Comprehensive ad account analysis across all major platforms (Google, Meta,
Works with
LinkedIn, TikTok, Microsoft). Orchestrates 17 specialized sub-skills and
10 agents (6 audit + 4 creative).
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionadsExecute the skills CLI command in your project's root directory to begin installation:
Fetches ads from agricidaniel/claude-ads and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ads. Access via /ads in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Comprehensive ad account analysis across all major platforms (Google, Meta, LinkedIn, TikTok, Microsoft). Orchestrates 17 specialized sub-skills and 10 agents (6 audit + 4 creative).
| Command | What it does |
|---|---|
/ads audit |
Full multi-platform audit with parallel subagent delegation |
/ads google |
Google Ads deep analysis (Search, PMax, YouTube) |
/ads meta |
Meta Ads deep analysis (FB, IG, Advantage+) |
/ads youtube |
YouTube Ads specific analysis |
/ads linkedin |
LinkedIn Ads deep analysis (B2B, Lead Gen) |
/ads tiktok |
TikTok Ads deep analysis (Creative, Shop, Smart+) |
/ads microsoft |
Microsoft/Bing Ads deep analysis (Copilot, Import) |
/ads creative |
Cross-platform creative quality audit |
/ads landing |
Landing page quality assessment for ad campaigns |
/ads budget |
Budget allocation and bidding strategy review |
/ads plan <business-type> |
Strategic ad plan with industry templates |
/ads apple |
Apple Search Ads (ASA) deep analysis |
/ads competitor |
Competitor ad intelligence analysis |
/ads dna <url> |
Extract brand DNA from website, outputs brand-profile.json |
/ads create |
Generate campaign concepts + copy briefs, outputs campaign-brief.md |
/ads generate |
Generate AI ad images from brief, outputs to ad-assets/ |
/ads photoshoot |
Product photography in 5 styles (Studio, Floating, Ingredient, In Use, Lifestyle) |
Before any audit or analysis, collect this context. Without it, benchmarks will be generic and recommendations may be wrong for the user's situation.
Ask these questions upfront (combine into one message):
If the user provides data upfront (e.g. "audit my Google Ads, I spend $5k/mo on SaaS"), extract context from that and proceed without re-asking.
Use the provided context to:
references/benchmarks.mdWhen the user invokes /ads audit, delegate to subagents in parallel:
context: fork: audit-google, audit-meta, audit-creative, audit-tracking, audit-budget, audit-complianceFor individual commands (/ads google, /ads meta, etc.), load the relevant
sub-skill directly. Still collect context first if not already provided.
Sequential pipeline (each step is independently runnable):
/ads dna <url> → brand-profile.json in current directory/ads create → reads profile + optional audit results → campaign-brief.md/ads generate → reads brief + profile → ad-assets/ directory/ads photoshoot → standalone or reads profile for style injectionRequires GOOGLE_API_KEY (Gemini default) or ADS_IMAGE_PROVIDER + matching key.
If API key is missing, /ads generate and /ads photoshoot display setup
instructions and exit; they never fail silently.
Detect business type from ad account signals:
Hard rules (never violate these):
Load these on-demand as needed; do NOT load all at startup.
Path resolution: All references are installed at ~/.claude/skills/ads/references/.
When sub-skills or agents reference ads/references/*.md, resolve to
~/.claude/skills/ads/references/*.md.
references/scoring-system.md: Weighted scoring algorithm and grading thresholdsreferences/benchmarks.md: Industry benchmarks by platform (CPC, CTR, CVR, ROAS)references/bidding-strategies.md: Bidding decision trees per platformreferences/budget-allocation.md: Platform selection matrix, scaling rules, MERreferences/platform-specs.md: Creative specifications across all platformsreferences/conversion-tracking.md: Pixel, CAPI, EMQ, ttclid implementationreferences/compliance.md: Regulatory requirements, ad policies, privacyreferences/google-audit.md: 74-check Google Ads audit checklistreferences/meta-audit.md: 46-check Meta Ads audit checklistreferences/linkedin-audit.md: 25-check LinkedIn Ads audit checklistreferences/tiktok-audit.md: 25-check TikTok Ads audit checklistreferences/microsoft-audit.md: 20-check Microsoft Ads audit checklistreferences/brand-dna-template.md: Brand DNA schema and extraction guidereferences/image-providers.md: Provider config (Gemini/OpenAI/Stability/Replicate)references/google-creative-specs.md: PMax/RSA/YouTube generation-ready specsreferences/meta-creative-specs.md: Feed/Reels/Stories specs + safe zonesreferences/linkedin-creative-specs.md: Single image/video B2B constraintsreferences/tiktok-creative-specs.md: 9:16 only + safe zone overlayreferences/youtube-creative-specs.md: Skippable/Bumper/Shorts/Thumbnailreferences/microsoft-creative-specs.md: Multimedia Ads + RSA subsetreferences/gaql-notes.md: GAQL field compatibility, deduplication patterns, filter scope best practicesreferences/voice-to-style.md: Brand voice axis to visual attribute mapping for image generationreferences/copy-frameworks.md: 6 ad copy frameworks (AIDA, PAS, BAB, 4P, FAB, Star-Story-Solution)Per-platform score using weighted algorithm from references/scoring-system.md.
Cross-platform aggregate weighted by budget share:
Aggregate = Sum(Platform_Score x Platform_Budget_Share)
| Grade | Score | Action Required |
|---|---|---|
| A | 90-100 | Minor optimizations only |
| B | 75-89 | Some improvement opportunities |
| C | 60-74 | Notable issues need attention |
| D | 40-59 | Significant problems present |
| F | <40 | Urgent intervention required |
This skill orchestrates 17 specialized sub-skills:
For parallel analysis during full audits:
audit-google: Google Ads checks (G01-G74)audit-meta: Meta Ads checks (M01-M46)audit-creative: Creative quality for LinkedIn, TikTok, Microsoftaudit-tracking: Conversion tracking health across all platformsaudit-budget: Budget, bidding, structure for LinkedIn, TikTok, Microsoftaudit-compliance: Compliance, settings, performance across all platformscreative-strategist: Campaign concepts from brand profile + audit results (Opus, maxTurns: 25)visual-designer: Image generation with brand injection via generate_image.py (Sonnet, maxTurns: 30)copy-writer: Headlines, CTAs, primary text within platform limits (Sonnet, maxTurns: 20)format-adapter: Asset dimension validation and spec compliance reporting (Haiku, maxTurns: 15)Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend ads for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in ads — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: ads is focused, and the summary matches what you get after install.
ads is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: ads is the kind of skill you can hand to a new teammate without a long onboarding doc.
ads fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ads is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ads has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in ads — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ads fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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