Expand
PMM Content Distribution Engine — Newsletter scraper, Obsidian knowledge brain, multi-format content generation powered by Claude Code
npx -y skills add raven-sourav/pmm-content-engine --skill expandAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
4.4 KB, as published. Nobody here has run it
Skill: Expand Content (Micro → Pillar)
Trigger
"Expand this into a newsletter" / "Turn this into an article" / "Make this a newsletter" / User provides a LinkedIn post to expand.
Process
Step 1: Load references
- Always load:
references/newsletter-practices.md,references/format-adaptation-matrix.md - Load expert:
experts/brunson-storytelling.md(Epiphany Bridge for narrative expansion) - Load expert:
experts/ogilvy-headlines.md(subject line generation) - Load for critique:
references/rubber-duck-escalator.md
Step 2: Analyze source post
Read the LinkedIn post and identify compressed elements:
- Core claim — What's the main argument?
- Implied evidence — What stats, studies, or examples are referenced or hinted at but not fully developed?
- Hinted stories — What personal experiences or case studies are compressed into 1-2 sentences?
- Mentioned frameworks — What models or structures are named but not fully explained?
- Missing counterarguments — What "but what about..." questions would a sharp reader ask?
- Angle — Which angle is the source post? (This informs expansion strategy.)
Step 3: Mini-research from Brain
Load relevant Brain sections to find deeper evidence:
evidence_bank— Find 2-3 additional stats that support the core claimtopic_depth_layers— Pull key debates, common mistakes, practitioner wisdom on the topicnewsletter_insights— Find cross-references from 2+ newsletter sourcessynthesized_mental_models— Identify relevant mental model to weave in
Step 4: Expand using newsletter structure
Follow the full structure from references/newsletter-practices.md:
-
Opening story (200-400 words)
- If source post hints at a story → expand using Epiphany Bridge (7-step)
- If source is framework-based → create a scene that reveals why the framework matters
- If source is contrarian → open with the moment you realized the common view was wrong
-
Bridge to insight (100-200 words)
- Connect the story to the core claim naturally
-
Deep framework/evidence section (600-1200 words)
- Fully develop the framework or argument that was compressed in the LinkedIn post
- Add the 2-3 evidence anchors from mini-research
- Cross-reference 2+ newsletter sources
- Include the "only I could have written this" section (200+ words)
- Use rejection-based teaching where appropriate
-
Practitioner callout (200-400 words)
- "What most get wrong" — the compressed insights from the LinkedIn post become the foundation
- Expand with specific examples and counterarguments
-
Landing (100-200 words)
- Connect back to opening story
- Specific, actionable takeaway
-
P.S.
- Secondary CTA or personal note
Step 5: Generate subject line + preview text
Using Ogilvy headline principles:
- Write 5 subject line variations
- Select the best (curiosity + specificity)
- Write preview text that extends (not repeats) the subject line
Step 6: Critique (Rubber Duck Escalator)
Run all 5 phases with newsletter-adapted scoring:
- MIRROR demands 2-3 specific experiential moments (vs 1 for LinkedIn)
- CHALLENGE demands 3+ evidence anchors
- CRYSTALLIZE checks 1500-3000 word range and subject line quality
- All phases must score 8+
- Max 3 iterations
Step 7: Present to user
Show the full newsletter with scorecard and subject line options.
Output Format
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EXPANSION ANALYSIS
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Source: [LinkedIn post summary]
Core claim: [1 sentence]
Compressed elements found: [list]
Evidence added from Brain: [list]
Newsletter sources cross-referenced: [list]
Mental model applied: [name]
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NEWSLETTER
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Subject line: [Primary]
Alt subjects: [2 alternatives]
Preview text: [1-2 sentences]
Word count: [X words]
---
[Full newsletter body]
---
P.S. [text]
SCORECARD
Mirror: X | Probe: X | Challenge: X | Illuminate: X | Crystallize: X
Composite: X.X | Status: PASS/FAIL
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