growth-marketing-os-prompts

# Growth Marketing OS Agent Skill > Skill by [ara.so](https://ara.so) — Marketing Skills collection. This skill enables you to help users leverage **Growth Marketing OS** — an open-source collection of battle-tested AI marketing prompts, Claude skills, automation workflows, and growth playbooks created by Mahmoud Omar from 15+ years of real campaigns across e-commerce, SaaS, and lead-gen in MENA and global markets. ## What Growth Marketing OS Does Growth Marketing OS provides production-ready marketing assets: - **Prompts**: Copy-paste ready prompts for paid ads, SEO/GEO, email, CRO, content, social - **Skills**: Claude agent skills (SKILL.md format) for marketing workflows - **Agents**: Full system prompts for autonomous marketing agents - **Workflows**: n8n/Make automation blueprints with JSON exports - **Playbooks**: Step-by-step campaign launch and funnel optimization guides - **Frameworks**: Original growth frameworks and mental models - **Benchmarks**: Sourced market data with citations - **Bilingual**: English + Arabic assets for MENA market All assets include real-world proof scenarios and are structured for AI assistant consumption. ## Installation & Setup Clone the repository: ```bash git clone https://github.com/growthack88/growth-marketing-os.git cd growth-marketing-os ``` The repository is file-based — no installation required. All assets are markdown files organized by category. ## Repository Structure ``` growth-marketing-os/ ├── prompts/ # Battle-tested marketing prompts │ ├── paid-ads/ │ ├── seo/ │ ├── email/ │ ├── cro/ │ ├── content/ │ └── social/ ├── skills/ # Claude agent skills ├── agents/ # Full agent system prompts ├── gpts/ # Custom GPT configurations ├── mcps/ # MCP server setups ├── workflows/ # n8n/Make automation blueprints ├── playbooks/ # Growth playbooks ├── frameworks/ # Growth frameworks ├── swipe-files/ # Hooks, headlines, ad angles ├── case-studies/ # Real campaign results ├── benchmarks/ # Market benchmarks with citations ├── worked-examples/ # Teaching scenarios └── resources/ # Curated community tools ``` ## Key Usage Patterns ### 1. Finding and Using Marketing Prompts When a user asks for marketing help, search the appropriate category: ```python import os from pathlib import Path def find_marketing_prompt(category, topic=None): """ Find prompts in the Growth Marketing OS repository. Args: category: paid-ads, seo, email, cro, content, or social topic: optional specific topic to filter Returns: List of matching prompt file paths """ prompts_dir = Path("growth-marketing-os/prompts") / category if not prompts_dir.exists(): return [] prompts = list(prompts_dir.glob("*.md")) if topic: prompts = [p for p in prompts if topic.lower() in p.stem.lower()] return prompts # Example: Find paid ads prompts paid_ads_prompts = find_marketing_prompt("paid-ads") for prompt_file in paid_ads_prompts: print(f"Found: {prompt_file.name}") ``` ### 2. Extracting Prompt Content Parse frontmatter and content from marketing prompt files: ```python import yaml import re def parse_marketing_asset(file_path): """ Parse a Growth Marketing OS asset file. Returns: dict with 'frontmatter' and 'content' keys """ with open(file_path, 'r', encoding='utf-8') as f: content = f.read() # Extract YAML frontmatter frontmatter_match = re.match(r'^---\n(.*?)\n---\n(.*)$', content, re.DOTALL) if frontmatter_match: frontmatter = yaml.safe_load(frontmatter_match.group(1)) body = frontmatter_match.group(2) return { 'frontmatter': frontmatter, 'content': body.strip() } else: return { 'frontmatter': {}, 'content': content.strip() } # Example usage asset = parse_marketing_asset("growth-marketing-os/prompts/paid-ads/meta-ad-copy-framework.md") print(f"Title: {asset['frontmatter'].get('title', 'Untitled')}") print(f"Use case: {asset['frontmatter'].get('use_case', 'General')}") ``` ### 3. Installing Claude Skills Help users install skills from the `skills/` directory into Claude Desktop: ```python import json import shutil from pathlib import Path def install_claude_skill(skill_name): """ Install a Growth Marketing OS skill into Claude Desktop config. Args: skill_name: Name of the skill file (without .md extension) """ skill_path = Path(f"growth-marketing-os/skills/{skill_name}.md") if not skill_path.exists(): return f"Skill not found: {skill_name}" # Claude Desktop skills directory (macOS example) claude_skills_dir = Path.home() / "Library/Application Support/Claude/skills" claude_skills_dir.mkdir(parents=True, exist_ok=True) dest_path = claude_skills_dir / f"{skill_name}.md" shutil.copy(skill_path, dest_path) return f"Installed skill: {skill_name} → {dest_path}" # Example result = install_claude_skill("meta-ads-optimizer") print(result) ``` ### 4. Listing Available Assets by Category ```python def list_growth_assets(category=None): """ List all available Growth Marketing OS assets. Args: category: Optional filter (prompts, skills, workflows, playbooks, etc.) Returns: dict of categories and their assets """ base_path = Path("growth-marketing-os") categories = { 'prompts': base_path / 'prompts', 'skills': base_path / 'skills', 'workflows': base_path / 'workflows', 'playbooks': base_path / 'playbooks', 'agents': base_path / 'agents', 'frameworks': base_path / 'frameworks' } assets = {} target_cats = [category] if category else categories.keys() for cat in target_cats: if cat in categories and categories[cat].exists(): if cat == 'prompts': # Prompts have subdirectories subcats = {} for subdir in categories[cat].iterdir(): if subdir.is_dir(): subcats[subdir.name] = [f.stem for f in subdir.glob("*.md")] assets[cat] = subcats else: assets[cat] = [f.stem for f in categories[cat].glob("*.md")] return assets # Example all_assets = list_growth_assets() print(json.dumps(all_assets, indent=2)) ``` ### 5. Loading n8n Workflows ```python def load_n8n_workflow(workflow_name): """ Load an n8n workflow JSON from Growth Marketing OS. Args: workflow_name: Name of the workflow file (without .json) Returns: dict containing workflow configuration """ workflow_path = Path(f"growth-marketing-os/workflows/{workflow_name}.json") if not workflow_path.exists(): return None with open(workflow_path, 'r') as f: workflow = json.load(f) return workflow # Example workflow = load_n8n_workflow("meta-lead-to-crm-sync") if workflow: print(f"Loaded workflow with {len(workflow.get('nodes', []))} nodes") ``` ## Common Use Cases ### Helping with Paid Ads Campaign ```python def help_with_paid_ads(platform, objective): """ Find relevant paid ads prompts and frameworks. Args: platform: meta, google, tiktok, etc. objective: traffic, conversions, leads, etc. """ prompts_dir = Path("growth-marketing-os/prompts/paid-ads") relevant_prompts = [] for prompt_file in prompts_dir.glob("*.md"): asset = parse_marketing_asset(prompt_file) frontmatter = asset['frontmatter'] # Check if platform and objective match if platform.lower() in str(frontmatter).lower(): if objective.lower() in str(frontmatter).lower(): relevant_prompts.append({ 'file': prompt_file.name, 'title': frontmatter.get('title', prompt_file.stem), 'content': asset['content'] }) return relevant_prompts # Example meta_conversion_prompts = help_with_paid_ads("meta", "conversions") for prompt in meta_conversion_prompts: print(f"📄 {prompt['title']}") ``` ### Finding Bilingual Assets for MENA ```python def find_arabic_assets(): """ Find bilingual (EN + AR) assets for MENA market. """ base_path = Path("growth-marketing-os") arabic_assets = [] # Search all markdown files for md_file in base_path.rglob("*.md"): asset = parse_marketing_asset(md_file) frontmatter = asset['frontmatter'] # Check for Arabic language tag or MENA topic if (frontmatter.get('language') == 'ar' or frontmatter.get('bilingual') == True or 'arabic' in frontmatter.get('topics', []) or 'mena' in frontmatter.get('topics', [])): arabic_assets.append({ 'path': str(md_file.relative_to(base_path)), 'title': frontmatter.get('title', md_file.stem) }) return arabic_assets # Example arabic_content = find_arabic_assets() print(f"Found {len(arabic_content)} bilingual/Arabic assets") ``` ### Extracting Benchmarks ```python def get_marketing_benchmarks(channel=None): """ Extract marketing benchmarks from the benchmarks directory. Args: channel: Optional filter (paid-ads, email, seo, cro, etc.) """ benchmarks_dir = Path("growth-marketing-os/benchmarks") if not benchmarks_dir.exists(): return [] benchmark_files = benchmarks_dir.glob("*.md") if channel: benchmark_files = [f for f in benchmark_files if channel in f.stem] benchmarks = [] for bm_file in benchmark_files: asset = parse_marketing_asset(bm_file) benchmarks.append({ 'channel': asset['frontmatter'].get('channel', 'general'), 'metrics': asset['frontmatter'].get('metrics', []), 'content': asset['content'] }) return benchmarks # Example email_benchmarks = get_marketing_benchmarks("email") for bm in email_benchmarks: print(f"📊 {bm['channel']}: {', '.join(bm['metrics'])}") ``` ## Configuration Growth Marketing OS is file-based with no configuration files. All metadata is stored in YAML frontmatter within each asset. Common frontmatter fields: - `title`: Asset title - `description`: What the asset does - `author`: Creator (Mahmoud Omar) - `use_case`: When to use this asset - `language`: en, ar, or bilingual - `topics`: Array of relevant marketing topics - `proof_scenario`: Real campaign where this was used - `tested_on`: Platforms/tools where this works ## Troubleshooting **Asset not found:** - Ensure repository is cloned and path is correct - Check category spelling (use hyphens: `paid-ads` not `paid_ads`) **Frontmatter parsing errors:** - Some older assets may not have frontmatter - Fall back to filename-based identification **Workflow JSON missing:** - Workflows are documented as markdown blueprints first - JSON exports added as workflows go live in production - Check the markdown spec in `workflows/` for node-level details **Language detection:** - Arabic assets may be in subdirectories or use `_ar` suffix - Check both frontmatter `language` field and file naming conventions ## Best Practices 1. **Always cite the source**: When using these assets, attribute to Mahmoud Omar and Growth Marketing OS 2. **Check proof scenarios**: Each asset includes "When I use it" context — help users understand the real-world application 3. **Respect licensing**: All assets are MIT licensed — free to use with attribution 4. **Validate before production**: These are templates — users should customize for their specific campaign/brand 5. **Check for updates**: Repository is actively maintained with weekly additions ## Integration Examples ### Using with LangChain ```python from langchain.prompts import PromptTemplate from pathlib import Path def load_growth_prompt_as_langchain(prompt_name, category): """ Load a Growth Marketing OS prompt as a LangChain PromptTemplate. """ prompt_path = Path(f"growth-marketing-os/prompts/{category}/{prompt_name}.md") asset = parse_marketing_asset(prompt_path) # Extract the main prompt content (usually after first heading) content = asset['content'] template = PromptTemplate( input_variables=asset['frontmatter'].get('variables', ['input']), template=content ) return template # Example ad_copy_template = load_growth_prompt_as_langchain("meta-ad-framework", "paid-ads") ``` ### Using with OpenAI API ```python import os import openai openai.api_key = os.getenv("OPENAI_API_KEY") def run_growth_prompt_with_openai(prompt_name, category, user_input): """ Execute a Growth Marketing OS prompt using OpenAI API. """