What makes Data Analysis notable?

1.2K downloads. Trust evidence available

How should Data Analysis be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Data Analysis

Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzin... Skill: Data Analysis Owner: yz6214589-hash Summary: Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats. Use this skill whenever the user mentions: analyzin... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-21T04:02:02.289Z | user init Archive index: Archive v1.0.0: 4 files, 12345 bytes Files: evals/evals.json (2462b), skill-card.md (2051b), SKILL.md (24095b)

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Apr 21, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17996fk24wt0zdqhvatzxareh857jje:yz6214589-hash-data-analysis

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-yz6214589-hash-yz6214589-hash-data-analysis/snapshot"

Documentation

CLAWHUB

29,098 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: data-analysis
version: 1.0.0
description: |
  Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.
  Use this skill whenever the user mentions: analyzing data, CSV files, data insights, generating reports,
  data cleaning, exploratory analysis, business metrics, sales analysis, user behavior analysis, data visualization,
  creating dashboards, or asks questions like "what does this data tell us?" or "analyze this dataset".
  Also trigger when the user provides a CSV file path and asks for any kind of analysis or summary.
  This skill provides a professional 7-step workflow with quality checks, interactive cleaning strategy selection,
  and multiple output formats (Markdown report, interactive HTML, or full dashboard).
metadata:
  openclaw:
    emoji: "📊"
    tags: ["data", "analysis", "csv", "visualization", "insights", "dashboard"]
---

# Data Analysis Skill

A comprehensive, interactive data analysis workflow that transforms CSV data into actionable business insights. This skill guides you through professional data analysis from initial exploration to final deliverable, with quality gates and user confirmations at key decision points.

## Core Workflow

This skill follows a 7-step methodology with 3 interaction points:

```
Input → Business Understanding → Data Inspection → Cleaning Strategy → EDA → Deep Analysis → Insights → Output
  ↓           ↓                      ↓                ↓                ↓           ↓          ↓
Required   Interaction 1        Quality Gate     Interaction 2    Auto-run    Auto-run   Interaction 3
```

---

## Input Requirements

**Required:**
- CSV file path (absolute or relative)
- Business question or analysis goal

**Optional:**
- Data dictionary (field descriptions)
- Analysis depth: `--quick` (basic stats), `--standard` (default), or `--deep` (advanced modeling)
- Auto mode: `--auto` (skip interactions, use recommended strategies)
- Output preference: `--format=markdown|html|dashboard`

**Usage examples:**
```
"Analyze sales_data.csv - I want to know which channels have the best conversion rates"
"Help me understand customer_behavior.csv, specifically looking at retention patterns"
"Quick analysis of Q4_results.csv --quick --auto"
```

---

## Step 1: Business Understanding (Interaction Point 1)

### Your Actions

1. **Parse the business question** and identify:
   - Key metrics mentioned (revenue, conversion rate, churn, etc.)
   - Analysis type needed (trend analysis, comparison, attribution, prediction)
   - Expected dimensions (time, geography, customer segments, channels)
   - Chart types that would best illustrate the answer

2. **Generate an analysis plan** in this format:
   ```markdown
   ## Analysis Plan

   **Core Question:** [Restate the user's goal in one sentence]

   **Key Metrics to Calculate:**
   - [Metric 1: e.g., Monthly conversion rate by channel]
   - [Metric 2: e.g., Average order value trend]

   **Analysis Dimensions:**

_meta.json

{
  "ownerId": "kn7070e6kjytarngxs9g8h1c018577bj",
  "slug": "yz6214589-hash-data-analysis",
  "version": "1.0.0",
  "publishedAt": 1776744122289
}

skill-card.md

## Description:

Comprehensive data analysis workflow for CSV files with interactive guidance and flexible output formats.

This skill is ready for commercial/non-commercial use.

## Publisher:

[yz6214589-hash](https://clawhub.ai/user/yz6214589-hash)

### License/Terms of Use:

MIT-0

## Use Case:

External users and developers use this skill to guide CSV data analysis, data cleaning, exploratory analysis, visualization, insight generation, and report or dashboard delivery.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill can modify the Python environment by installing analysis packages.

Mitigation: Run it in an isolated environment and preinstall reviewed dependencies when possible.

Risk: Generated HTML reports can contact external CDNs for Chart.js and Tailwind CSS.

Mitigation: Use Markdown output for sensitive data, or review and self-host required assets before sharing HTML reports.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/yz6214589-hash/skills/yz6214589-hash-data-analysis)
- [Chart.js CDN script used by generated HTML reports](https://cdn.jsdelivr.net/npm/[email protected]/dist/chart.umd.min.js)
- [Tailwind CSS CDN script used by generated HTML reports](https://cdn.tailwindcss.com)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown reports, interactive HTML reports, dashboard files, charts, cleaned CSV data, and analysis guidance.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May create timestamped data-analysis-results directories containing reports, charts, cleaned data, and optional analysis code.]

## Skill Version(s):

1.0.0 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

evals/evals.json

{
  "skill_name": "data-analysis",
  "evals": [
    {
      "id": 1,
      "prompt": "I need to analyze ~/.claude/skills/data-analysis-workspace/test-data/sales_channels.csv - specifically, I want to know which marketing channel has the best conversion rate and ROI. Can you help me figure out where we should invest more budget? Use --auto mode and give me a markdown report.",
      "expected_output": "Analysis report that: (1) correctly calculates conversion metrics by channel, (2) identifies Organic Search or Email Campaign as top performers, (3) calculates ROI (Revenue/Cost) per channel, (4) provides specific budget allocation recommendations with numbers, (5) includes at least 2 visualizations (channel comparison chart, trend over time), (6) generates a markdown report with embedded charts",
      "files": []
    },
    {
      "id": 2,
      "prompt": "Help me understand user retention in this file: ~/.claude/skills/data-analysis-workspace/test-data/user_behavior.csv. I'm worried about users who sign up but never make a purchase, and also want to see if there's any pattern in how long people stay active. The Age column has some missing values - just use the median to fill those. Give me an interactive HTML report.",
      "expected_output": "Analysis report that: (1) identifies the percentage of users who never purchased (FirstPurchaseDate is null), (2) calculates average days from signup to first purchase for converters, (3) analyzes retention by cohort or time period, (4) handles missing Age values as requested (median fill), (5) provides insights about user segments (e.g., by age, country, spending level), (6) generates an interactive HTML report with Chart.js visualizations",
      "files": []
    },
    {
      "id": 3,
      "prompt": "quick analysis of ~/.claude/skills/data-analysis-workspace/test-data/product_inventory.csv --quick --auto - just want to see what products are out of stock and if there are any weird prices. don't need anything fancy, just the key issues",
      "expected_output": "Quick analysis report that: (1) identifies all products with Stock=0 (out of stock items), (2) detects the negative price anomaly (P016 has -25.99), (3) flags missing values in Price and Stock columns, (4) provides a data quality score, (5) lists specific recommendations for each issue, (6) completes in quick mode without extensive visualizations, (7) outputs a concise markdown summary",
      "files": []
    }
  ]
}

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