Analytics Tracking Automation
jtrackingai/analytics-tracking-automation
A skill your agent uses when you need GA4 + GTM tracking delivery from site discovery through publish, or when the right phase entry point is still unclear.
A skill your agent uses when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and…
Install 13 Data Analysis Global
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-global --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/en/13-data-analysis-global .claude/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .claude/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-globalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-global --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/en/13-data-analysis-global .agents/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .agents/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-global --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/en/13-data-analysis-global .cursor/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .cursor/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/minhnv0807/ai-business-skills.git --path skills/en/13-data-analysis-global--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-global --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/en/13-data-analysis-global .gemini/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .gemini/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-globalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/en/13-data-analysis-global .github/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .github/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install minhnv0807/ai-business-skills 13-data-analysis-global --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/minhnv0807/ai-business-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/en/13-data-analysis-global .opencode/skills/13-data-analysis-global && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "13-data-analysis-global" agent skill from https://github.com/minhnv0807/ai-business-skills/tree/master/skills/en/13-data-analysis-global into .opencode/skills/13-data-analysis-global/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "13-data-analysis-global", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
13-data-analysis-global- GitHub stars
- 608
- Token cost
- ~4k tokens
- SKILL.md length
- 1,044 words
- Files
- 1
- Skills in repo
- 142
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and…
- Works in 4 steps: Data source? Meta Ads, TikTok Ads, GA4,… → Time window? This week, this month, A vs… → Current business goal? Increase leads,… → …
- Raw data exists — Meta
- SKILL.md covers Information Gathering, Analysis Principles, Analysis Frameworks by Source and Trend Detection, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
13 Data Analysis Global is an agent skill from minhnv0807/ai-business-skills. Use when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for —…
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Paid advertising, Excel spreadsheets and Data analysis. It works with Google Analytics, Shopify, TikTok and Meta Ads. The repository describes itself as: 138 bilingual AI marketing skills (69 VN + 69 Global) for Claude Code, OpenCode, Codex, VS Code. Four role SOP packs — content, design, performance, leader ops — plus strategy… The licence is MIT.
When your agent uses it
- Raw data exists — Meta
- A spreadsheet — and has to become insight and decisions: descriptive
- Prescriptive layers
- Cuts by channel
Example prompts
- “analyze this data”
- “read these numbers for me”
- “what does this export say”
- “/13-data-analysis-global”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Data source? Meta Ads, TikTok Ads, GA4, Shopify, Triple Whale/Hyros/Northbeam, Google Sheets — single source or combined?
- Time window? This week, this month, A vs B (e.g. March vs April)?
- Current business goal? Increase leads, lower CPL, raise ROAS, or a specific issue to fix?
- Paste data here — drop a table, or describe core metrics (spend, impressions, clicks, leads, revenue).
What it can do on your machine
Read from SKILL.md and the folder at commit 0360adc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
13 Data Analysis Global loads about 4k tokens when it runs. Until then it costs about 182 tokens; SKILL.md has 1,044 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from minhnv0807/ai-business-skills at commit 0360adc, republished under its MIT licence (© minhnv0807). 1,044 words, ~3,999 tokens.
Download SKILL.mdSave it as
.claude/skills/13-data-analysis-global/SKILL.md (or your agent's skills folder).- name
- 13-data-analysis-global
- description
- Use when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.
- metadata.version
- 2.5.1
- metadata.category
- performance
- metadata.language
- en
- triggers
- data analysis, analyze data, marketing analytics, Meta Ads analysis, TikTok Ads analysis, GA4 report, performance analysis, Triple Whale, Hyros, Northbeam
- output
- A .md report structured as Descriptive, Diagnostic, Predictive, Prescriptive — with tables and concrete recommendations
- related
- 03-performance-review-global, 07-marketing-report-global, 10-reverse-kpi-calc-global, 12-landing-page-brief-global
Marketing Data Analysis (Global)
Insight before numbers. Lead with judgment, illustrate with data — never list numbers without interpretation.
Information Gathering
Ask up to 4 questions:
- Data source? Meta Ads, TikTok Ads, GA4, Shopify, Triple Whale/Hyros/Northbeam, Google Sheets — single source or combined?
- Time window? This week, this month, A vs B (e.g. March vs April)?
- Current business goal? Increase leads, lower CPL, raise ROAS, or a specific issue to fix?
- Paste data here — drop a table, or describe core metrics (spend, impressions, clicks, leads, revenue).
Analysis Principles
Reading Order
1. DESCRIPTIVE — What happened? (numbers, trends)
2. DIAGNOSTIC — Why? (root cause)
3. PREDICTIVE — What's next? (forecast)
4. PRESCRIPTIVE — What to do? (concrete actions)Presentation Rules
| Rule | Explanation |
|---|---|
| Insight first, numbers second | "CPL up 40% due to creative fatigue" — not "CPL went from $5 to $7" |
| Compare, don't quote absolutes | Always compare with: prior week (WoW), prior month (MoM), or industry benchmark |
| Flag anomalies | Any metric moving > 20% vs prior period → flag for investigation |
| Recommendations have deadlines | Each recommendation specifies: action, when, owner, success metric |
Analysis Frameworks by Source
Meta Ads
| Level | Primary metrics | Secondary metrics |
|---|---|---|
| Account | Spend, ROAS, CPA | Frequency, Reach |
| Campaign | CPM, CPL, Conv rate | Budget utilization |
| Ad Set | CPC, CTR, CPM | Audience size, overlap |
| Ad (Creative) | Hook rate (3s view), Hold rate, CTR | Engagement rate, save rate |
Reading Meta Ads:
High spend + low impressions → CPM high → audience too narrow or auction-pressured
High impressions + low clicks → CTR low → creative not compelling
High clicks + low leads → LP problem or form too long
High leads + low bookings → poor lead quality or weak nurtureTikTok Ads
| Level | Primary metrics | Secondary metrics |
|---|---|---|
| Account | Spend, CPA, ROAS | Total impressions |
| Campaign | CPM, Cost per result | Campaign type performance |
| Ad Group | CPC, CTR, Conv rate | Audience size, age/gender split |
| Ad (Video) | 2s view rate, 6s view rate, completion rate | Like, comment, share |
Reading TikTok Ads:
2s view rate low → weak hook — first 3 seconds aren't strong enough
6s view rate low → losing attention after the hook
Completion rate low + CTR low → video doesn't drive action
CPV high → wrong audience, or video doesn't fit TikTok formatGoogle Analytics 4
| Metric group | Metric | Meaning |
|---|---|---|
| Acquisition | Users, Sessions, Source/Medium | Traffic origin |
| Engagement | Engagement rate, Time on page, Pages/session | Traffic quality |
| Conversion | Conv rate, Events (form submit, click CTA) | Conversion effectiveness |
| Retention | Returning users, User retention | Stickiness |
Reading GA4:
Traffic up + engagement down → low-quality traffic, filter sources
Traffic up + conversions down → LP problem or wrong-intent traffic
Bounce rate high (>70%) on one page → mismatch with ad copy or slow loadE-commerce Attribution Tools (Dropshipping/DTC)
For dropshipping or DTC stores, native ad-platform metrics often diverge from real revenue. Use one of these:
| Tool | Best for | Key feature |
|---|---|---|
| Triple Whale | Shopify DTC | Pixel-based attribution, blended ROAS, AI insights |
| Hyros | Info products + DTC | Server-side tracking, long-window attribution |
| Northbeam | High-spend DTC ($100K+/mo) | MTA + MMM, incrementality testing |
| Polar Analytics | Mid-market DTC | All-in-one dashboards, source-of-truth tracking |
| Wicked Reports | Email-heavy DTC | Multi-touch attribution including email |
Cross-checking: when Meta reports 5x ROAS but Shopify reports 2x ROAS, trust the platform-of-record (Shopify). The gap is usually iOS 14+ attribution loss.
Spreadsheet Data (Manual)
When user pastes data from a sheet:
- Identify core columns: date, channel, spend, units (impressions/clicks/leads/orders), revenue
- Compute derived metrics: CPL, CPA, ROAS, conversion rate
- Sort by time to surface trends
- Group by channel/campaign for comparison
Trend Detection
Week over Week (WoW)
| Metric | Prior week | This week | Change | Status |
|---|---|---|---|---|
| [Metric] | [Value] | [Value] | [+/- %] | [Normal / Watch / Alert] |
Alert thresholds:
- 10–20% change → monitor, no action yet
- 20–40% change → investigate, prepare a response
40% change → act now
Month over Month (MoM)
| Metric | Prior month | This month | Change | vs Industry benchmark |
|---|---|---|---|---|
| [Metric] | [Value] | [Value] | [+/- %] | [Above/Below industry avg] |
Seasonality (Global)
| Period | Impact | Adjustment |
|---|---|---|
| Q4 holiday (US: Black Friday → Christmas) | CPM +30–50%, conversion up | Increase budget; book inventory early; lock LPs |
| Chinese New Year | Asia logistics paused, CPM +20% in APAC | Move launches before/after; warn customers about shipping |
| Back-to-school (US: Aug; UK: Sep) | CPM +10–15% (education/electronics) | Plan from June |
| Valentine's, Mother's Day, Father's Day | CPM +15–25% (gifting niches) | Run campaigns 1 week before |
| Summer (Northern hemisphere: Jun–Aug) | CPM dips 10–15% in many verticals | Test creative, scale new channels |
| Ramadan / Eid (varies by year) | MENA conversion shifts | Adjust tone, timing — engagement spikes after iftar |
Anomaly Detection (Decision Trees)
CPL Spike
CPL up
├── CTR down? → Creative fatigue → Refresh creative
├── CTR normal + Conv rate down? → LP issue
│ ├── Slow load? → Check PageSpeed
│ ├── Form broken? → Test form on mobile
│ └── Wrong intent traffic? → Audit audience targeting
└── CPM up? → Auction pressure or seasonality
├── Holiday / sale season? → Increase budget or pause
└── Competitor spend up? → Switch audience or channelROAS Drop
ROAS down
├── Revenue down + spend flat? → Conversion problem
│ ├── Lead quality poor? → Check audience
│ ├── Sales team slow? → Check response time
│ └── Pricing changed? → Audit pricing
├── Revenue flat + spend up? → Over-spending
│ ├── Scaled too fast? → Reduce, max 20%/day increase
│ └── New channel not optimized? → Stop scaling, optimize first
└── Both down? → Systemic issue
├── Competitor running big promo? → Competitor scan
└── Off-season? → Check seasonalityEngagement Drop
Engagement down
├── Reach down? → Algo de-prioritized
│ ├── Too many promo posts? → Increase educational/entertainment ratio
│ └── Posting too often? → Reduce frequency
├── Reach normal + ER down? → Content not compelling
│ ├── Stale format? → Try new formats (carousel, POV, duet)
│ └── Repetitive topics? → Rotate angles per content matrix
└── Reach up + ER down? → Wrong audience reachingCohort Analysis
Monthly Cohort Template
| Cohort (signup month) | Month 1 | Month 2 | Month 3 | Month 6 | Month 12 |
|---|---|---|---|---|---|
| Jan 2026 (100 customers) | 100% | [X%] active | [X%] | [X%] | [X%] |
| Feb 2026 (120 customers) | 100% | [X%] | [X%] | [X%] | — |
| Mar 2026 (95 customers) | 100% | [X%] | [X%] | — | — |
Reading:
- Steady decline across months → natural churn, build retention program
- Sharp drop in month 2 → bad first experience, fix onboarding
- Stable from month 3 → retention floor reached, focus on this segment
Show full SKILL.md (389 more words)Show less
Cohort by Acquisition Source
| Source | Customers | CAC | LTV 90 days | LTV:CAC |
|---|---|---|---|---|
| Meta Ads | [X] | [X] | [X] | [X:1] |
| TikTok Ads | [X] | [X] | [X] | [X:1] |
| Organic | [X] | [X] | [X] | [X:1] |
| Referral | [X] | [X] | [X] | [X:1] |
| [X] | [X] | [X] | [X:1] |
Healthy LTV:CAC is generally 3:1 or better.
Attribution Models
Comparing 3 Models
| Model | How it credits | When to use |
|---|---|---|
| Last Click | 100% to final touch | Default, simple, short funnels |
| First Click | 100% to first touch | Evaluating TOFU/awareness channels |
| Linear | Equal split across all touches | Long funnels, multi-channel, fair credit |
Attribution comparison template:
| Channel | Last Click | First Click | Linear | Note |
|---|---|---|---|---|
| Meta Ads | [X orders] | [X orders] | [X orders] | [Role: TOFU/BOFU?] |
| TikTok Ads | [X orders] | [X orders] | [X orders] | [Role?] |
| Google Search | [X orders] | [X orders] | [X orders] | [Role?] |
| Organic | [X orders] | [X orders] | [X orders] | [Role?] |
| [X orders] | [X orders] | [X orders] | [Role?] |
Recommendations:
- Short funnel (1–3 days): Last Click works
- Medium funnel (7–14 days): use Linear
- Long funnel (30+ days): First Click for TOFU, Last Click for BOFU
- DTC/dropshipping at scale: switch to a dedicated tool (Triple Whale, Hyros, Northbeam)
Output Template
# Data Analysis Report — [Brand/Campaign]
Period: [Start] — [End]
Data sources: [Meta Ads / TikTok Ads / GA4 / Shopify / ...]
Analysis date: [YYYY-MM-DD]
---
## 1. Executive Summary
**3 most important insights:**
1. [Insight 1 — written as judgment, not raw numbers]
2. [Insight 2]
3. [Insight 3]
**Overall status:** [Green = stable | Yellow = monitor | Red = urgent action]
---
## 2. Descriptive — What happened?
### Top-line metrics
| Metric | This period | Prior period | Change | Industry benchmark | Status |
|--------|-------------|--------------|--------|--------------------|--------|
| Spend | [X] | [X] | [+/- %] | — | [icon] |
| Impressions | [X] | [X] | [+/- %] | — | [icon] |
| Clicks | [X] | [X] | [+/- %] | — | [icon] |
| CTR | [X%] | [X%] | [+/- %] | [X%] | [icon] |
| Leads | [X] | [X] | [+/- %] | — | [icon] |
| CPL | [X] | [X] | [+/- %] | [X] | [icon] |
| ROAS | [Xx] | [Xx] | [+/- %] | [Xx] | [icon] |
### Performance by channel
| Channel | Spend | Leads | CPL | ROAS | % of budget | Note |
|---------|-------|-------|-----|------|-------------|------|
| Meta Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |
| TikTok Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |
| Google Ads | [X] | [X] | [X] | [Xx] | [X%] | [1 sentence] |
### Top 5 campaigns
| Campaign | Spend | Leads | CPL | ROAS | Note |
|----------|-------|-------|-----|------|------|
| 1. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 2. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 3. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 4. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
| 5. [Name] | [X] | [X] | [X] | [Xx] | [1 sentence] |
### Top 3 creatives
| Creative | Format | Hook rate | CTR | CPL | Days running | Note |
|----------|--------|-----------|-----|-----|--------------|------|
| 1. [Name/desc] | [Video/Image/Carousel] | [X%] | [X%] | [X] | [X days] | [1 sentence] |
| 2. [Name/desc] | [Format] | [X%] | [X%] | [X] | [X days] | [1 sentence] |
| 3. [Name/desc] | [Format] | [X%] | [X%] | [X] | [X days] | [1 sentence] |
---
## 3. Diagnostic — Why?
### What's working — why?
- [Cause 1 + supporting data]
- [Cause 2 + supporting data]
### What's not — why?
- [Cause 1 + supporting data + remedy]
- [Cause 2 + supporting data + remedy]
### Anomalies to investigate
- [Anomaly 1 — description + likely cause + investigation step]
- [Anomaly 2]
---
## 4. Predictive — Forecast
### Next period (3 scenarios)
| Metric | Bear | Base | Bull |
|--------|------|------|------|
| Spend | [X] | [X] | [X] |
| Leads | [X] | [X] | [X] |
| CPL | [X] | [X] | [X] |
| ROAS | [Xx] | [Xx] | [Xx] |
| Revenue | [X] | [X] | [X] |
### Forecast drivers
- [Driver 1: seasonality, competitor, algo change, ...]
- [Driver 2]
---
## 5. Prescriptive — Actions
### Act now (next 48h)
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |
### This week
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |
### This month
| # | Action | Owner | Deadline | Measure by |
|---|--------|-------|----------|------------|
| 1 | [Specific action] | [Role] | [Date] | [Metric] |
| 2 | [Specific action] | [Role] | [Date] | [Metric] |Auto-Diagnostics
When analyzing, automatically check these conditions:
| Condition | Check | Action |
|---|---|---|
| CPL up > 30% WoW | Creative running > 14 days? Frequency > 3? | Refresh creative, rotate audience |
| CTR < 0.8% | Strong 3s hook? Eye-catching imagery? | A/B test hooks, change opening frame |
| ROAS < 2x for 7 days | Right audience? LP conv rate? | Narrow audience, audit LP |
| LP conv rate < 3% | Load time? Form length? CTA clarity? | Trigger skill 12-landing-page-brief-global |
| Frequency > 4 | Audience saturated | Expand audience or switch channel |
| Spend < 70% of budget | Audience too narrow or bid too low | Expand audience, raise bid |
| One channel > 60% spend | Single-channel dependency risk | Reallocate, test new channel |
Skill Cross-references
03-performance-review-global— broader marketing performance review07-marketing-report-global— turn analysis into stakeholder-ready monthly/quarterly report10-reverse-kpi-calc-global— recompute KPIs and budget from real data12-landing-page-brief-global— when LP conversion is the bottleneck05-ad-copy-global— when creative is the bottleneck15-social-listening-global— add qualitative data (sentiment, trends) alongside quantitative
Quality Checklist
Before delivering the report
- Every insight has supporting data
- Every number is compared (WoW, MoM, or vs benchmark)
- Anomalies (> 20% change) flagged and explained
- Recommendations specify: owner, deadline, success metric
- Forecast includes 3 scenarios (bear, base, bull)
- No raw numbers without interpretation
- Source and time window are clearly stated
- Cross-checked: ad-platform spend matches actual spend
- For dropshipping/DTC: revenue cross-checked between Shopify and ad platform
© minhnv0807, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/en/13-data-analysis-global of minhnv0807/ai-business-skills.
Open the folder on GitHubat commit 0360adc
Compare with similar skills
13 Data Analysis Global next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| 13 Data Analysis Global this skillminhnv0807/ai-business-skills | 608 | — | ~4k | Automated safety check: Pass | MIT | |
| Analytics Tracking Automationjtrackingai/analytics-tracking-automation | 142 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Analytics Tracking Testingpetrkindlmann/qa-skills | 165 | — | ~6.2k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 83k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Raccoon DataanalysisSenseTime-Copilot/raccoon-dataanalysis-skill | 137 | — | ~1.9k | Automated safety check: Pass | None | |
| Money Adsiamzifei/show-me-the-money | 1k | — | ~2.4k | Automated safety check: Pass | Custom licence |
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Questions about 13 Data Analysis Global
What does 13 Data Analysis Global do?
A skill your agent uses when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and…. 13 Data Analysis Global is an agent skill from minhnv0807/ai-business-skills. Use when raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log.
When should I use 13 Data Analysis Global?
13 Data Analysis Global fits situations like: raw data exists — Meta; A spreadsheet — and has to become insight and decisions: descriptive; prescriptive layers; cuts by channel.
How do I install 13 Data Analysis Global in Claude Code?
Run `npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a claude-code`. Or copy the skill folder (skills/en/13-data-analysis-global in minhnv0807/ai-business-skills) into .claude/skills/13-data-analysis-global in your project. Claude Code loads it when a task matches its description.
How do I install 13 Data Analysis Global in Codex?
Run `npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a codex`. Or copy the skill folder (skills/en/13-data-analysis-global in minhnv0807/ai-business-skills) into .agents/skills/13-data-analysis-global in your project. Codex loads it when a task matches its description.
Can I use 13 Data Analysis Global in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add minhnv0807/ai-business-skills --skill 13-data-analysis-global -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/13-data-analysis-global, .gemini/skills/13-data-analysis-global, .github/skills/13-data-analysis-global and .opencode/skills/13-data-analysis-global in your project.
What does 13 Data Analysis Global need to run?
SKILL.md names no scripts, command-line tools or credentials: 13 Data Analysis Global is instructions for the agent only.
Does 13 Data Analysis Global access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is 13 Data Analysis Global safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does 13 Data Analysis Global use?
13 Data Analysis Global is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does 13 Data Analysis Global use?
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to 13 Data Analysis Global?
Skills that share tags, products or a category with 13 Data Analysis Global: Analytics Tracking Automation (jtrackingai/analytics-tracking-automation, 142 stars), Analytics Tracking Testing (petrkindlmann/qa-skills, 165 stars), Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars) and Raccoon Dataanalysis (SenseTime-Copilot/raccoon-dataanalysis-skill, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains 13 Data Analysis Global?
minhnv0807 (a GitHub user) maintains it in minhnv0807/ai-business-skills, which has 608 GitHub stars. The repository holds 142 skills in this directory. The repository was last updated on September 12, 2026.
Source: minhnv0807/ai-business-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.