content-pillar-atomizer

Name: content-pillar-atomizer
Author: Affitor

6installs184forks

How to install content-pillar-atomizer?

affitor/affiliate-skills/content-pillar-atomizer

$npx -y skills add Affitor/affiliate-skills --skill content-pillar-atomizer

Installs into the current project.

Summary

Take 1 blog post or article and generate 15-30 platform-native micro-content pieces. Not reformatting — re-contextualizing for each platform's culture. Triggers on: "atomize this content", "repurpose my blog post", "turn this into social posts", "content atomizer", "pillar conten

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SKILL.md

1	# Content Pillar Atomizer
2
3	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.
4
5	## Stage
6
7	S2: Content Creation — This IS content creation, just at 10x scale. One piece of deep work becomes a month of social content.
8
9	## When to Use
10
11	- User has a blog post, article, or long-form content and wants to maximize its reach
12	- User asks to "repurpose" or "atomize" content
13	- User says "turn this into social posts", "content multiplication", "pillar content"
14	- After `affiliate-blog-builder` (S3) produces an article — atomize it into social
15	- User wants to maintain consistent content output without creating from scratch daily
16
17	## Input Schema
18
19	```yaml
20	pillar_content: string # REQUIRED — the full blog post/article text, or URL to fetch
21
22	platforms: string[] # OPTIONAL — target platforms
23	# Options: "twitter", "linkedin", "reddit", "tiktok", "email", "threads"
24	# Default: ["twitter", "linkedin", "reddit"]
25
26	product: object # OPTIONAL — affiliate product being promoted
27	name: string
28	url: string
29	reward_value: string
30
31	mode: string # OPTIONAL — "quality" \| "volume"
32	# Default: "quality"
33
34	tone: string # OPTIONAL — "professional" \| "casual" \| "edgy" \| "educational"
35	# Default: inferred from pillar content
36	```
37
38	Chaining from S3: If `affiliate-blog-builder` was run, use its output article as `pillar_content`.
39
40	Chaining from S1 monopoly-niche-finder: Use `monopoly_niche` positioning to angle all micro-content.
41
42	## Workflow
43
44	### Step 1: Analyze Pillar Content
45
46	1. If URL provided, use `web_fetch` to retrieve content
47	2. Extract: key insights (5-8), data points, quotes, frameworks, stories, opinions
48	3. Identify the "atomic units" — self-contained ideas that work independently
49	4. Note the product/affiliate angle (if present)
50
51	### Step 1.5: Check Platform Performance for This Topic (data-driven)
52
53	Before atomizing equally across all platforms, understand which platforms are hot for this topic:
54
55	If `trending-content-scout` ran:
56	- Use platform-level engagement data from `pattern_analysis`
57	- Check `engagement_benchmark.platform_averages` — which platform has highest engagement for this keyword?
58	- Prioritize platforms where this topic has highest engagement
59	- Adjust platform allocation accordingly (see below)
60
61	Quick check (no scout data):
62	- `web_search "[topic] youtube vs tiktok vs linkedin"` → which platform dominates discussion?
63	- Check: is this topic more visual (→ TikTok/YouTube heavy) or professional (→ LinkedIn heavy)?
64	- Look for: which platform shows up most in search results for this topic?
65
66	Apply to atomization allocation:
67	- Default: equal split across platforms
68	- Data-driven: proportional to engagement potential
69	- If TikTok engagement is 5x LinkedIn for this topic → generate 5 TikTok scripts, 1 LinkedIn post
70	- If Reddit has high engagement → don't skip Reddit (often ignored by affiliates = opportunity)
71	- If YouTube dominates → consider atomizing into YouTube Shorts scripts instead of just TikTok
72
73	Platform allocation example:
74	```
75	Default (no data): Twitter: 5 \| LinkedIn: 3 \| Reddit: 3 \| TikTok: 3 \| Email: 2
76	Data-driven (TikTok hot): Twitter: 3 \| LinkedIn: 1 \| Reddit: 2 \| TikTok: 6 \| Email: 2
77	Data-driven (LinkedIn hot): Twitter: 3 \| LinkedIn: 5 \| Reddit: 2 \| TikTok: 2 \| Email: 2
78	```
79
80	### Step 2: Platform Mapping
81
82	Read `shared/references/platform-rules.md` for platform-specific rules.
83
84	For each platform, map the culture:
85
86	\| Platform \| Format \| Tone \| Length \| CTA Style \|
87	\|---\|---\|---\|---\|---\|
88	\| Twitter/X \| Thread or single tweet \| Punchy, opinionated \| 280 chars or 5-10 tweet thread \| Last tweet \|
89	\| LinkedIn \| Story or insight post \| Professional, first-person \|

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Type

content-pillar-atomizer

6installs184forks

View on GitHub

How to install content-pillar-atomizer?

affitor/affiliate-skills/content-pillar-atomizer

$npx -y skills add Affitor/affiliate-skills --skill content-pillar-atomizer

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Content Pillar Atomizer
2
3	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.
4
5	## Stage
6
7	S2: Content Creation — This IS content creation, just at 10x scale. One piece of deep work becomes a month of social content.
8
9	## When to Use
10
11	- User has a blog post, article, or long-form content and wants to maximize its reach
12	- User asks to "repurpose" or "atomize" content
13	- User says "turn this into social posts", "content multiplication", "pillar content"
14	- After `affiliate-blog-builder` (S3) produces an article — atomize it into social
15	- User wants to maintain consistent content output without creating from scratch daily
16
17	## Input Schema
18
19	```yaml
20	pillar_content: string # REQUIRED — the full blog post/article text, or URL to fetch
21
22	platforms: string[] # OPTIONAL — target platforms
23	# Options: "twitter", "linkedin", "reddit", "tiktok", "email", "threads"
24	# Default: ["twitter", "linkedin", "reddit"]
25
26	product: object # OPTIONAL — affiliate product being promoted
27	name: string
28	url: string
29	reward_value: string
30
31	mode: string # OPTIONAL — "quality" \| "volume"
32	# Default: "quality"
33
34	tone: string # OPTIONAL — "professional" \| "casual" \| "edgy" \| "educational"
35	# Default: inferred from pillar content
36	```
37
38	Chaining from S3: If `affiliate-blog-builder` was run, use its output article as `pillar_content`.
39
40	Chaining from S1 monopoly-niche-finder: Use `monopoly_niche` positioning to angle all micro-content.
41
42	## Workflow
43
44	### Step 1: Analyze Pillar Content
45
46	1. If URL provided, use `web_fetch` to retrieve content
47	2. Extract: key insights (5-8), data points, quotes, frameworks, stories, opinions
48	3. Identify the "atomic units" — self-contained ideas that work independently
49	4. Note the product/affiliate angle (if present)
50
51	### Step 1.5: Check Platform Performance for This Topic (data-driven)
52
53	Before atomizing equally across all platforms, understand which platforms are hot for this topic:
54
55	If `trending-content-scout` ran:
56	- Use platform-level engagement data from `pattern_analysis`
57	- Check `engagement_benchmark.platform_averages` — which platform has highest engagement for this keyword?
58	- Prioritize platforms where this topic has highest engagement
59	- Adjust platform allocation accordingly (see below)
60
61	Quick check (no scout data):
62	- `web_search "[topic] youtube vs tiktok vs linkedin"` → which platform dominates discussion?
63	- Check: is this topic more visual (→ TikTok/YouTube heavy) or professional (→ LinkedIn heavy)?
64	- Look for: which platform shows up most in search results for this topic?
65
66	Apply to atomization allocation:
67	- Default: equal split across platforms
68	- Data-driven: proportional to engagement potential
69	- If TikTok engagement is 5x LinkedIn for this topic → generate 5 TikTok scripts, 1 LinkedIn post
70	- If Reddit has high engagement → don't skip Reddit (often ignored by affiliates = opportunity)
71	- If YouTube dominates → consider atomizing into YouTube Shorts scripts instead of just TikTok
72
73	Platform allocation example:
74	```
75	Default (no data): Twitter: 5 \| LinkedIn: 3 \| Reddit: 3 \| TikTok: 3 \| Email: 2
76	Data-driven (TikTok hot): Twitter: 3 \| LinkedIn: 1 \| Reddit: 2 \| TikTok: 6 \| Email: 2
77	Data-driven (LinkedIn hot): Twitter: 3 \| LinkedIn: 5 \| Reddit: 2 \| TikTok: 2 \| Email: 2
78	```
79
80	### Step 2: Platform Mapping
81
82	Read `shared/references/platform-rules.md` for platform-specific rules.
83
84	For each platform, map the culture:
85
86	\| Platform \| Format \| Tone \| Length \| CTA Style \|
87	\|---\|---\|---\|---\|---\|
88	\| Twitter/X \| Thread or single tweet \| Punchy, opinionated \| 280 chars or 5-10 tweet thread \| Last tweet \|
89	\| LinkedIn \| Story or insight post \| Professional, first-person \|

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6 picks

Type