Data Storytelling workflow skill. Use this skill when the user needs Transform raw data into compelling narratives that drive decisions and inspire action and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Data Storytelling workflow skill. Use this skill when the user needs Transform raw data into compelling narratives that drive decisions and inspire action and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/data-storytelling from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.
Data Storytelling Transform raw data into compelling narratives that drive decisions and inspire action.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Story Frameworks, The Hook, The Context, The Insight, The Solution.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
The task is unrelated to data storytelling
You need a different domain or tool outside this scope
Presenting analytics to executives
Creating quarterly business reviews
Building investor presentations
Writing data-driven reports
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Imported Workflow Notes
Imported: Instructions
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
Imported: Core Concepts
1. Story Structure
Setup → Conflict → Resolution
Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations
2. Narrative Arc
1. Hook: Grab attention with surprising insight
2. Context: Establish the baseline
3. Rising Action: Build through data points
4. Climax: The key insight
5. Resolution: Recommendations
6. Call to Action: Next steps
3. Three Pillars
Pillar
Purpose
Components
Data
Evidence
Numbers, trends, comparisons
Narrative
Meaning
Context, causation, implications
Visuals
Clarity
Charts, diagrams, highlights
Examples
Example 1: Ask for the upstream workflow directly
Use @data-storytelling to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @data-storytelling against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @data-storytelling for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @data-storytelling using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Start with the "so what" - Lead with insight
Use the rule of three - Three points, three comparisons
Show, don't tell - Let data speak
Make it personal - Connect to audience goals
End with action - Clear next steps
Don't data dump - Curate ruthlessly
Don't bury the insight - Front-load key findings
Imported Operating Notes
Imported: Best Practices
Do's
Start with the "so what" - Lead with insight
Use the rule of three - Three points, three comparisons
Show, don't tell - Let data speak
Make it personal - Connect to audience goals
End with action - Clear next steps
Don'ts
Don't data dump - Curate ruthlessly
Don't bury the insight - Front-load key findings
Don't use jargon - Match audience vocabulary
Don't show methodology first - Context, then method
Don't forget the narrative - Numbers need meaning
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/data-storytelling, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: The Problem
Analysis of churned customers reveals a pattern:
73% churned within first 90 days
Common factor: < 3 support interactions
Low feature adoption in first month
Related Skills
@conductor-validator - Use when the work is better handled by that native specialization after this imported skill establishes context.
@confluence-automation - Use when the work is better handled by that native specialization after this imported skill establishes context.
@content-creator - Use when the work is better handled by that native specialization after this imported skill establishes context.
@content-marketer - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
# Customer Churn Analysis#### Imported: The Hook
"We're losing $2.4M annually to preventable churn."
#### Imported: The Context- Current churn rate: 8.5% (industry average: 5%)
- Average customer lifetime value: $4,800
- 500 customers churned last quarter
#### Imported: The Insight
[Show engagement curve visualization]
Customers who don't engage in the first 14 days
are 4x more likely to churn.
#### Imported: The Solution1. Implement 14-day onboarding sequence
2. Proactive outreach at day 7
3. Feature adoption tracking
#### Imported: Call to Action
Approve $50K budget for onboarding automation.
Framework 2: The Trend Story
# Q4 Performance Analysis#### Imported: Where We Started
Q3 ended with $1.2M MRR, 15% below target.
Team morale was low after missed goals.
#### Imported: What Changed
[Timeline visualization]
- Oct: Launched self-serve pricing
- Nov: Reduced friction in signup
- Dec: Added customer success calls
#### Imported: The Transformation
[Before/after comparison chart]
| Metric | Q3 | Q4 | Change |
|----------------|--------|--------|--------|
| Trial → Paid | 8% | 15% | +87% |
| Time to Value | 14 days| 5 days | -64% |
| Expansion Rate | 2% | 8% | +300% |
#### Imported: Key Insight
Self-serve + high-touch creates compound growth.
Customers who self-serve AND get a success call
have 3x higher expansion rate.
#### Imported: Going Forward
Double down on hybrid model.
Target: $1.8M MRR by Q2.
Framework 3: The Comparison Story
# Market Opportunity Analysis#### Imported: The Question
Should we expand into EMEA or APAC first?
#### Imported: The Comparison
[Side-by-side market analysis]
### EMEA- Market size: $4.2B
- Growth rate: 8%
- Competition: High
- Regulatory: Complex (GDPR)
- Language: Multiple
### APAC- Market size: $3.8B
- Growth rate: 15%
- Competition: Moderate
- Regulatory: Varied
- Language: Multiple
#### Imported: The Analysis
[Weighted scoring matrix visualization]
| Factor | Weight | EMEA Score | APAC Score |
| ----------- | ------ | ---------- | ---------- |
| Market Size | 25% | 5 | 4 |
| Growth | 30% | 3 | 5 |
| Competition | 20% | 2 | 4 |
| Ease | 25% | 2 | 3 |
| **Total** | | **2.9** | **4.1** |
#### Imported: The Recommendation
APAC first. Higher growth, less competition.
Start with Singapore hub (English, business-friendly).
Enter EMEA in Year 2 with localization ready.
#### Imported: Risk Mitigation- Timezone coverage: Hire 24/7 support
- Cultural fit: Local partnerships
- Payment: Multi-currency from day 1
Imported: Visualization Techniques
Technique 1: Progressive Reveal
Start simple, add layers:
Slide 1: "Revenue is growing" [single line chart]
Slide 2: "But growth is slowing" [add growth rate overlay]
Slide 3: "Driven by one segment" [add segment breakdown]
Slide 4: "Which is saturating" [add market share]
Slide 5: "We need new segments" [add opportunity zones]
Slide 1: THE HEADLINE
"We can grow 40% faster by fixing onboarding"
Slide 2: THE CONTEXT
Current state metrics
Industry benchmarks
Gap analysis
Slide 3: THE DISCOVERY
What the data revealed
Surprising finding
Pattern identification
Slide 4: THE DEEP DIVE
Root cause analysis
Segment breakdowns
Statistical significance
Slide 5: THE RECOMMENDATION
Proposed actions
Resource requirements
Timeline
Slide 6: THE IMPACT
Expected outcomes
ROI calculation
Risk assessment
Slide 7: THE ASK
Specific request
Decision needed
Next steps
Template 3: One-Page Dashboard Story
# Monthly Business Review: January 2024#### Imported: THE HEADLINE
Revenue up 15% but CAC increasing faster than LTV
#### Imported: KEY METRICS AT A GLANCE
┌────────┬────────┬────────┬────────┐
│ MRR │ NRR │ CAC │ LTV │
│ $125K │ 108% │ $450 │ $2,200 │
│ ▲15% │ ▲3% │ ▲22% │ ▲8% │
└────────┴────────┴────────┴────────┘
#### Imported: WHAT'S WORKING
✓ Enterprise segment growing 25% MoM
✓ Referral program driving 30% of new logos
✓ Support satisfaction at all-time high (94%)
#### Imported: WHAT NEEDS ATTENTION
✗ SMB acquisition cost up 40%
✗ Trial conversion down 5 points
✗ Time-to-value increased by 3 days
#### Imported: ROOT CAUSE
[Mini chart showing SMB vs Enterprise CAC trend]
SMB paid ads becoming less efficient.
CPC up 35% while conversion flat.
#### Imported: RECOMMENDATION1. Shift $20K/mo from paid to content
2. Launch SMB self-serve trial
3. A/B test shorter onboarding
#### Imported: NEXT MONTH'S FOCUS- Launch content marketing pilot
- Complete self-serve MVP
- Reduce time-to-value to < 7 days
Building the narrative:
• "This leads us to ask..."
• "When we dig deeper..."
• "The pattern becomes clear when..."
• "Contrast this with..."
Introducing insights:
• "The data reveals..."
• "What surprised us was..."
• "The inflection point came when..."
• "The key finding is..."
Moving to action:
• "This insight suggests..."
• "Based on this analysis..."
• "The implication is clear..."
• "Our recommendation is..."
Handling Uncertainty
Acknowledge limitations:
• "With 95% confidence, we can say..."
• "The sample size of 500 shows..."
• "While correlation is strong, causation requires..."
• "This trend holds for [segment], though [caveat]..."
Present ranges:
• "Impact estimate: $400K-$600K"
• "Confidence interval: 15-20% improvement"
• "Best case: X, Conservative: Y"
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
