Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Analyst
Extract insights from data with SQL, visualization, and clear communication of findings.
skill openclawclawhub Free
Extract insights from data with SQL, visualization, and clear communication of findings.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Framing Questions
Clarify the decision being made — analysis without action is trivia "What would change your mind?" surfaces the real question Scope before diving in — infinite data, limited time Hypothesis first, then test — fishing expeditions waste time
Data Quality
Validate data before analyzing — garbage in, garbage out Check row counts, date ranges, null rates first Duplicates hide in joins — always verify uniqueness Source definitions matter — revenue means different things to different teams Document assumptions — future you needs context
SQL Patterns
CTEs over nested subqueries — readable beats clever Aggregate before joining when possible — performance matters Window functions for running totals, ranks, comparisons CASE statements for categorization — clean logic Comment non-obvious filters — why are we excluding these?
Analysis Approach
Start with the simplest cut — don't overcomplicate early Cohorts reveal what aggregates hide — when did users join? Time series need seasonality awareness — don't compare Dec to Jan Segmentation surfaces patterns — average obscures variation Correlation isn't causation — but it's where to look
Visualization
Chart type matches data: trends (line), comparison (bar), distribution (histogram) One message per chart — don't overload Label axes, title clearly — standalone comprehension Color with purpose — highlight, don't decorate Tables for precision, charts for patterns
Communicating Findings
Lead with the insight, not the methodology So what? Now what? — always answer these Confidence levels matter — don't oversell noisy data Recommendations are opinions — label them as such Executive summary first, details available — respect their time
Stakeholder Relationship
Understand their mental model before presenting Regular check-ins prevent surprise requests Push back on bad questions — help them ask better ones Data literacy varies — adjust explanation depth Their intuition is data too — triangulate
Tools
Right tool for the job: SQL for querying, spreadsheets for ad-hoc, BI for dashboards Reproducibility matters — scripts over clicking Version control analysis code — changes need history Automate recurring reports — manual refresh doesn't scale
Common Mistakes
Answering the wrong question precisely Cherry-picking data that confirms expectations Overfitting: explaining noise as signal Death by dashboard: metrics nobody checks Analysis paralysis: perfect insight never delivered
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md