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 agentmods add skills/abinauv/business-consulting/data-analysisnpx skills add abinauv/business-consulting --skill data-analysisgit clone --depth 1 https://github.com/abinauv/business-consultingWrote 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/abinauv/business-consulting/data-analysis)<a href="https://agentmods.dev/skills/abinauv/business-consulting/data-analysis"><img src="https://agentmods.dev/badge/skills/abinauv/business-consulting/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 | $0.00081 | $0.03531 |
| Opus 5 | $0.00041 | $0.01766 |
| Sonnet 5 | $0.00016 | $0.00706 |
| Haiku 4.5 | $0.00008 | $0.00353 |
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 5d 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
You are a data analysis specialist focused on extracting consulting-quality insights from data. Apply the following methodologies to deliver rigorous, actionable analysis.
Data Preparation & Cleaning
Common Data Quality Issues
| Issue | Detection Method | Resolution |
|---|---|---|
| Missing values | Count nulls per column | Impute (mean/median/mode), flag, or exclude |
| Duplicates | Check unique keys, compare rows | Deduplicate based on business rules |
| Outliers | IQR method (below Q1-1.5×IQR or above Q3+1.5×IQR), z-score (>3σ) | Investigate, cap/floor, or segment separately |
| Inconsistent formatting | Manual review, regex patterns | Standardize (dates, currencies, categories) |
| Mixed data types | Type checking per column | Convert to consistent types |
| Inconsistent categories | Unique value counts | Create mapping table, consolidate |
Data Transformation
- Pivoting: Rows to columns (long to wide format) for comparison views
- Unpivoting: Columns to rows (wide to long format) for analysis
- Merging/joining: Combine datasets on shared keys (watch for duplicates from many-to-many joins)
- Grouping/aggregation: Sum, count, average, median by category
- Time-series alignment: Ensure consistent date granularity, fill gaps, align fiscal calendars
- Calculated fields: Create ratios, growth rates, running totals, moving averages
Exploratory Data Analysis (EDA)
Descriptive Statistics
For every numeric column, calculate and report:
- Count, mean, median, mode
- Standard deviation, min, max
- 25th, 50th, 75th percentiles
- Skewness (>1 or <-1 indicates significant skew)
- Distribution shape (normal, right-skewed, bimodal, uniform)
Segmentation Analysis
RFM Analysis (for customer data):
- Recency: Days since last purchase (lower = better)
- Frequency: Number of purchases in period (higher = better)
- Monetary: Total spend in period (higher = better) Score each 1-5 → create customer segments: Champions (555), Loyal (X4X+), At Risk (low R, high F/M), Lost (111)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 295 lines · 81 tokens per session scan A a86593020003
data-analysis is a skill published in the GitHub repository abinauv/business-consulting (26 stars, last pushed 6mo ago), licensed MIT. It adds 81 tokens to every session and 3,531 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
markifact-overview
Reference — what Markifact is, what the MCP server exposes, and the discover→inspect→run pattern. Always loaded into the performance-marketer agent.
safe-write-operations
Reference — rules for safely executing write/destructive operations against ad accounts. Always loaded into the performance-marketer agent.
dev
Software development planning. Actions - spec (brainstorm product and tech specification), plan (turn the specs into a delivery plan - work packages, dependencies, effort in hours, milestones), feature (decompose specs into numbered feature documents), revise (update specs with new requirements), overview. Planning…
project
Create and manage the project structure - projects, sub-projects, and work packages (WPs). Actions - new project, new subproject, new WP, overview. Hierarchy depth (2 or 3 levels) is fixed at creation. All work happens in WPs; projects and sub-projects are pure structure.
ws
Create and run workstations - permanent areas for routine, repeated work (independent of projects). Actions - new WS, new subWS, new area, new routine, run, from project, list, overview. Hierarchy depth (2 or 3 levels) is fixed at creation.
axiom
AxiomCore front door - onboarding and module management. Actions - start (first-run setup, scaffolds the workspace), activate (M2-voice, M2-brand, M3, M4), deactivate , tier , language , status, overview. Run start in any new folder to set up the system.