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agentCLAWHUBUnverified

Data Analysis

Data analysis and visualization. Query databases, generate reports, automate spreadsheets, and turn raw data into clear, actionable insights. Use when (1) yo... Skill: Data Analysis Owner: ivangdavila Summary: Data analysis and visualization. Query databases, generate reports, automate spreadsheets, and turn raw data into clear, actionable insights. Use when (1) yo... Tags: latest:1.0.2 Version history: v1.0.2 | 2026-03-11T15:11:50.484Z | user Added metric contracts, chart guidance, and decision brief templates for more reliable analysis. v1.0.1 | 2026-03-11T14:45:27.317Z |

OpenClaw
View on ClawHub

Rank

62

Safety

84

Downloads

31k

Updated

May 11, 2026

Version

1.0.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 31.3K downloads reported by the source. Last updated 5/11/2026.

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 May 11, 2026
Protocol compatibility
OpenClawcompatibility · observed May 11, 2026
Adoption signal
31.3K downloadsadoption · observed May 11, 2026
Latest release
1.0.2release · observed Mar 11, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s178jdk12x4qj3gs2se3etxf3h83h7ft:data-analysis
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. 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-ivangdavila-data-analysis-2/snapshot"

Documentation

CLAWHUB

47,507 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
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":[]},"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 | `decision-briefs.md` |
| Failure modes

_meta.json

{
  "ownerId": "kn73vp5rarc3b14rc7wjcw8f8580t5d1",
  "slug": "data-analysis",
  "version": "1.0.2",
  "publishedAt": 1773241910484
}

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.

metric-contracts.md

# Metric Contracts

Use this when a KPI, dashboard tile, or report number could be interpreted in more than one way.

## Contract Template

Capture each metric in this order before trusting comparisons:

1. Business question the metric is meant to answer.
2. Entity and grain: user, account, order, session, day, week, month.
3. Numerator and denominator with exact inclusion logic.
4. Filters and exclusions: internal traffic, refunds, test accounts, paused users.
5. Time window, timezone, and refresh cadence.
6. Source of truth and owner.
7. Known caveats, version changes, and safe interpretation range.

## Minimum Contract Output

| Field | Example |
|-------|---------|
| Metric | Paid conversion rate |
| Question | Is onboarding quality improving? |
| Grain | weekly |
| Numerator | first paid subscriptions |
| Denominator | qualified onboarding starts |
| Filters | excludes employees and QA accounts |
| Timezone | UTC |
| Source | warehouse.subscriptions_daily |
| Owner | Growth lead |
| Caveat | Launch week excluded because tracking was partial |

## Stop Conditions

Do not present a metric as stable if:

- Numerator or denominator changed between periods.
- Source ownership is unclear.
- Filters were applied ad hoc and not documented.
- Time windows or timezones differ across comparisons.
- A dashboard label hides a formula change.

## Fast Questions to Ask

- "What exactly counts in the numerator?"
- "Who is excluded and why?"
- "What is the comparison baseline?"
- "Has this definition changed over time?"
- "Who would dispute this number internally?"

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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      "category": "vendor",
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}

Record generated Oct 9, 2026.

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