Install
$ agentstack add skill-citedy-adclaw-ads-generate ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Ads Generate: AI Ad Image Generator
Generates platform-sized ad creative images from your campaign brief and brand profile. Uses banana-claude as the image generation provider.
Quick Reference
| Command | What it does | |---------|-------------| | /ads generate | Generate all images from campaign-brief.md | | /ads generate --platform meta | Generate Meta assets only | | /ads generate --prompt "text" --ratio 9:16 | Standalone generation without brief |
Environment Setup
Required before running:
- Requires banana-claude (v1.4.1+) with nanobanana-mcp configured
- Run
/banana setupto configure API key and MCP - Fallback: if banana is not available, use
scripts/generate_image.py(deprecated)
If banana-claude is not installed, this skill will display setup instructions and stop. It will never fail silently.
Process
Step 1: Verify banana-claude
Verify banana-claude is installed (run /banana setup to check). If not installed, display setup instructions and exit.
Step 2: Locate Source Files
Check for:
campaign-brief.md→ primary source for prompts and dimensionsbrand-profile.json→ brand color/style injection (optional but recommended)
If campaign-brief.md is found: Use ## Image Generation Briefs section as the generation job list.
If no campaign-brief.md: Enter standalone mode (Step 2b).
Step 2b: Standalone Mode
Ask the user:
- Generation prompt (what should the image show?)
- Target platform (to set correct dimensions)
- Output filename (optional)
Then skip to Step 5.
Step 3: Read Provider Config
Load ads-shared/references/image-providers.md to confirm:
- Active provider pricing (show user the cost estimate)
- Rate limits for current tier
- Batch API availability
Step 4: Read Platform Specs
For each platform in the campaign brief, load the relevant spec reference:
ads-shared/references/meta-creative-specs.mdads-shared/references/google-creative-specs.mdads-shared/references/tiktok-creative-specs.mdads-shared/references/linkedin-creative-specs.mdads-shared/references/youtube-creative-specs.mdads-shared/references/microsoft-creative-specs.md
Step 5: Prepare banana Configuration
Create banana brand preset from brand-profile.json if one does not already exist at ~/.banana/presets/{brand-slug}.json.
Select banana domain mode based on campaign brief content:
- Product: e-commerce, packshots
- Editorial: brand awareness, lifestyle
- Cinema: video thumbnails, dramatic
- UI/Web: app install, SaaS
- Portrait: testimonials, people
Step 6: Spawn Visual Designer Agent
Spawn the visual-designer agent using the Task tool with context: fork, passing the selected domain mode and preset name.
The agent will:
- Parse the image generation briefs from campaign-brief.md
- Inject brand colors and mood from brand-profile.json
- Use banana-claude with the configured domain mode for each asset
- Save to
./ad-assets/[platform]/[concept]/directory structure - Write
generation-manifest.json
Step 7: Validate with Format Adapter
After the visual-designer completes, spawn the format-adapter agent with context: fork to validate dimensions and report missing formats.
Step 8: Quality Gate
Use Claude vision to assess each generated image against the brief (score 1 to 10 on brand alignment, composition, platform fit). If any image scores below 6, regenerate once with an adjusted prompt.
Step 9: Aggregate Costs
Read banana cost data from ~/.banana/costs.json and include total creative spend in generation-manifest.json.
Step 10: Report Results
Present a summary:
Generation complete:
Generated assets:
✓ ./ad-assets/meta/concept-1/feed-1080x1350.png
✓ ./ad-assets/tiktok/concept-1/vertical-1080x1920.png
✗ ./ad-assets/google/concept-1/landscape-1200x628.png [error reason]
Format validation: See format-report.md
Cost: $[N] total creative spend (from ~/.banana/costs.json)
Next steps:
1. Review assets in ./ad-assets/
2. Check format-report.md for any missing formats
3. Upload to your ad platform managers
Cost Transparency
Before generating, estimate and show the cost:
- Count the number of image briefs in campaign-brief.md
- Show estimated cost based on banana pricing tiers
- If >$1.00, ask for confirmation before proceeding
Standalone Mode (No campaign-brief.md)
When running without a campaign brief:
Platform target → dimensions used:
meta-feed → 1080×1350 (4:5)
meta-reels → 1080×1920 (9:16)
tiktok → 1080×1920 (9:16)
google-pmax → 1200×628 (1.91:1)
linkedin → 1080×1080 (1:1)
youtube → 1280×720 (16:9)
youtube-short → 1080×1920 (9:16)
Use /banana generate directly with the specified prompt and aspect ratio.
Reference Files
ads-shared/references/image-providers.md: provider config, pricing, limitsads-shared/references/[platform]-creative-specs.md: per-platform specsads-shared/references/brand-dna-template.md: brand injection schema
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: citedy
- Source: citedy/adclaw
- License: Apache-2.0
- Homepage: https://pypi.org/project/adclaw/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.