Content Pillar Atomizer

Take 1 blog post or article and generate 15-30 platform-native micro-content pieces. This is NOT reformatting — it's re-contextualizing each piece for the platform's culture, format, and audience expectations. A LinkedIn post reads nothing like a Reddit comment, even if they carry the same insight.

Stage

S2: Content Creation — This IS content creation, just at 10x scale. One piece of deep work becomes a month of social content.

When to Use

User has a blog post, article, or long-form content and wants to maximize its reach
User asks to "repurpose" or "atomize" content
User says "turn this into social posts", "content multiplication", "pillar content"
After affiliate-blog-builder (S3) produces an article — atomize it into social
User wants to maintain consistent content output without creating from scratch daily

Input Schema

pillar_content: string        # REQUIRED — the full blog post/article text, or URL to fetch

platforms: string[]           # OPTIONAL — target platforms
                              # Options: "twitter", "linkedin", "reddit", "tiktok", "email", "threads"
                              # Default: ["twitter", "linkedin", "reddit"]

product: object               # OPTIONAL — affiliate product being promoted
  name: string
  url: string
  reward_value: string

mode: string                  # OPTIONAL — "quality" | "volume"
                              # Default: "quality"

tone: string                  # OPTIONAL — "professional" | "casual" | "edgy" | "educational"
                              # Default: inferred from pillar content

Chaining from S3: If affiliate-blog-builder was run, use its output article as pillar_content.

Chaining from S1 monopoly-niche-finder: Use monopoly_niche positioning to angle all micro-content.

Workflow

Step 1: Analyze Pillar Content

If URL provided, use web_fetch to retrieve content
Extract: key insights (5-8), data points, quotes, frameworks, stories, opinions
Identify the "atomic units" — self-contained ideas that work independently
Note the product/affiliate angle (if present)

Step 2: Platform Mapping

Read shared/references/platform-rules.md for platform-specific rules.

For each platform, map the culture:

Platform	Format	Tone	Length	CTA Style
Twitter/X	Thread or single tweet	Punchy, opinionated	280 chars or 5-10 tweet thread	Last tweet
LinkedIn	Story or insight post	Professional, first-person	1300 chars	Soft CTA in comments
Reddit	Value-first post/comment	Helpful, honest, skeptical-aware	Variable	Disclosure + subtle
TikTok	Script with hook	Casual, energetic	30-60s script	Verbal + bio link
Email	Newsletter section	Conversational	200-400 words	Direct link
Threads	Conversational take	Casual, authentic	500 chars	Bio link

Step 3: Generate Micro-Content

For each platform, generate pieces from different atomic units:

Twitter: 3-5 pieces (1 thread, 2-3 standalone tweets, 1 hot take)
LinkedIn: 2-3 pieces (1 story post, 1 insight post, 1 question post)
Reddit: 2-3 pieces (1 detailed post, 1-2 comment-ready responses)
TikTok: 2-3 scripts (1 educational, 1 hot take, 1 tutorial)
Email: 1-2 pieces (newsletter section, dedicated email)
Threads: 2-3 pieces (conversational takes)

Each piece must:

Stand alone (makes sense without reading the pillar)
Feel native to the platform (not a copy-paste resize)
Carry one clear insight or value point
Include appropriate FTC disclosure for affiliate content

Step 4: Tag for Tracking

Tag each piece with:

Source pillar reference
Platform
Content type (thread, single, story, script)
Affiliate product (if applicable)
Suggested posting time/day

Step 5: Self-Validation

Each piece feels native to its platform (not copy-pasted)
Each piece stands alone without needing the pillar
FTC disclosure included where affiliate links present
No two pieces on the same platform say the same thing
Platform rules followed (Reddit skepticism, LinkedIn professionalism, etc.)

Output Schema

output_schema_version: "1.0.0"
atomized_content:
  pillar_title: string
  total_pieces: number
  platforms_covered: string[]

  pieces:
    - platform: string
      type: string              # "thread" | "single" | "story" | "script" | "email" | "comment"
      content: string           # The actual content, ready to post
      insight_source: string    # Which atomic unit from the pillar
      has_affiliate_link: boolean
      suggested_timing: string  # e.g., "Tuesday 9am"
      variant_id: string        # For volume mode A/B tracking

  content_pillars: string[]    # Atomic units extracted (for chaining)

chain_metadata:
  skill_slug: "content-pillar-atomizer"
  stage: "content"
  timestamp: string
  suggested_next:
    - "social-media-scheduler"
    - "email-drip-sequence"
    - "ab-test-generator"

Output Format

## Content Atomizer: [Pillar Title]

### Pillar Analysis
- **Atomic units extracted:** X insights
- **Platforms:** [list]
- **Total pieces generated:** XX

---

### Twitter/X (X pieces)

**Thread: [Title]**
🧵 1/ [first tweet]
2/ [second tweet]
...
[last tweet with CTA]

**Standalone Tweet:**
[tweet text]

---

### LinkedIn (X pieces)

**Story Post:**
[full LinkedIn post]

---

### Reddit (X pieces)

**Post: r/[subreddit]**
Title: [title]
[body with disclosure]

---

[Continue for each platform]

### Posting Schedule
| Day | Platform | Piece | Time |
|---|---|---|---|
| Mon | Twitter | Thread | 9am |
| Tue | LinkedIn | Story | 8am |
| Wed | Reddit | Post | 12pm |

Error Handling

No pillar content provided: "Paste your blog post or article, or give me the URL and I'll fetch it."
Content too short: "This is quite short for atomization. I'll extract what I can, but consider writing a longer pillar first with affiliate-blog-builder."
No affiliate angle: Generate content without affiliate links. Pure value content builds audience for future promotions.
Platform not supported: "I don't have specific rules for [platform]. I'll format it generically — review before posting."

Examples

Example 1: "Atomize my HeyGen review blog post into social content" → Extract 6 key insights, generate 15 pieces across Twitter (thread + 3 tweets), LinkedIn (2 posts), Reddit (2 posts), TikTok (2 scripts).

Example 2: "Turn this article into LinkedIn and Twitter content" → Focus on 2 platforms only. Generate 3 LinkedIn posts (story, insight, question) and 5 Twitter pieces (thread, 3 tweets, hot take).

Example 3: "Atomize in volume mode" (after affiliate-blog-builder) → Pick up article from chain. Generate 25-30 pieces with multiple variations per platform for A/B testing.

Revenue & Action Plan

Expected Outcomes

Revenue potential: Each atomized piece is a new touchpoint driving affiliate clicks. 15-30 pieces from 1 article = 15-30x more chances for commission
Benchmark: Top affiliate content creators report 2-5% of social impressions convert to link clicks. At $50 avg commission, 10,000 impressions across all pieces = $100-250/month from ONE pillar article
Key metric to track: Bio link / affiliate link CTR per platform — which platform drives the most clicks per impression?

Do This Right Now (15 min)

Pick the single strongest piece from the output — the one with the most specific, surprising insight
Post it on your highest-engagement platform immediately
Add your affiliate link in bio or first comment
Set a reminder to post the next piece tomorrow

Track Your Results

After 7 days, check: which platform generated the most affiliate link clicks? Double down on that platform, reduce effort on underperformers.

Next step — copy-paste this prompt: "Schedule all my atomized content for the next 30 days" → runs social-media-scheduler

Flywheel Connections

Feeds Into

social-media-scheduler (S5) — atomized pieces ready to schedule
email-drip-sequence (S5) — email-format pieces for sequences
ab-test-generator (S6) — volume mode variants for testing

Fed By

affiliate-blog-builder (S3) — pillar content to atomize
monopoly-niche-finder (S1) — positioning angle for all pieces
content-repurposer (S7) — repurposed content to atomize further

Feedback Loop

performance-report (S6) reveals which platforms and content types perform best → focus future atomization on winning platforms

Quality Gate

Before delivering output, verify:

Would I share this on MY personal social?
Contains specific, surprising detail? (not generic)
Respects reader's intelligence?
Remarkable enough to share? (Purple Cow test)
Irresistible offer framing? (if S4 offer skills ran)

Any NO → rewrite before delivering.

Volume Mode

When mode: "volume":

Generate 5-10 variations per platform instead of 2-3
Prioritize speed + variety over perfection
Tag each with variant ID for A/B tracking
Let data pick the winner (GaryVee philosophy)

volume_output:
  variants:
    - id: string           # e.g., "tw-v1", "tw-v2"
      content: string      # The variation
      angle: string        # What makes this one different

References

shared/references/platform-rules.md — Platform-specific culture, format, and CTA rules
shared/references/ftc-compliance.md — FTC disclosure per platform type
shared/references/affitor-branding.md — Branding rules
shared/references/flywheel-connections.md — Master connection map