Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill data-storytellinggit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/data-storytelling)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/data-storytelling"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/data-storytelling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/data-storytelling"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/data-storytelling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.02838 |
| Opus 5.5 | $0.00014 | $0.01135 |
| Sonnet 5.5 | $0.00007 | $0.00568 |
| Haiku 4.5 | $0.00003 | $0.00284 |
Grade A, and why
data-storytelling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- data-storytelling — 100% identical, 0 lines differ
- data-storytelling — 100% identical, 2 lines differ
- data-storytelling — 100% identical, 4 lines differ
- data-storytelling — 100% identical, 80 lines differ
How it starts
The opening of the file, as written. The whole thing — 454 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Storytelling
Transform raw data into compelling narratives that drive decisions and inspire action.
When to Use This Skill
- Presenting analytics to executives
- Creating quarterly business reviews
- Building investor presentations
- Writing data-driven reports
- Communicating insights to non-technical audiences
- Making recommendations based on data
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 |
Story Frameworks
Framework 1: The Problem-Solution Story
# Customer Churn Analysis
## The Hook
"We're losing $2.4M annually to preventable churn."
## The Context
- Current churn rate: 8.5% (industry average: 5%)
- Average customer lifetime value: $4,800
- 500 customers churned last quarter
## 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
## The Insight
[Show engagement curve visualization]
Customers who don't engage in the first 14 days
are 4x more likely to churn.
## The Solution
1. Implement 14-day onboarding sequence
2. Proactive outreach at day 7
3. Feature adoption tracking
## Expected Impact
- Reduce early churn by 40%
- Save $960K annually
- Payback period: 3 months
## Call to Action
Approve $50K budget for onboarding automation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 454 lines · 34 tokens per session scan A 5ab8643ba05c
data-storytelling is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,772 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 2,838 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
git-code-review
Review uncommitted or committed changes in the current branch and generate a code review report. Use when the user wants to review their changes before or after committing.
file-code-review
A code-review tool for specified files or pasted code. It checks the material against project conventions and creates a code review report, without needing the surrounding Git history.
wechat-article-writer
A Chinese-language writing workflow for turning a vague topic into a publishable WeChat Official Account article. WeChat Official Accounts are channels used to publish articles to followers in China.
openspec-archive-change
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.