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Distribute

Skill raven-sourav/pmm-content-engine/skills/distribute

PMM Content Distribution Engine — Newsletter scraper, Obsidian knowledge brain, multi-format content generation powered by Claude CodeFrom the repository description

Install
npx -y skills add raven-sourav/pmm-content-engine --skill distribute

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SKILL.md

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Skill: Distribute Content (Atomize Pillar → Micro)

Trigger

"Distribute this" / "Atomize this" / "Repurpose for all formats" / User provides a newsletter or long-form post to distribute.

Process

Step 1: Load references

  • Always load: references/format-adaptation-matrix.md, references/generation-angles.md
  • Load per format: references/twitter-practices.md, references/carousel-practices.md, references/linkedin-practices.md
  • Load expert: experts/garyvee-distribution.md
  • Load for critique: references/rubber-duck-escalator.md

Step 2: Analyze source content

Read the source content (newsletter article, long-form post, or any pillar piece) and extract:

  1. Core insight — The single sharpest idea. One sentence.
  2. Key evidence anchors — The strongest 1-3 stats, case studies, or examples.
  3. Framework/progression — Any structural model, steps, or progression that can stand alone.
  4. Story elements — Any narrative beats (backstory, turning point, lesson).
  5. Best angle — Which of the 3 angles (contrarian, framework, story) is strongest in the source?

Step 3: Generate platform-native adaptations

For each target format, RE-CREATE the content using the format-adaptation-matrix:

LinkedIn Post (800-1200 characters)

  1. Pick the best angle from the source analysis
  2. Extract the sharpest insight + strongest evidence anchor
  3. Apply hook typology (compress to LinkedIn format)
  4. Follow generation-angles.md structure for the chosen angle
  5. Apply AI decontamination

Twitter/X Thread (7-10 tweets, 280 chars each)

  1. Map the source to a thread type (Educational / Story / Framework / Hot Take)
  2. Write hook tweet — compress the best hook to 280 chars
  3. One idea per tweet, tweet 2 = strongest insight
  4. Vary rhythm (short/long tweets)
  5. Close with CTA
  6. Verify EVERY tweet is ≤280 characters
  7. Apply AI decontamination per tweet

Carousel Brief (5-10 slides)

  1. Extract framework or progression from source
  2. One key point per slide, max 3-4 lines
  3. Cover slide: hook adapted for visual format
  4. Close slide: CTA + AllAboutPMM brand mark
  5. Include at least 1 evidence anchor
  6. Follow visual design rules from carousel-practices.md

Step 4: Critique each output (Rubber Duck Escalator)

Run all 5 phases for EACH format output:

  1. MIRROR → PROBE → CHALLENGE → ILLUMINATE → CRYSTALLIZE
  2. Use format-adapted scoring (see rubber-duck-escalator.md format calibration)
  3. Score 1-10 per phase. All phases must hit 8+.
  4. If any phase < 8: rewrite addressing failures
  5. Max 3 iterations per format

Step 5: Present all outputs together

Show all format outputs in a single view with scorecards.

Output Format

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SOURCE ANALYSIS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Core insight: [1 sentence]
Best angle: [Contrarian / Framework / Story]
Evidence anchors: [list]
Framework elements: [if applicable]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LINKEDIN POST
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[Draft text]

SCORECARD
Mirror: X | Probe: X | Challenge: X | Illuminate: X | Crystallize: X
Composite: X.X | Status: PASS/FAIL
Characters: XXX

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TWITTER/X THREAD
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1/ [Hook tweet] [X/280]
2/ [Tweet] [X/280]
...
N/ [Close + CTA] [X/280]

SCORECARD
Mirror: X | Probe: X | Challenge: X | Illuminate: X | Crystallize: X
Composite: X.X | Status: PASS/FAIL

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
CAROUSEL BRIEF
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Slide 1 (Cover): [Hook + Title]
Slide 2: [Point]
...
Slide N (Close): [CTA]

SCORECARD
Mirror: X | Probe: X | Challenge: X | Illuminate: X | Crystallize: X
Composite: X.X | Status: PASS/FAIL

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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