Ad Creative
LeoYeAI/openclaw-marketing-skills
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform.
Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities
Install Ads Audit
$ npx skills add zubair-trabzada/ai-ads-claude --skill ads-audit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zubair-trabzada/ai-ads-claude ads-audit --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/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads-audit .claude/skills/ads-audit && 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 "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .claude/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-auditType 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 zubair-trabzada/ai-ads-claude --skill ads-audit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zubair-trabzada/ai-ads-claude ads-audit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ads-audit .agents/skills/ads-audit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .agents/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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 zubair-trabzada/ai-ads-claude --skill ads-audit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zubair-trabzada/ai-ads-claude ads-audit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ads-audit .cursor/skills/ads-audit && 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 "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .cursor/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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/zubair-trabzada/ai-ads-claude.git --path skills/ads-audit--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 zubair-trabzada/ai-ads-claude --skill ads-audit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zubair-trabzada/ai-ads-claude ads-audit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ads-audit .gemini/skills/ads-audit && 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 "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .gemini/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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 zubair-trabzada/ai-ads-claude ads-auditInstalls 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 zubair-trabzada/ai-ads-claude --skill ads-audit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ads-audit .github/skills/ads-audit && 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 "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .github/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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 zubair-trabzada/ai-ads-claude --skill ads-audit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zubair-trabzada/ai-ads-claude ads-audit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-ads-claude.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ads-audit .opencode/skills/ads-audit && 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 "ads-audit" agent skill from https://github.com/zubair-trabzada/ai-ads-claude/tree/main/skills/ads-audit into .opencode/skills/ads-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ads-audit", 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
ads-audit- GitHub stars
- 268
- Token cost
- ~3.5k tokens
- SKILL.md length
- 1,300 words
- Files
- 1
- Skills in repo
- 9
- Repo updated
- First seen
- Licence
- MIT
At a glance
Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities
- Works in 6 steps: Gather Performance Data → Establish Industry Benchmarks → Performance Analysis → …
- Tasks that involve Paid advertising
- SKILL.md covers Skill Purpose, When to Use, Data Collection and Output Format, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Ads Audit is an agent skill from zubair-trabzada/ai-ads-claude. Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities
Its SKILL.md is about 3.5k 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 Marketing & SEO, covering Paid advertising. The repository describes itself as: AI-powered advertising strategy engine for Claude Code. Build complete ad strategies, generate platform-specific copy (Google, Meta, LinkedIn, TikTok, YouTube, Pinterest), design… The licence is MIT.
When your agent uses it
- Tasks that involve Paid advertising
Example prompts
- “/ads-audit”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit d1df3f5. 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
Ads Audit loads about 3.5k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 1,300 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 zubair-trabzada/ai-ads-claude at commit d1df3f5, republished under its MIT licence (© zubair-trabzada). 1,300 words, ~3,513 tokens.
Download SKILL.mdSave it as
.claude/skills/ads-audit/SKILL.md (or your agent's skills folder).- name
- ads-audit
- description
- Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities
- version
- 1.0.0
- author
- AI Ads Strategist
- tags
- ads, audit, performance, optimization, analytics
- trigger
- /ads audit
- output
- ADS-AUDIT.md
Ad Performance Auditor
Skill Purpose
Analyze existing ad campaign performance from user-provided data (screenshots, descriptions, or metrics). Evaluate key performance indicators against industry benchmarks, detect creative fatigue and audience overlap, identify budget waste, and provide prioritized optimization recommendations ranked by expected impact. This is a diagnostic tool — it tells you what is working, what is broken, and what to fix first.
When to Use
- User wants to audit their existing ad campaigns
- User shares ad performance data (screenshots, CSV, or described metrics)
- User asks "why aren't my ads working?" or "how can I improve my ads?"
- User wants to know if their ad spend is efficient
- User has been running ads for 7+ days and wants a performance check
- Triggered by
/ads auditor/ads audit <platform>
Data Collection
Step 1: Gather Performance Data
Ask the user to provide their ad data in any of these formats:
Option A: Key Metrics (Manual Input) Ask for these metrics per campaign/ad set:
| Metric | What to Ask |
|---|---|
| Platform | Which platform (Meta, Google, TikTok, LinkedIn, etc.)? |
| Campaign Objective | What's the campaign optimized for (awareness, traffic, conversions, leads)? |
| Time Period | How long has this campaign been running? Date range? |
| Spend | Total amount spent in this period |
| Impressions | Total impressions |
| Reach | Unique people reached (if available) |
| Clicks | Total clicks (link clicks, not all clicks) |
| CTR | Click-through rate (or calculate from impressions/clicks) |
| CPC | Cost per click |
| Conversions | Total conversions (purchases, leads, sign-ups) |
| Conversion Rate | Landing page conversion rate |
| CPA/CPL | Cost per acquisition or cost per lead |
| ROAS | Return on ad spend (revenue / spend) |
| Frequency | Average times each person saw the ad |
| Ad Creative Type | Image, video, carousel, etc. |
Option B: Screenshot Analysis If the user shares screenshots of their ad dashboard:
- Extract all visible metrics from the screenshot
- Note which metrics are missing
- Ask follow-up questions for critical missing data
Option C: Platform-Specific Export Guide the user to export data:
- Meta: Ads Manager > Export > Select columns
- Google: Reports > Download
- TikTok: Analytics > Export
- LinkedIn: Campaign Manager > Export
Step 2: Establish Industry Benchmarks
Use these benchmark ranges by platform and objective. Compare the user's metrics against these:
Meta (Facebook/Instagram) Benchmarks:
| Metric | Poor | Below Average | Average | Good | Excellent |
|---|---|---|---|---|---|
| CTR (Link) | <0.5% | 0.5-0.8% | 0.8-1.2% | 1.2-2.0% | >2.0% |
| CPC (Link) | >$3.00 | $2.00-3.00 | $1.00-2.00 | $0.50-1.00 | <$0.50 |
| CPM | >$20 | $15-20 | $10-15 | $5-10 | <$5 |
| Conv. Rate | <1% | 1-2% | 2-5% | 5-10% | >10% |
| ROAS | <1x | 1-2x | 2-4x | 4-8x | >8x |
| Frequency (7d) | >5 | 3-5 | 2-3 | 1.5-2 | 1-1.5 |
Google Ads (Search) Benchmarks:
| Metric | Poor | Below Average | Average | Good | Excellent |
|---|---|---|---|---|---|
| CTR | <1% | 1-2% | 2-4% | 4-7% | >7% |
| CPC | >$5.00 | $3.00-5.00 | $1.50-3.00 | $0.75-1.50 | <$0.75 |
| Conv. Rate | <1% | 1-3% | 3-6% | 6-10% | >10% |
| Quality Score | 1-3 | 4-5 | 6-7 | 8-9 | 10 |
Google Ads (Display) Benchmarks:
| Metric | Poor | Below Average | Average | Good | Excellent |
|---|---|---|---|---|---|
| CTR | <0.1% | 0.1-0.3% | 0.3-0.5% | 0.5-1.0% | >1.0% |
| CPC | >$2.00 | $1.00-2.00 | $0.50-1.00 | $0.25-0.50 | <$0.25 |
| Conv. Rate | <0.5% | 0.5-1% | 1-2% | 2-4% | >4% |
TikTok Benchmarks:
| Metric | Poor | Below Average | Average | Good | Excellent |
|---|---|---|---|---|---|
| CTR | <0.3% | 0.3-0.5% | 0.5-1.0% | 1.0-2.0% | >2.0% |
| CPC | >$2.00 | $1.00-2.00 | $0.50-1.00 | $0.25-0.50 | <$0.25 |
| CPM | >$15 | $10-15 | $6-10 | $3-6 | <$3 |
| Video View Rate | <15% | 15-25% | 25-35% | 35-50% | >50% |
LinkedIn Benchmarks:
| Metric | Poor | Below Average | Average | Good | Excellent |
|---|---|---|---|---|---|
| CTR | <0.2% | 0.2-0.4% | 0.4-0.7% | 0.7-1.0% | >1.0% |
| CPC | >$10.00 | $7.00-10.00 | $4.00-7.00 | $2.00-4.00 | <$2.00 |
| CPM | >$50 | $35-50 | $20-35 | $10-20 | <$10 |
| Conv. Rate | <1% | 1-2% | 2-4% | 4-7% | >7% |
Industry Vertical Adjustments:
- E-commerce: CPA benchmarks $15-$45, ROAS target 3-5x
- SaaS/B2B: CPA benchmarks $50-$200, focus on CPL not CPA
- Local Services: CPA benchmarks $20-$75, call tracking critical
- Education/Courses: CPA benchmarks $30-$100, webinar reg focus
- Real Estate: CPL benchmarks $20-$80, lead quality matters more than volume
Step 3: Performance Analysis
Run each of these diagnostic checks:
3A: Efficiency Analysis
Calculate and evaluate:
- Cost Efficiency Score: How much value per dollar spent?
- Click Efficiency: CTR vs benchmark for this platform/industry
- Conversion Efficiency: Conv rate vs benchmark
- ROAS Check: Is the return justifying the spend?
- Budget Utilization: Is the daily budget being fully spent? Underspending signals targeting too narrow.
3B: Creative Fatigue Detection
Identify if ads have fatigued:
| Signal | Threshold | Status |
|---|---|---|
| CTR decline | >20% drop over 7 days | Fatigued |
| Frequency | >3 in 7 days | Oversaturated |
| CPC increase | >30% increase over 7 days | Auction pressure |
| Relevance/Quality dropping | Score declining | Creative stale |
| Conversion rate declining | >15% drop | Message exhausted |
Creative Fatigue Diagnosis:
- Mild fatigue: CTR dropped 10-20%, frequency 2-3 → Refresh ad copy and images
- Moderate fatigue: CTR dropped 20-40%, frequency 3-5 → New creative concepts needed
- Severe fatigue: CTR dropped >40%, frequency >5 → Pause and relaunch with entirely new creative
3C: Audience Analysis
Evaluate targeting effectiveness:
- Audience Size: Too broad (>10M on Meta) or too narrow (<100K)?
- Audience Overlap: Multiple ad sets targeting the same people?
- Demographic Performance: Which age/gender/location segments convert best?
- Placement Performance: Which placements (feed, stories, reels, search, display) perform best?
- Device Performance: Mobile vs desktop conversion differences?
Show full SKILL.md (521 more words)Show less
3D: Budget Waste Identification
Find where money is being wasted:
| Waste Type | How to Detect | Fix |
|---|---|---|
| Audience Overlap | Multiple ad sets with >30% audience overlap | Consolidate or exclude |
| Poor Placements | Placements with high spend, low conversion | Exclude underperformers |
| Time-of-Day Waste | Spending during low-converting hours | Dayparting schedule |
| Geographic Waste | Spending in non-converting locations | Geo-targeting refinement |
| Device Mismatch | High mobile clicks but desktop-only landing page | Mobile-optimize LP |
| Broad Match Waste | (Google) Irrelevant search terms eating budget | Negative keywords |
| Frequency Cap Missing | Same person seeing ad 10+ times | Set frequency cap |
| Low Quality Score | (Google) Score <5 driving up CPC | Improve relevance |
3E: Funnel Leak Detection
Identify where conversions are being lost:
Impressions (100%) → Clicks (CTR: X%) → Landing Page Views (X%) → Conversions (X%)
^ ^
| |
LP Load Speed? LP Conversion?
Bounce Rate? Form Friction?
Mobile UX? Offer Mismatch?Calculate drop-off at each stage:
- Impression to Click: Is the ad compelling? (Creative issue)
- Click to Landing Page View: Is the page loading? (Technical issue)
- Landing Page View to Conversion: Is the offer converting? (Offer/UX issue)
Step 4: Scoring
Overall Ad Performance Score (0-100):
| Dimension | Weight | Score Range | Assessment |
|---|---|---|---|
| Cost Efficiency | 25% | 0-25 | CPC, CPM, CPA vs benchmarks |
| Creative Performance | 20% | 0-20 | CTR, engagement, fatigue level |
| Conversion Quality | 25% | 0-25 | Conv rate, ROAS, lead quality |
| Audience Targeting | 15% | 0-15 | Targeting precision, overlap, segmentation |
| Budget Optimization | 15% | 0-15 | Budget utilization, waste, allocation |
Score Interpretation:
- 80-100: Excellent — minor optimizations only
- 60-79: Good — solid foundation with optimization opportunities
- 40-59: Needs Work — significant improvements needed in multiple areas
- 20-39: Poor — fundamental strategy issues to address
- 0-19: Critical — major problems, consider pausing and restructuring
Step 5: Generate Recommendations
Produce recommendations in 3 tiers, ordered by expected impact:
Tier 1: Immediate Actions (This Week) Actions that can be done today with no additional resources:
- Pause underperforming ad sets
- Adjust budgets toward top performers
- Add negative keywords (Google)
- Adjust frequency caps
- Fix audience overlap
- Update ad copy with top-performing hooks
Tier 2: Short-Term Optimizations (Next 2 Weeks) Actions that require some creative or strategic work:
- Launch new creative variations
- Test new audiences
- A/B test landing pages
- Implement retargeting
- Adjust bidding strategy
- Expand to new placements
Tier 3: Strategic Changes (Next 30 Days) Actions that require planning and significant effort:
- Restructure campaign architecture
- Build new funnel stages
- Create new creative concepts
- Expand to new platforms
- Redesign landing pages
- Implement conversion tracking improvements
Step 6: Projected Impact
For each Tier 1 recommendation, estimate:
| Action | Current Metric | Expected Improvement | Confidence |
|---|---|---|---|
| [action] | [current value] | [projected value] | High/Medium/Low |
Output Format
Save as ADS-AUDIT.md in the current working directory.
# Ad Performance Audit: [Business/Campaign Name]
**Audit Date:** [Date]
**Period Analyzed:** [Date Range]
**Platform(s):** [Platforms]
**Total Spend Analyzed:** $[Amount]
---
## Overall Ad Performance Score: [X]/100
[Visual score bar or assessment]
## Executive Summary
- **Top Finding:** [Single most impactful finding]
- **Biggest Waste:** $[Amount] wasted on [what]
- **Biggest Opportunity:** [What could improve results most]
- **Estimated Improvement:** [X]% improvement possible with Tier 1 changes
---
## Performance Dashboard
### Key Metrics vs Benchmarks
| Metric | Your Value | Industry Avg | Status | Gap |
|--------|-----------|-------------|--------|-----|
| CTR | X% | X% | [emoji-free status] | +/-X% |
| CPC | $X | $X | [status] | +/-$X |
| Conv Rate | X% | X% | [status] | +/-X% |
| ROAS | Xx | Xx | [status] | +/-Xx |
| Frequency | X | X | [status] | +/-X |
### Scoring Breakdown
| Dimension | Score | Key Finding |
|-----------|-------|-------------|
| Cost Efficiency | X/25 | [finding] |
| Creative Performance | X/20 | [finding] |
| Conversion Quality | X/25 | [finding] |
| Audience Targeting | X/15 | [finding] |
| Budget Optimization | X/15 | [finding] |
---
## Detailed Analysis
### Creative Fatigue Assessment
[Fatigue level, evidence, recommendation]
### Audience Analysis
[Targeting assessment, overlap issues, segment performance]
### Budget Waste Report
| Waste Type | Estimated Waste | Fix |
|------------|----------------|-----|
| [type] | $[amount] | [fix] |
### Funnel Leak Analysis
[Stage-by-stage drop-off analysis]
---
## Recommendations
### Tier 1: Immediate Actions (This Week)
| # | Action | Expected Impact | Effort |
|---|--------|----------------|--------|
| 1 | [action] | [impact] | Low |
### Tier 2: Short-Term (Next 2 Weeks)
| # | Action | Expected Impact | Effort |
|---|--------|----------------|--------|
| 1 | [action] | [impact] | Medium |
### Tier 3: Strategic (Next 30 Days)
| # | Action | Expected Impact | Effort |
|---|--------|----------------|--------|
| 1 | [action] | [impact] | High |
---
## Projected Impact of Tier 1 Changes
| Action | Current | Projected | Confidence |
|--------|---------|-----------|------------|
| [action] | [value] | [value] | High/Med/Low |
## Next Steps
1. Implement Tier 1 actions immediately
2. Re-audit in 7 days with `/ads audit`
3. Use `/ads creative` to generate fresh creative if fatigue detected
4. Use `/ads funnel` to restructure campaign architecture if needed
5. Generate new copy with `/ads copy <platform>`Important Rules
- Never make assumptions about missing data — ask for it or flag it as unavailable
- Always compare to platform-specific benchmarks, not cross-platform averages
- Industry vertical matters — adjust benchmarks accordingly
- Frequency is one of the most overlooked metrics — always check it
- ROAS alone is not enough — consider contribution margin and LTV
- Budget waste identification should always include estimated dollar amounts
- Recommendations must be specific and actionable, not generic best practices
- Tier 1 recommendations should be achievable without additional creative assets
- Always consider seasonality when evaluating performance trends
- If data is insufficient for a thorough audit, say so clearly and specify what is needed
- A declining CTR with stable conversions might be fine — context matters
- Never recommend increasing budget on a campaign that has fundamental creative/targeting issues
© zubair-trabzada, 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/ads-audit of zubair-trabzada/ai-ads-claude.
Open the folder on GitHubat commit d1df3f5
Compare with similar skills
Ads Audit 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 |
|---|---|---|---|---|---|---|
| Ads Audit this skillzubair-trabzada/ai-ads-claude | 268 | — | ~3.5k | Automated safety check: Pass | MIT | |
| Ad CreativeLeoYeAI/openclaw-marketing-skills | 1k | 8 repos | ~3.4k | Automated safety check: Pass | Custom licence | |
| Blog GoogleAgriciDaniel/claude-blog | 2.3k | 1 repos | ~3.3k | Automated safety check: Notes | MIT | |
| Ad Account Auditoraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Ad Creative Builderaaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Ad Test Designeraaron-he-zhu/aaron-marketing-skills | 2.9k | 2 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 |
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aaron-he-zhu/aaron-marketing-skills
A skill your agent uses when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"…
Paid Ads Audit
AgriciDaniel/claude-ads
Runs a source-grounded paid advertising audit across up to 12 ad platforms, with parallel platform workers, deterministic scoring and a versioned JSON bundle.
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Budget Allocation & ROI Projector. An agent skill from zubair-trabzada/ai-ads-claude.
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Competitive Ad Intelligence. An agent skill from zubair-trabzada/ai-ads-claude.
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Creative Brief Generator for designers, video editors, and content teams
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Landing Page Audit & Optimizer. An agent skill from zubair-trabzada/ai-ads-claude.
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60-Second Ad Readiness Snapshot — quick assessment without subagents
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Professional PDF Ad Strategy Report Generator. An agent skill from zubair-trabzada/ai-ads-claude.
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Questions about Ads Audit
What does Ads Audit do?
Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities. Ads Audit is an agent skill from zubair-trabzada/ai-ads-claude.
When should I use Ads Audit?
Ads Audit fits situations like: tasks that involve Paid advertising.
How do I install Ads Audit in Claude Code?
Run `npx skills add zubair-trabzada/ai-ads-claude --skill ads-audit -a claude-code`. Or copy the skill folder (skills/ads-audit in zubair-trabzada/ai-ads-claude) into .claude/skills/ads-audit in your project. Claude Code loads it when a task matches its description.
How do I install Ads Audit in Codex?
Run `npx skills add zubair-trabzada/ai-ads-claude --skill ads-audit -a codex`. Or copy the skill folder (skills/ads-audit in zubair-trabzada/ai-ads-claude) into .agents/skills/ads-audit in your project. Codex loads it when a task matches its description.
Can I use Ads Audit 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 zubair-trabzada/ai-ads-claude --skill ads-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ads-audit, .gemini/skills/ads-audit, .github/skills/ads-audit and .opencode/skills/ads-audit in your project.
What does Ads Audit need to run?
SKILL.md names no scripts, command-line tools or credentials: Ads Audit is instructions for the agent only.
Does Ads Audit 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 Ads Audit 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 Ads Audit use?
Ads Audit 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 Ads Audit use?
About 3.5k tokens (SKILL.md is roughly 14k 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 Ads Audit?
Skills that share tags, products or a category with Ads Audit: Ad Creative (LeoYeAI/openclaw-marketing-skills, 1k stars), Blog Google (AgriciDaniel/claude-blog, 2.3k stars), Ad Account Auditor (aaron-he-zhu/aaron-marketing-skills, 2.9k stars) and Ad Creative Builder (aaron-he-zhu/aaron-marketing-skills, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ads Audit?
zubair-trabzada (a GitHub user) maintains it in zubair-trabzada/ai-ads-claude, which has 268 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on April 8, 2026.
Source: zubair-trabzada/ai-ads-claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.