What makes Data Analyzer Pro notable?

1.1K downloads. Trust evidence available

How should Data Analyzer Pro 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 Analyzer Pro

数据分析 - 加载CSV/JSON自动计算统计描述(均�?中位�?标准�?极�?，异常检测，趋势分析，结果本地持久化 Skill: Data Analyzer Pro Owner: 534422530 Summary: 数据分析 - 加载CSV/JSON自动计算统计描述(均�?中位�?标准�?极�?，异常检测，趋势分析，结果本地持久化 Tags: analysis:2.0.0, data:2.0.0, latest:2.0.0, pro:2.0.0, productivity:1.0.0, statistics:2.0.0, visualization:2.0.0 Version history: v2.0.0 | 2026-05-29T23:31:39.130Z | auto laosi-data-analyzer 2.0.0 adds premium status and metadata. - Added a "premium" badge to indicate enhanced or premium features. - Intro

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

2.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K 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: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 2.0.0release · observed May 29, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170k9770tgh0506hw0dwtb6pd83kwyq:laosi-data-analyzer

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-534422530-laosi-data-analyzer/snapshot"

Documentation

CLAWHUB

19,607 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
badge: premium
name: data-analyzer
version: 2.0.0
description: 数据分析 - 加载CSV/JSON自动计算统计描述(均�?中位�?标准�?极�?，异常检测，趋势分析，结果本地持久化
tags: [data, analysis, statistics, visualization, productivity]
author: laosi
source: original
---

# Data Analyzer - 数据分析引擎

> 激活词: 分析数据 / data analyze / 统计

## 功能

- CSV/JSON 数据加载解析
- 自动统计描述：均值、中位数、标准差、极�?- 异常值检测（IQR/z-score�?- 趋势判断（上�?下降/波动�?- 结果保存到本�?JSON

## Python 实现

```python
import csv, json, statistics, math
from datetime import datetime
from typing import List, Dict, Any

class DataAnalyzer:
    def __init__(self):
        self.data: List[Dict[str, Any]] = []
        self.numeric_cols: List[str] = []
    
    def load_csv(self, path: str, delimiter: str = ",") -> int:
        """从CSV加载数据"""
        with open(path, newline="", encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter=delimiter)
            self.data = list(reader)
        self._detect_numeric()
        return len(self.data)
    
    def load_json(self, path: str) -> int:
        """从JSON加载数据（支持列表和记录列表�?""
        with open(path, encoding="utf-8") as f:
            raw = json.load(f)
        if isinstance(raw, list):
            self.data = raw
        elif isinstance(raw, dict):
            # 尝试找到第一个列表字�?            for v in raw.values():
                if isinstance(v, list):
                    self.data = v
                    break
        self._detect_numeric()
        return len(self.data)
    
    def _detect_numeric(self):
        """自动检测数值列"""
        if not self.data:
            return
        for col in self.data[0]:
            try:
                float(self.data[0][col])
                self.numeric_cols.append(col)
            except (ValueError, TypeError):
                pass
    
    def describe(self, col: str) -> dict:
        """数值列的统计描�?""
        if col not in self.numeric_cols:
            return {"error": f"'{col}' is not numeric"}
        vals = [float(r[col]) for r in self.data if r.get(col)]
        
        n = len(vals)
        mean = statistics.mean(vals)
        median = statistics.median(vals)
        stdev = statistics.stdev(vals) if n > 1 else 0
        
        # 异常检�?(IQR方法)
        sorted_vals = sorted(vals)
        q1 = sorted_vals[n // 4]
        q3 = sorted_vals[3 * n // 4]
        iqr = q3 - q1
        lower = q1 - 1.5 * iqr
        upper = q3 + 1.5 * iqr
        outliers = [v for v in vals if v < lower or v > upper]
        
        # 趋势判断
        half = n // 2
        first_half = statistics.mean(vals[:half]) if half > 0 else mean
        second_half = statistics.mean(vals[half:]) if half > 0 else mean
        trend = "up" if second_half > first_half * 1.05 else "down" if second_half < first_half * 0.95 else "stable"
        
        return {
            "column": col,
            "count": n,
            "mean": round(mean, 2),
            "median": round(median, 2),
            "stdev": round(stdev, 2),
            "min": round(min(vals), 2),
            "max": round(max(vals), 2),

_meta.json

{
  "ownerId": "kn71pk44ca87scz3pstt90r66n80xhaa",
  "slug": "laosi-data-analyzer",
  "version": "2.0.0",
  "publishedAt": 1780097499130
}

skill-card.md

## Description:

Data Analyzer Pro helps an agent load CSV or JSON datasets, compute descriptive statistics, detect outliers, assess trends, calculate correlations, and persist analysis results as JSON.

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

## Publisher:

[534422530](https://clawhub.ai/user/534422530)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, analysts, and business users can use this skill to analyze structured CSV or JSON data for summaries, outliers, trends, and correlations. It is suitable for operational reports, A/B test review, data-quality checks, and metric monitoring.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Report output paths can overwrite arbitrary writable local files when a filename is supplied.

Mitigation: Use only explicit trusted output paths, keep generated reports in a dedicated directory, and confirm before overwriting existing files.

Risk: Broad activation or manifest disclosures may invoke the skill in contexts where local file writes are not expected.

Mitigation: Avoid broad auto-invocation and review the intended dataset and output path before use.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/534422530/skills/laosi-data-analyzer)

## Skill Output:

**Output Type(s):** [text, code, JSON, files]

**Output Format:** [Markdown guidance with Python code examples and optional JSON report files]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Reports may be written to a user-supplied local output path.]

## Skill Version(s):

2.0.0 (source: server release, SKILL.md frontmatter, hub.json)

## 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.

hub.json

{
    "description":  "Premium version with full implementation, examples, and enterprise support",
    "minOpenClawVersion":  "1.2.0",
    "version":  "2.0.0",
    "tags":  [
                 "data",
                 "analysis",
                 "statistics",
                 "visualization",
                 "pro"
             ],
    "author":  "laosi",
    "name":  "data-analyzer",
    "pricing":  {
                    "price":  3900,
                    "model":  "one-time"
                },
    "license":  "MIT",
    "displayName":  "Data Analyzer Pro"
}

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