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 fatihkan/badi --skill data-analyticsgit clone --depth 1 https://github.com/fatihkan/badiWrote 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/fatihkan/badi/data-analytics)<a href="https://agentmods.dev/skills/fatihkan/badi/data-analytics"><img src="https://agentmods.dev/badge/skills/fatihkan/badi/data-analytics.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 | $0.00060 | $0.01166 |
| Opus 5 | $0.00030 | $0.00583 |
| Sonnet 5 | $0.00012 | $0.00233 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
data-analytics 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 today.
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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analytics Skills
This file contains all the skills across data collection, analysis, visualization, machine learning, business intelligence, and data engineering.
data-strategy-design
Enables data-driven decision making by building a comprehensive data strategy for the organization.
data-collection-design
Designs data collection mechanisms to ensure an accurate, complete data flow.
event-tracking-design
Designs the event-tracking infrastructure for applications and websites.
data-warehouse-design
Builds the analytics foundation by designing the data warehouse architecture.
etl-pipeline-design
Automates data transformation and loading by designing ETL/ELT pipelines.
data-lake-architecture
Enables storage of structured and unstructured data by designing a data lake architecture.
sql-analysis-queries
Writes and optimizes SQL analysis queries that answer business questions.
dashboard-design
Designs effective, actionable dashboards for business users.
data-visualization
Applies chart-type selection and design principles to present data clearly and effectively.
cohort-analysis
Surfaces trends through time-based behavior analyses over user cohorts.
funnel-analysis
Identifies drop-off points and optimization opportunities by analyzing the user conversion funnel.
segmentation-analysis
Runs segmentation analyses that split users or customers into meaningful groups.
a-b-test-analysis
Drives reliable decisions by analyzing A/B test results statistically.
forecasting-model
Anticipates future trends by building forecasting models on historical data.
customer-lifetime-value
Builds models and analyses that compute customer lifetime value (LTV/CLV).
churn-analysis
Determines churn causes and prevention strategies by analyzing customer/user loss.
rfm-analysis
Shapes marketing strategy by segmenting customers with RFM (Recency, Frequency, Monetary) analysis.
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.
- today First seen · 265 lines · 0 tokens per session scan A 7b8bab9e41b7
data-analytics is a skill published in the GitHub repository fatihkan/badi (7 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,166 once invoked, about $0.0003 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 2026-09-06.
Other skills, from other repositories
pr-triage
4-phase PR backlog management with audit, deep code review, validated comments, and optional worktree setup. Use when triaging pull requests, catching up on pending code reviews, or managing a backlog of open PRs. Args: 'all' to review all, PR numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit…
audit-agents-skills
Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
eval-agents
Audit Claude Code agents defined in .claude/agents/ for description specificity, model tier appropriateness, tools scoping, and system prompt quality. Detects dispatch ambiguity between agents, flags over-permissive tool grants, and checks for human-in-the-loop patterns that break programmatic orchestration. Use when…
issue-triage
3-phase issue backlog management with audit, deep analysis, and validated triage actions. Use when triaging GitHub issues, sorting bug reports, cleaning up stale tickets, or detecting duplicate issues. Args: 'all' to analyze all, issue numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit only.
check-cache-bugs
Audit Claude Code setup for cache bugs (CC#40524): sentinel, --resume/--continue, attribution header + ArkNill B3/B4/B5.
git-ai-archaeology
Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.