those people will never convert.
4. The final outcome, once
| Outcome at the observation cutoff | People | Original-cohort share | Source IDs |
|---|---|---|---|
| Reached paid use | 840 | 7% | visit, paid |
| Not recorded at the paid stage | 11,160 | 93% | visit, paid |
Formulas: Paid share = 840 / 12,000 × 100% = 7%. Complement = (12,000 − 840) / 12,000 × 100% = 93%. The final partition reconciles to 840 + 11,160 =
12,000.
Next review: Audit event coverage, identity matching and the observation window around Visited → Signed up. Investigate the experience with source records before choosing an intervention. This is a descriptive review priority, not evidence of causation or guaranteed improvement.
Example status: Fictional data; local creator demonstration. Buyer-Agent and Agensi MCP execution have not been independently verified. The deliverable shown here is the complete inline report.
Connects securely to your tools. The creator never sees your data.
About this skill
Give your spreadsheet a story people can follow. Data Story Studio Pro guides your AI Agent from source inspection and metric definitions to a distinctive visual report with traceable findings, precise charts and an editable handoff. Built for client work, team reviews, research communication and public explanations.
What makes it useful
- Evidence before headlines: define population, period, units, comparison and numerator/denominator; distinguish zero, missing and unavailable values.
- Complete analytical stories: choose complementary chapters that answer different questions, with source-linked findings, exact evidence tables, formulas and a useful next decision.
- Distinctive visual authorship: 18 visual-direction guides cover composition, typography, chart treatment, annotation and interaction. Create a new direction when the brief calls for it.
- Coherent edits and exports: keep current findings, marks, tables and translated copy synchronized when source values or filters change.
- English and Chinese: English instructions support English, Chinese or a requested bilingual edition from one consistent data model.
24 scenario playbooks
Growth and product: sales contribution, acquisition efficiency, gross margin, market comparison, activation funnels, cohort retention, survey preferences and customer journeys.
Operations and finance: monthly operating reviews, cash/budget bridges, project calendars, response times, delivery reliability, manufacturing quality and inventory age.
Research and public communication: environmental records, energy use, public-service access, nonprofit participation/outcomes and research sample coverage.
Learning and systems: learning progression, aggregate hiring pipelines, event participation, and qualitative knowledge/process explanations. Combine or extend these playbooks for a new brief.
Design choice
Use visual guides such as Swiss Index, Night Observatory, Editorial Broadsheet, Field Atlas, Bauhaus Blocks, Calibration Plate or Evidence Folio—or develop an original direction. Carry its typography and structure through the analytical chapters and evidence, with purposeful responsive behavior.
Requested outputs
Interactive HTML, editable SVG, PNG sharing graphics, PDF reports and companion CSV/JSON can be authored when your Agent environment supports the format. Inputs can include CSV, XLSX, JSON, verified tables and source material. XLSX requires an available spreadsheet reader; the optional inspection helper uses openpyxl. No paid provider, seller backend or creator demonstration runtime is required.
What the examples represent
The six gallery images are actual local creator outputs using fictional data. Our separate bilingual showcase contains 24 case families and 84 complementary analytical chapters. The purchase provides reusable instructions and reference guidance; the scenario/style catalogs are not a bundle of prebuilt reports, datasets, fonts or demonstration assets.
Execution and delivery
Skill access follows Agensi’s agent connection. Generated reports and their original editable sources are separate from protected Skill instructions. Buyer execution and Agensi MCP multi-file delivery have not been verified; outputs depend on the Agent’s available tools. Local HTML reopening was checked through HTTP. The demonstrated PDFs are raster reports with non-searchable text; retain SVG and data for editing. Every real assignment needs source and rendered-output verification.
Foundation provenance
The MIT-0 chart-generator and data-analyzer instruction files were starting inspiration. Narrative methods, metric contracts, scenario guidance, art direction and acceptance guidance here are newly authored and do not require those Skills or copy their executable templates.
Version 0.3.0.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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