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/casper-studios/casper-marketplace/data-analysisnpx skills add Casper-Studios/casper-marketplace --skill data-analysisgit clone --depth 1 https://github.com/Casper-Studios/casper-marketplaceWrote 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/casper-studios/casper-marketplace/data-analysis)<a href="https://agentmods.dev/skills/casper-studios/casper-marketplace/data-analysis"><img src="https://agentmods.dev/badge/skills/casper-studios/casper-marketplace/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 | $0.00092 | $0.03007 |
| Opus 5 | $0.00046 | $0.01503 |
| Sonnet 5 | $0.00018 | $0.00601 |
| Haiku 4.5 | $0.00009 | $0.00301 |
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 — 408 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
Overview
A comprehensive data analysis and storytelling skill optimized for financial, SaaS, and RevOps contexts. This skill provides structured workflows for turning raw data into actionable insights with full transparency on analytical decisions, bias awareness, and progressive disclosure reporting.
Workflow Overview
Every analysis follows a 7-phase process:
1. SETUP → Initialize Marimo notebook (run init_marimo_notebook.py)
2. INGEST → Load data, document sources and assumptions
3. EXPLORE → EDA with logged decisions (why this viz, why this filter)
4. MODEL → If needed, with interpretable-first approach
5. INTERPRET → Apply bias checklist, hedge appropriately
6. WISHLIST → Document data gaps and proxies used
7. OUTPUT → Generate appropriate tier (slides/report/notebook)
Decision Logging Protocol
Every analytical choice must be logged. This creates an audit trail and enables reproducibility.
What to Log
| Decision Type | Example | Log Format |
|---|---|---|
| Data filtering | Removed 47 records with null revenue | FILTER: [reason] - [count] records affected |
| Metric choice | Used logo churn vs revenue churn | METRIC: [chosen] over [alternative] because [reason] |
| Visualization | Line chart for time series | VIZ: [type] because [reason] |
| Assumption | Assumed linear growth for projection | ASSUMPTION: [statement] - confidence: [H/M/L] |
| Proxy used | Used support tickets as NPS proxy | PROXY: [proxy] for [missing data] - quality: [S/M/W] |
Log Format in Notebook
# === DECISION LOG ===
# FILTER: Excluded trial accounts - 1,247 records removed
# METRIC: NRR over GRR because expansion is significant factor
# ASSUMPTION: Q4 seasonality similar to prior year - confidence: M
# PROXY: Support ticket sentiment for NPS - quality: Weak
Analysis Workflow Details
Phase 1: Setup
Run the initialization script to create a new Marimo notebook with pre-built scaffolding:
What ships with it
21 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.
- .claude-plugin/plugin.json 814 B
- commands/analyze.md 2.5 KB
- README.md 6.1 KB
- references/biases.md 9.4 KB
- references/dashboard-patterns.md 15 KB
- references/data-cleaning.md 14 KB
- references/data-quality-validator.md 18 KB
- references/data-wishlisting.md 8.0 KB
- references/datetime-handling.md 13 KB
- references/metrics.md 7.8 KB
- references/pdf-patterns.md 14 KB
- references/report-templates.md 11 KB
- references/visualization-guide.md 14 KB
- references/xlsx-patterns.md 13 KB
- scripts/generate_pptx_summary.py 13 KB runs code
- scripts/init_dashboard.py 11 KB runs code
- scripts/init_marimo_notebook.py 7.6 KB runs code
- scripts/profile_data.py 15 KB runs code
- scripts/recalc.py 7.3 KB runs code
- skills/data-analysis/agents/openai.yaml 198 B
- skills/data-analysis/SKILL.md 460 B
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 · 408 lines · 92 tokens per session scan A 4e29ccd663ae
data-analysis is a skill published in the GitHub repository Casper-Studios/casper-marketplace (12 stars, last pushed yesterday), licensed MPL-2.0. It adds 92 tokens to every session and 3,007 once invoked, about $0.0005 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.
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