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Data Analysis Litiao
by
litiao1224
· GitHub ↗
· v1.0.0
· MIT-0
1738
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Install in OpenClaw
/install data-analysis-litiao
Description
Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls.
README (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
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install data-analysis-litiao - After installation, invoke the skill by name or use
/data-analysis-litiao - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Bug fixes and improvements with -litiao suffix
Metadata
Frequently Asked Questions
What is Data Analysis Litiao?
Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls. It is an AI Agent Skill for Claude Code / OpenClaw, with 1738 downloads so far.
How do I install Data Analysis Litiao?
Run "/install data-analysis-litiao" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Data Analysis Litiao free?
Yes, Data Analysis Litiao is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Data Analysis Litiao support?
Data Analysis Litiao is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Data Analysis Litiao?
It is built and maintained by litiao1224 (@litiao1224); the current version is v1.0.0.
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