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 GiangGiangTran/ba-skills --skill data-analysisgit clone --depth 1 https://github.com/GiangGiangTran/ba-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/gianggiangtran/ba-skills/data-analysis)<a href="https://agentmods.dev/skills/gianggiangtran/ba-skills/data-analysis"><img src="https://agentmods.dev/badge/skills/gianggiangtran/ba-skills/data-analysis.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00039 | $0.01813 |
| Opus 5 | $0.00019 | $0.00907 |
| Sonnet 5 | $0.00008 | $0.00363 |
| Haiku 4.5 | $0.00004 | $0.00181 |
Grade A, and why
data-analysis 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis for BA
Support decisions with evidence, not intuition.
What is Data Analysis?
Definition: Systematic process of collecting, cleaning, analyzing, and interpreting data to answer business questions and inform decisions.
Why it matters:
- Intuition wrong 30-40% of time (cognitive biases)
- Data reveals truth hidden from observation
- Numbers convince executives better than opinions
- Baseline data allows measuring progress
When to Use:
- ✅ Validating assumptions (do 50% of users really want this?)
- ✅ Prioritizing features (which matters most to customers?)
- ✅ Measuring success (are we meeting our KPIs?)
- ✅ Identifying problems (where is quality failing?)
- ✅ Comparing options (Option A or Option B?)
4 Levels of Analytics
Level 1: Descriptive Analytics (What happened?)
Summarize historical data.
Question: "How many users signed up last month?"
Data: User signup logs
Analysis: Count signups by cohort
Output: 5,234 signups (↑12% vs previous month)
Question: "Which features get used most?"
Data: Feature usage logs
Analysis: Count usage by feature
Output: Feature A: 85%, Feature B: 45%, Feature C: 12%
Level 2: Diagnostic Analytics (Why happened?)
Understand causes behind observations.
Question: "Why did signups drop last week?"
Data: Signups, marketing spend, conversion rates, technical incidents
Analysis: Correlate events with signup dips
Output: Correlation with:
- Reduced ad spend (-$10K) = -800 signups expected
- Website downtime (2 hours) = -50 signups lost
Root cause: Marketing reduced budget, not technical issue
Level 3: Predictive Analytics (What will happen?)
Forecast future based on patterns.
Question: "How many users will churn next quarter?"
Historical Data: Churn patterns by cohort, feature adoption, support tickets
Analysis: Identify predictive factors (inactivity >30 days = 70% churn)
Output: Predicted 300 users (±20%) will churn next quarter
Action: Design retention campaign for inactive users
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.
- 7d ago First seen · 259 lines · 39 tokens per session scan A 41a11c57f900
data-analysis is a skill published in the GitHub repository GiangGiangTran/ba-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 1,813 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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