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Data Analysis Litiao

Name: Data Analysis Litiao
Author: litiao1224

作者 litiao1224 · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ✓ 安全检测通过

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在 OpenClaw 中安装

/install data-analysis-litiao

功能描述

Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls.

使用说明 (SKILL.md)

When to Load

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

What decision is this analysis supporting?
What would change your mind? (the real question)
What data do you actually have vs what you wish you had?
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")

Analytical Pitfalls to Catch

Pitfall	What it looks like	How to avoid
Simpson's Paradox	Trend reverses when you segment	Always check by key dimensions
Survivorship bias	Only analyzing current users	Include churned/failed in dataset
Comparing unequal periods	Feb (28d) vs March (31d)	Normalize to per-day or same-length windows
p-hacking	Testing until something is "significant"	Pre-register hypotheses or adjust for multiple comparisons
Correlation in time series	Both went up = "related"	Check if controlling for time removes relationship
Aggregating percentages	Averaging percentages directly	Re-calculate from underlying totals

For detailed examples of each pitfall, see pitfalls.md.

Approach Selection

Question type	Approach	Key output
"Is X different from Y?"	Hypothesis test	p-value + effect size + CI
"What predicts Z?"	Regression/correlation	Coefficients + R² + residual check
"How do users behave over time?"	Cohort analysis	Retention curves by cohort
"Are these groups different?"	Segmentation	Profiles + statistical comparison
"What's unusual?"	Anomaly detection	Flagged points + context

For technique details and when to use each, see techniques.md.

Output Standards

Lead with the insight, not the methodology
Quantify uncertainty — ranges, not point estimates
State limitations — what this analysis can't tell you
Recommend next steps — what would strengthen the conclusion

Red Flags to Escalate

User wants to "prove" a predetermined conclusion
Sample size too small for reliable inference
Data quality issues that invalidate analysis
Confounders that can't be controlled for

安全使用建议

This skill appears to be a benign, instruction-only data-analysis helper. Before installing or allowing it to run on sensitive data: 1) verify provenance — ask the publisher for a homepage, source repository, or author identity because _meta.json and registry metadata disagree; 2) avoid feeding sensitive PII or credentials into examples run with the skill; 3) test on small, non-sensitive datasets to confirm outputs meet your expectations; and 4) treat its recommendations as guidance (not authoritative code) — validate analyses and check assumptions before making decisions based on its output.

功能分析

Type: OpenClaw Skill Name: data-analysis-litiao Version: 1.0.0 The skill bundle provides a comprehensive and professional framework for data analysis, focusing on statistical rigor, methodology, and avoiding common analytical pitfalls. The files (SKILL.md, pitfalls.md, and techniques.md) contain educational content and procedural instructions for an AI agent without any evidence of malicious code, data exfiltration, or prompt-injection attacks.

能力评估

ℹ Purpose & Capability

The skill's name, description, and the SKILL.md / techniques/pitfalls content are coherent: they provide analysis methodology, checks, and recommended outputs appropriate for a 'data analysis' helper. However, metadata inconsistencies exist: the registry metadata ownerId (kn7838z...) and slug (data-analysis-litiao) do not match the _meta.json ownerId (kn73vp5...) and slug (data-analysis). There is also no homepage or source URL listed. These mismatches are a provenance concern but do not indicate malicious functionality.

✓ Instruction Scope

SKILL.md and the supporting docs are purely prescriptive about analytical methodology and outputs. They do not instruct the agent to read arbitrary system files, call external endpoints, exfiltrate data, or access environment variables. There is no step that broadens scope beyond data-analysis guidance.

✓ Install Mechanism

No install spec and no code files beyond markdown—this is instruction-only. Nothing is downloaded or written to disk by an installer, which minimizes installation risk.

✓ Credentials

The skill declares no required environment variables, credentials, or config paths. The instructions do not reference secrets or unrelated service tokens. The requested access is proportionate to its stated purpose.

✓ Persistence & Privilege

Skill flags are default (not always:true), and autonomous invocation is enabled (the platform default). It does not request permanent presence nor system configuration changes. No concerning privilege escalation is requested.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install data-analysis-litiao
安装完成后，直接呼叫该 Skill 的名称或使用 /data-analysis-litiao 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

Bug fixes and improvements with -litiao suffix

元数据

Slug data-analysis-litiao

版本 1.0.0

许可证 MIT-0

累计安装 9

当前安装数 9

历史版本数 1

常见问题