What makes data-analysis notable?

966 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

Analyze, visualize, and explain data using SQL, Python, or spreadsheets to deliver decision-ready reports, cohort and funnel analyses, and hypothesis tests. Skill: data-analysis Owner: modestyrichards Summary: Analyze, visualize, and explain data using SQL, Python, or spreadsheets to deliver decision-ready reports, cohort and funnel analyses, and hypothesis tests. Tags: ai:1.0.0, automation:1.0.0, latest:1.0.0 Version history: v1.0.0 | 2026-04-15T01:17:34.159Z | auto - Added metric contracts, chart guidance, and decision brief templates for more reliable analysis. - Expa

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

966

Updated

Oct 12, 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. 966 downloads reported by the source. Last updated 10/12/2026.

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 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 966 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.0release · observed Apr 15, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
- Added metric contracts, chart guidance, and decision brief templates for more reliable analysis. - Expanded documentation on methodology-first analysis, statistical rigor, and output standards. - Included reference files for KPI definitions, chart selection, pitfalls, and analytical techniques. - Clarified security, privacy, and external endpoint handling. - Added table for related skills and installation guidance.
Observed Apr 15, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17069c392wb17n1nvrdv0kq1h84qjrk:modesty-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-modestyrichards-modesty-data-analysis/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

25,491 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
---
name: Data Analysis
name: Data Analysis
slug: data-analysis
version: 1.0.2
homepage: https://clawic.com/skills/data-analysis
description: "Data analysis and visualization. Query databases, generate reports, automate spreadsheets, and turn raw data into clear, actionable insights. Use when (1) you need to analyze, visualize, or explain data; (2) the user wants reports, dashboards, or metrics turned into a decision; (3) the work involves SQL, Python, spreadsheets, BI tools, or notebooks; (4) you need to compare segments, cohorts, funnels, experiments, or time periods; (5) the user explicitly installs or references the skill for the current task."
changelog: Added metric contracts, chart guidance, and decision brief templates for more reliable analysis.
metadata: {"clawdbot":{"emoji":"D","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}}
---


## When to Use

Use this skill when the user needs to analyze, explain, or visualize data from SQL, spreadsheets, notebooks, dashboards, exports, or ad hoc tables.

Use it for KPI debugging, experiment readouts, funnel or cohort analysis, anomaly reviews, executive reporting, and quality checks on metrics or query logic.

Prefer this skill over generic coding or spreadsheet help when the hard part is analytical judgment: metric definition, comparison design, interpretation, or recommendation.

User asks about: analyzing data, finding patterns, understanding metrics, testing hypotheses, cohort analysis, A/B testing, churn analysis, or statistical significance.

## Core Principle

Analysis without a decision is just arithmetic. Always clarify: **What would change if this analysis shows X vs Y?**

## Methodology First

Before touching data:
1. **What decision** is this analysis supporting?
2. **What would change your mind?** (the real question)
3. **What data do you actually have** vs what you wish you had?
4. **What timeframe** is relevant?

## Statistical Rigor Checklist

- [ ] Sample size sufficient? (small N = wide confidence intervals)
- [ ] Comparison groups fair? (same time period, similar conditions)
- [ ] Multiple comparisons? (20 tests = 1 "significant" by chance)
- [ ] Effect size meaningful? (statistically significant != practically important)
- [ ] Uncertainty quantified? ("12-18% lift" not just "15% lift")

## Architecture

This skill does not require local folders, persistent memory, or setup state.

Use the included reference files as lightweight guides:
- `metric-contracts.md` for KPI definitions and caveats
- `chart-selection.md` for visual choice and chart anti-patterns
- `decision-briefs.md` for stakeholder-facing outputs
- `pitfalls.md` and `techniques.md` for analytical rigor and method choice

## Quick Reference

Load only the smallest relevant file to keep context focused.

| Topic | File |
|-------|------|
| Metric definition contracts | `metric-contracts.md` |
| Visual selection and chart anti-patterns | `chart-selection.md` |
| Decision-ready output formats | `dec

README.md

# data-analysis - OpenClaw Skill

## Installation

```bash
openclaw skill install data-analysis
```

> 📖 **Complete setup guide**: https://skillboss.co/skill.md

### Via ClawHub
```bash
clawhub install modesty-data-analysis
```

### Manual Installation
```bash
git clone https://github.com/ModestyRichards/data-analysis.git
cp -r data-analysis ~/.openclaw/skills/data-analysis
```

## Usage
See SKILL.md for details.

## License
MIT

_meta.json

{
  "ownerId": "kn75r027x07arrf9jf44m6w8r982r01e",
  "slug": "modesty-data-analysis",
  "version": "1.0.0",
  "publishedAt": 1776215854159
}

chart-selection.md

# Chart Selection

Choose visuals based on the question, not on what is easiest to render.

## Question to Chart Map

| Question | Preferred chart | Notes |
|----------|-----------------|-------|
| How is a metric changing over time? | line chart | annotate structural breaks and missing data |
| Which groups are highest or lowest? | sorted bar chart | keep a shared baseline |
| How is the distribution shaped? | histogram or box plot | avoid average-only summaries |
| Are two variables related? | scatter plot | show trend and outliers separately |
| How do parts contribute to the whole? | stacked bar with totals | keep category count low |
| Where are users dropping? | funnel chart | define the time window explicitly |
| How do cohorts retain over time? | cohort table or heatmap | show cohort size alongside retention |

## Default Rules

- Bars start at zero unless there is a strong reason not to.
- Show underlying counts next to percentages when denominators are small.
- Prefer direct labels over legends when possible.
- Use one chart per decision question, not one chart per available metric.

## Visual Anti-Patterns

- Pie charts with many slices -> comparisons become guesswork.
- Dual-axis charts -> viewers infer relationships that are not there.
- Cumulative-only charts -> hide recent deterioration or recovery.
- Truncated bar axes -> exaggerate small differences.
- Stacked areas with many categories -> impossible to compare layers.

## Before Shipping a Chart

Check:

1. What decision question this chart answers.
2. Whether the baseline is visible.
3. Whether the grain and time window match the narrative.
4. Whether annotations explain outages, launches, or missing data.
5. Whether a table would be clearer than the chart.

decision-briefs.md

# Decision Briefs

Use these templates to turn analysis into action instead of dumping findings.

## Standard Decision Brief

1. Decision question.
2. Short answer.
3. Evidence: key numbers and comparison baseline.
4. Confidence: high, medium, or low, with one sentence why.
5. Caveats and what could still change the conclusion.
6. Recommended next action, owner, and due date.

## Experiment Readout

- Hypothesis:
- Primary metric and guardrails:
- Estimated effect and uncertainty:
- Segment differences:
- Ship, iterate, or stop:
- Follow-up test:

## Anomaly Note

- What moved:
- Since when:
- Likely drivers:
- Data quality checks passed or failed:
- Immediate action:
- What to watch next:

## Executive Summary

- One-sentence answer.
- Two or three supporting bullets with numbers.
- One caveat.
- One decision or escalation request.

## Writing Rules

- Lead with the answer, not the method.
- Translate statistics into business implications.
- Separate observations from recommendations.
- If confidence is low, say what would raise confidence.
- Avoid dumping every cut you explored; keep only evidence that changes the decision.

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