By name: ai-marketing-skills-tom-babb description: Documented AI marketing workflows for content, distribution, brand systems, and campaign execution triggers: - how do I use Tom Babb's AI marketing skills - show me the marketing skills workflows - how to scrape content for marketing - build a brand system with AI - create anti-slop prompts for content - set up AI video production workflow - implement GEO optimization strategy - use reverse prompting for images
AI Marketing Skills by Tom Babb
Skill by ara.so — Marketing Skills collection.
What This Project Does
ai-marketing-skills-with-by-tom-babb is a collection of battle-tested, repeatable AI marketing workflows built from real agency and in-house experience. Each skill is a documented process that collapses traditional multi-person marketing workflows into single-operator systems using AI tools.
The repository contains workflows for:
- Distribution: Content scraping, lead generation, and multi-format content pipelines
- Brand: Visual system creation and brand operating systems (prompts as brand guides)
- Prompting: Constraint-based prompting, anti-slop techniques, and reverse prompting
- Content & Video: AI video production pipelines and personal content engines
- Paid Advertising: Google Ads campaign generation workflows
- Discoverability: GEO (Generative Engine Optimization) strategies
Installation
This is a documentation repository, not an installable package. Clone it to access the skills:
git clone https://github.com/Tom-Babb/ai-marketing-skills-with-by-tom-babb.git
cd ai-marketing-skills-with-by-tom-babb
Each skill lives in /skills/<skill-name>/SKILL.md with its own documentation, tools list, and step-by-step process.
Repository Structure
/skills/
/knowledge-scrape-to-content/
SKILL.md
/people-scrape-to-list/
SKILL.md
/long-form-to-content-pipeline/
SKILL.md
/brand-from-references/
SKILL.md
/brand-operating-system/
SKILL.md
/constraint-prompting/
SKILL.md
/anti-slop/
SKILL.md
/reverse-prompting-for-image-gen/
SKILL.md
/ai-video-production/
SKILL.md
/personal-content-engine/
SKILL.md
/reve-world-builder/
SKILL.md
/google-ads-with-ai/
SKILL.md
/music-distribution-strategy/
SKILL.md
/geo-optimization/
SKILL.md
Key Workflows
Anti-Slop Prompting
The anti-slop workflow eliminates AI content patterns. Add this to the end of any content generation prompt:
ANTI-SLOP CONSTRAINTS:
1. SENTENCE OPENINGS: Do not start more than one sentence per paragraph with the same word. Vary structure.
2. CONTRAST FRAMING: Avoid "X, not Y" or "It's not about X, it's about Y" constructions unless absolutely necessary for clarity.
3. COLON TITLES: Do not use colons in titles or headlines unless listing items or introducing a direct quote.
4. BUZZWORD FILLER: Remove words like "ecosystem," "paradigm," "holistic," "synergy," "leverage," "robust," "revolutionary," "game-changing," "cutting-edge," "disruptive" unless they have specific technical meaning in context.
5. VAGUE CLAIMS: Every claim needs evidence. Replace "studies show" with the actual study. Replace "experts say" with the expert's name. Replace "significantly" with the number.
Output format: [your original format request]
Usage Example:
Write a 500-word blog post about email marketing automation for B2B SaaS companies.
ANTI-SLOP CONSTRAINTS:
[paste constraints above]
Output format: markdown with H2 sections
Brand Operating System Prompt Template
Convert a brand guide into reusable AI prompts. Structure:
# Brand Operating System for [Brand Name]
## Voice Prompt
Use this for all written content:
You are writing as [Brand Name].
Voice characteristics:
- [Trait 1]: [Specific example]
- [Trait 2]: [Specific example]
- [Trait 3]: [Specific example]
Never use: [specific words/phrases to avoid]
Always include: [specific elements that must appear]
## Design Prompt
Use this for all visual generation:
Create designs for [Brand Name] following this system:
- Color palette: [hex codes with usage rules]
- Typography: [font families, sizes, hierarchy]
- Layout principles: [spacing, alignment, grid]
- Visual style: [specific descriptors with reference examples]
## Messaging Prompt
Use this for positioning and key messages:
[Brand Name] positioning:
- Core message: [one sentence]
- Value propositions: [3-5 bullets]
- Proof points: [specific evidence]
- Differentiation: [vs. competitors]
## Identity Prompt
Use this for brand consistency checks:
[Brand Name] is: [3 adjectives]
[Brand Name] is not: [3 anti-adjectives]
We talk about: [topics list]
We do not talk about: [topics to avoid]
Constraint Prompting Pattern
Every prompt should separate context from constraints:
CONTEXT:
[Everything the AI needs to understand the situation]
- Audience: [specific description]
- Goal: [measurable outcome]
- Format: [where this will be used]
- Prior work: [what's been tried]
CONSTRAINTS:
[Rules that prevent known failure modes]
- Length: [exact count or range]
- Structure: [required elements]
- Prohibitions: [what not to do]
- Requirements: [what must appear]
- Quality bar: [specific standard]
TASK:
[The actual request]
Example:
CONTEXT:
- Audience: B2B SaaS founders, 30-45, technical background, burning $50k+/month on paid ads
- Goal: 500 qualified demo bookings in 90 days
- Format: LinkedIn post, will be boosted with $2k budget
- Prior work: Last 3 posts got 2k impressions, 15 clicks, 0 conversions
CONSTRAINTS:
- Length: 150-200 words
- Structure: Hook + problem + insight + CTA, no bullets
- Prohibitions: No rhetorical questions in first sentence, no em dashes, no "imagine if"
- Requirements: Must include specific number or stat in hook, CTA must be single action
- Quality bar: Should read like a peer wrote it, not a marketer
TASK:
Write a LinkedIn post about CAC payback period optimization
Reverse Prompting for Images
Extract prompts from existing images you want to replicate:
# Conceptual workflow (requires vision model API access)
import anthropic
import os
client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
def reverse_prompt_image(image_path):
"""Extract detailed prompt from an image"""
with open(image_path, "rb") as image_file:
image_data = image_file.read()
message = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": image_data,
},
},
{
"type": "text",
"text": """Analyze this image and extract a detailed prompt that would recreate it.
Format your response as:
SUBJECT: [main subject with specific details]
SETTING: [environment, lighting, atmosphere]
COMPOSITION: [framing, angle, focus]
STYLE: [artistic style, render quality, visual treatment]
COLOR: [palette, mood, contrast]
TECHNICAL: [camera settings equivalent, depth of field]
Then provide a single consolidated prompt."""
}
],
}
],
)
return message.content[0].text
# Usage
prompt = reverse_prompt_image("reference_image.jpg")
print(prompt)
Knowledge Scrape to Content Workflow
Process for turning online discussions into ranked content:
- Scrape Sources (Reddit, forums, YouTube comments):
# Example using Reddit API
import praw
import os
reddit = praw.Reddit(
client_id=os.environ.get("REDDIT_CLIENT_ID"),
client_secret=os.environ.get("REDDIT_CLIENT_SECRET"),
user_agent="marketing_scraper"
)
def scrape_pain_points(subreddit_name, keyword, limit=100):
"""Extract pain points from subreddit discussions"""
subreddit = reddit.subreddit(subreddit_name)
pain_points = []
for submission in subreddit.search(keyword, limit=limit):
pain_points.append({
"title": submission.title,
"body": submission.selftext,
"score": submission.score,
"num_comments": submission.num_comments,
"url": submission.url
})
# Get top comments
submission.comments.replace_more(limit=0)
for comment in submission.comments.list()[:10]:
pain_points.append({
"title": f"Comment on: {submission.title}",
"body": comment.body,
"score": comment.score,
"url": submission.url
})
return pain_points
# Example usage
pain_points = scrape_pain_points("saas", "customer churn", limit=50)
- Synthesize into content brief:
Take these pain points and create a blog post outline:
[paste scraped pain points]
CONSTRAINTS:
- Title must match a real search query (provide 3 options with search volume estimate)
- H2s must address specific pain points found in the data
- Include exact quotes from the scrape (anonymized)
- Each section needs a specific solution, not generic advice
- End with a CTA that matches the reader's stage (awareness, not purchase)
Output: Title options, full outline with H2/H3 structure, key points for each section
AI Video Production Pipeline
The correct order: Prompt → Image → Video → Edit
# Step 1: Generate base image with Reve (or Midjourney, Flux)
# Use detailed prompt from reverse prompting or original creation
# Step 2: Image-to-video with Veo 3 via Google Flow
# Upload image, specify camera movement and duration
# Step 3: Download and edit in Premiere Pro or DaVinci Resolve
# Add transitions, audio, text overlays
# Decision framework:
# Stop at image if: Static social post, thumbnail, presentation slide
# Stop at video if: Short-form content (<30s), no dialogue, simple message
# Go to edit if: Long-form (>30s), multiple scenes, audio sync required
When to use each tool:
- Text-to-image: Reve, Midjourney, Flux (cheapest, most controllable)
- Image-to-video: Veo 3, Runway Gen-3 (mid-price, consistent with source)
- Text-to-video: Only when you need motion that can't be keyframed (most expensive, least consistent)
GEO (Generative Engine Optimization)
Optimize content for AI readers, not just human search:
GEO Principles:
- Authority: State credentials and sources immediately
This analysis uses data from [specific source with date], compiled by [role/credential].
- Structure: Use semantic HTML and clear hierarchy
<article>
<h1>Primary Topic</h1>
<section>
<h2>Subtopic</h2>
<p>Definition: [explicit definition]</p>
<p>Context: [why it matters]</p>
<p>Evidence: [specific data]</p>
</section>
</article>
- Discoverability: Include natural language queries as H2s
## What is [concept]?
## How do I [action]?
## Why does [thing] happen?
## When should I [decision]?
- Conciseness: State conclusions before explanations
**Answer**: [Direct answer in first sentence]
**Explanation**: [Supporting detail]
**Source**: [Citation]
Common Patterns
Pattern: Multi-Format Content from Single Source
Input: 1 long-form piece (podcast, video, essay)
Output: 20-30 pieces across formats
Breakdown:
- 1 long-form blog post (2000+ words)
- 5-7 short blog posts (500 words each)
- 10-15 social posts (LinkedIn, X)
- 5-8 short-form videos (30-60s)
- 3-5 quote graphics
- 1 newsletter section
Process:
1. Transcribe source material
2. Extract key points, quotes, data, stories
3. Use constraint prompting to generate each format
4. Apply anti-slop constraints to all written content
5. Use reverse prompting for visual consistency
Pattern: Personalized Outreach at Scale
Input: List of prospects with public content
Output: Personalized outreach messages
Process:
1. Scrape recent LinkedIn posts or X threads
2. Summarize each person's current focus
3. Generate personalized first line referencing their content
4. Attach to templated pitch
Example prompt:
CONTEXT:
- Prospect: [Name], [Title] at [Company]
- Recent post: [paste content]
- Our offer: [specific value prop]
- Goal: Book 15-min call
CONSTRAINTS:
- First sentence must reference their specific post with insight
- No generic compliments ("love your content")
- Transition must be natural, not forced
- CTA is single action (reply to book time)
- Length: 80-120 words total
TASK:
Write outreach email
Pattern: Brand-Consistent Generation
# Always use two-prompt system:
# Prompt 1: Brand OS prompt (voice/design/messaging)
[Paste brand operating system prompt]
# Prompt 2: Specific task with constraints
Create [asset type] for [use case]
CONSTRAINTS:
[specific rules for this asset]
Ensure output follows the brand system above.
Troubleshooting
Problem: Generated content sounds generic/sloppy
- Solution: Add anti-slop constraints. Check for repeated sentence openings, contrast framing, vague claims.
Problem: Brand inconsistency across outputs
- Solution: Build a brand operating system. Use the voice prompt at the start of every content generation request.
Problem: AI video output is inconsistent or low quality
- Solution: Don't use text-to-video. Use prompt → image → video pipeline. Start with Reve for consistent images, then animate with Veo 3.
Problem: Content isn't ranking or getting picked up by AI summaries
- Solution: Apply GEO principles. Add explicit definitions, state credentials, structure with semantic HTML, include natural language queries as headers.
Problem: Prompts are too long or complex
- Solution: Use constraint prompting pattern. Separate context (what AI needs to know) from constraints (rules to follow) from task (what to do).
Problem: Can't replicate a visual style
- Solution: Use reverse prompting. Feed the reference image to Claude with vision, extract detailed prompt, use that prompt in Reve or Midjourney.
Additional Resources
- Project Repository: https://github.com/Tom-Babb/ai-marketing-skills-with-by-tom-babb
- Author LinkedIn: https://www.linkedin.com/in/tbabb02/
- Author X: https://x.com/TBabb02
- Gauntlet AI: https://www.gauntletai.com
Key Tools Referenced in Skills
- Claude: Anthropic's LLM (vision, artifacts, long context)
- Reve: Image generation with persistent world-building
- Veo 3: Google's image-to-video model (via Google Flow)
- Manus: Marketing research and campaign generation
- Pipeboard: Direct integration to ad platforms
- Reddit API: Content and pain point scraping
- YouTube API: Comment and video analysis
- Adobe Premiere: Video editing
- Google Ads API: Campaign management
Each skill document contains specific tool setup and usage instructions.