An automated content atom library builder that scans past content to extract reusable ideas, claims, and data points, eliminating blank-page creator burnout.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install phy-content-compound
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install phy-content-compound using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Content Compound?
Content Compound is an automated content atom library builder designed for social media creators and technical writers. It scans your directory of past markdown or text files and uses regex-based patterns to extract reusable content atoms—including data points, assertions, anecdotes, frameworks, contrarian angles, and questions. Each extracted atom is tagged with keywords and structured with clear source attribution, ensuring you never have to start writing from a blank page again.
This utility leverages the compounding serendipity effect of the Zettelkasten method. Built as part of the broader ecosystem of Openclaw Skills, it integrates seamlessly into modern content workflows, turning every post you write into reusable material that makes your next piece of content easier and faster to produce.
Content Compound Use Cases
- Eliminating Blank Page Syndrome: Resurfaces relevant claims, data, and stories to write faster.
- Content Repurposing: Scans long-form archives or newsletters to extract bite-sized social media nuggets.
- Knowledge Management: Links historical text fragments dynamically using a Zettelkasten-style keyword system.
- Outline Generation: Creates rapid-fire content structures (Hook-Body-CTA) using highly personalized, pre-written components.
How Content Compound Works
- Directory Scanning: Recursively scans user-defined content directories for .md and .txt files while ignoring standard noise folders like .git/ or node_modules/.
- Regex Atom Extraction: Automatically classifies text fragments into six unique categories (e.g., claims, frameworks, and contrarian perspectives) based on pre-defined regex structures.
- Keyword & Context Tagging: Employs a zero-dependency context-window TF approach to map and index keywords to each atom.
- Search and Retrieval: Users search their library by topic. The tool uses a scoring algorithm based on keyword overlap, text presence, and engagement metrics to fetch the best results.
- Outline Compilation: Synthesizes the retrieved atoms into a ready-to-use template, organizing content into highly engaging Hook, Body, and CTA sections.
Content Compound Setup
Prerequisites
Make sure you have Python 3.7+ installed. This tool requires no external dependencies.
Installation
Simply clone or copy the script to your local skills directory.
CLI Usage
Run the Python script directly from your terminal:
# Build atom library from your content directory
python3 ~/.claude/skills/phy-content-compound/scripts/content_compound.py ~/Desktop/content-ideas/
# Build library and query for a specific topic
python3 ~/.claude/skills/phy-content-compound/scripts/content_compound.py ~/posts/ --topic "developer tools"
# Query specific atom type (e.g., data points only)
python3 ~/.claude/skills/phy-content-compound/scripts/content_compound.py ~/posts/ --topic "AI" --type data_point
# Limit output results
python3 ~/.claude/skills/phy-content-compound/scripts/content_compound.py ~/posts/ --topic "security" --top 10
# Output results in structured JSON format
python3 ~/.claude/skills/phy-content-compound/scripts/content_compound.py ~/posts/ --topic "growth" --format json
Content Compound Data Schema & Taxonomy
Atom Taxonomy
| Type | Icon | What It Captures | Detection Pattern |
|---|---|---|---|
data_point |
📊 | Specific numbers, percentages, metrics | Sentences with digits + units (%, $, K, M, x) |
claim |
💡 | Strong assertions, thesis statements | Phrases like "The key insight is...", "The reality is..." |
anecdote |
📖 | Personal stories and experiences | First-person indicators like "I built...", "We shipped..." |
framework |
🔧 | Structured lists, step-by-step models | 3+ consecutive bullet points or numbered items |
contrarian |
🔥 | Challenges to conventional wisdom | Disruption triggers like "Stop...", "Everyone is wrong about..." |
question |
❓ | Thought-provoking discussion drivers | Sentences ending with "?" exceeding 25 characters |
Library Scoring Matrix
- Keyword Overlap Weight: Multiplied by 2.0
- Text Presence Weight: Multiplied by 1.5
- Engagement Bonus: Calculated automatically based on specific file names (e.g., files containing keywords like
viralorhit)
Content Compound Advanced Features
- Zero External Dependencies: Built entirely using the pure Python stdlib for rapid cross-environment execution.
- Zettelkasten Serendipity Threshold: Scalable compounding effect that kicks into high-speed automation after the library crosses 50-100 structured atoms.
- Multi-Skill Flywheel Integration: Fully compatible with sister Openclaw Skills including
phy-content-humanizer-audit(AI detection auditing) andphy-post-forensics(performance feedback loops).
SKILL.md
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