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Repurpose finished articles into platform-specific social media posts for LinkedIn, Twitter/X, Instagram, Facebook, and Threads
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| name | cf-social-adapt |
| description | Repurpose finished articles into platform-specific social media posts for LinkedIn, Twitter/X, Instagram, Facebook, and Threads |
| disable-model-invocation | true |
| argument-hint | [article-path] |
| effort | medium |
Repurpose any ContentForge article into ready-to-publish social media posts for LinkedIn, Twitter/X, Instagram, Facebook, and Threads. Each post is tailored to platform character limits, audience expectations, hashtag conventions, and optimal posting times.
All character limits, hashtag counts, ideal lengths, and format rules come from config/social-platform-specs.json. Read that file at run time and use its values. Never use remembered limits, and never trust any limit that appears in an example in this file. The supported platform list is exactly the set of platform keys in that config (e.g., linkedin, twitter, instagram, facebook, threads — plus any additions such as tiktok, bluesky, youtube-shorts).
Use /contentforge:cf-social-adapt when:
Do NOT use for:
config/social-platform-specs.jsonMinimum Required:
REQ-001)all, or a comma-separated subset of the platform keys defined in config/social-platform-specs.jsonOptional:
#AcmeMedInsights)casual, professional, provocative)/contentforge:cf-social-adapt
Prompts you for:
all)/contentforge:cf-social-adapt REQ-001 --platforms=linkedin,twitter,instagram --count=5 --url=https://acme-corp.com/blog/ai-healthcare-2026
/contentforge:cf-social-adapt REQ-001 --platforms=all
/contentforge:cf-social-adapt REQ-001 --platforms=all --hashtag=#AcmeMedInsights --count=4
Load the finished article and extract key metadata:
Source Content Loaded
---------------------------------------------------
Title: "AI in Healthcare: 2026 Trends"
Brand: AcmeMed
Word Count: 1,947
Quality Score: 9.2/10
Key Topics: AI diagnostics, precision medicine, patient care, cost reduction
Primary Keyword: "AI in healthcare"
---------------------------------------------------
The Social Adapter Agent (agents/10-social-adapter.md) identifies 10-15 moments from the article that will resonate on social media.
Extraction criteria:
SYNTHETIC EXAMPLE — fabricated for illustration. All statistics and organizations below are invented; never reuse them in real output:
Shareworthy Moments Extracted: 12
1. STATISTIC: "73% of healthcare organizations now use AI-powered diagnostics, up from 12% in 2024"
2. INSIGHT: "AI diagnostic accuracy now exceeds human radiologists by 14% for early-stage cancers"
3. TIP: "Three steps to evaluate AI diagnostic tools: accuracy benchmarks, integration requirements, compliance checklist"
4. QUOTE: "The question is no longer whether to adopt AI in healthcare, but how fast you can implement it"
5. DATA: "$4.2 billion saved annually by hospitals using AI triage systems"
6. PROVOCATIVE: "Manual diagnostic processes will be considered malpractice liability by 2030"
7. COMPARISON: "AI-assisted diagnosis: 4 minutes avg vs. traditional: 45 minutes"
8. FRAMEWORK: "The 3-layer AI healthcare stack: data ingestion, model inference, clinical integration"
9. TREND: "Precision medicine powered by AI will reduce misdiagnosis rates by 60% by 2028"
10. CASE STUDY: "Northlake Clinic (a fictional example hospital) reduced diagnostic wait times by 78% after implementing AI triage"
11. LIST: "Top 5 AI healthcare applications: diagnostics, drug discovery, patient monitoring, administrative automation, clinical trials"
12. ACTIONABLE: "Start with radiology -- it has the highest AI ROI and lowest integration complexity"
For each platform, generate the requested number of posts using platform specs from config/social-platform-specs.json.
Each post includes:
Every post is validated against:
The output is organized by platform with all metadata included.
SYNTHETIC EXAMPLE — fabricated for illustration. Every statistic, organization, URL, character count, and reach estimate below is invented. Character limits shown are examples only — always use the current values from config/social-platform-specs.json.
SOCIAL ADAPTATION REPORT
===================================================
Source: "AI in Healthcare: 2026 Trends"
Brand: AcmeMed
Platforms: 5 | Posts Per Platform: 3 | Total Posts: 15
===================================================
LINKEDIN (3 posts)
---------------------------------------------------
[LinkedIn Post 1 of 3] -- Type: Data-Driven Insight
Hook: Statistical lead
Recommended Time: Tuesday 8:00 AM
73% of healthcare organizations now use AI-powered diagnostics.
In 2024, that number was 12%.
That is not incremental adoption. That is a seismic shift in how
medicine gets practiced -- and the organizations still running
purely manual diagnostic workflows are falling behind fast.
Three things driving this acceleration:
-- Diagnostic accuracy that exceeds human radiologists by 14%
-- Average diagnosis time dropping from 45 minutes to 4 minutes
-- $4.2 billion in annual savings from AI triage systems alone
The question is no longer whether to adopt AI in healthcare.
It is how fast you can implement it without compromising patient safety.
Full analysis with 14 verified sources:
https://acme-corp.com/blog/ai-healthcare-2026
#AIinHealthcare #HealthTech #DigitalHealth #PrecisionMedicine #2026Trends
Character Count: 718 / 3,000
Image: Infographic showing 12% to 73% adoption curve (1200x627 px, PNG or JPG)
---------------------------------------------------
[LinkedIn Post 2 of 3] -- Type: How-To/Tip
Hook: Actionable framework
Recommended Time: Thursday 10:00 AM
Evaluating AI diagnostic tools? Here is a 3-step framework
that Northlake Clinic (a fictional example hospital) used before their 78% reduction in
diagnostic wait times:
Step 1: Accuracy Benchmarks
Compare the AI system against board-certified specialists
on your specific case mix. Generic accuracy claims are
meaningless without your patient population data.
Step 2: Integration Requirements
Map every touchpoint between the AI system and your existing
EHR, PACS, and clinical workflows. The biggest failures happen
at integration, not accuracy.
Step 3: Compliance Checklist
FDA clearance, HIPAA data handling, audit trail requirements,
and clinician override protocols. Non-negotiable.
The full evaluation framework (with vendor comparison criteria):
https://acme-corp.com/blog/ai-healthcare-2026
#HealthcareAI #ClinicalAI #HealthTechStrategy #MedTech
Character Count: 812 / 3,000
Image: Checklist graphic with 3 steps (1200x627 px, PNG or JPG)
---------------------------------------------------
[LinkedIn Post 3 of 3] -- Type: Provocative Statement
Hook: Contrarian take
Recommended Time: Wednesday 12:00 PM
Manual diagnostic processes will be considered a malpractice
liability by 2030.
Controversial? Maybe. But consider the data:
AI diagnostic accuracy now exceeds human radiologists by 14%
for early-stage cancer detection. When a technology demonstrably
outperforms manual methods and a physician chooses not to use it,
the legal exposure is real.
This is not about replacing doctors. It is about augmenting them
with tools that catch what human eyes miss -- especially at 3 AM
on the seventh consecutive shift.
The hospitals getting this right are not asking "should we adopt AI?"
They are asking "how do we implement it responsibly?"
https://acme-corp.com/blog/ai-healthcare-2026
#HealthcareInnovation #AIdiagnostics #FutureOfMedicine #PatientSafety
Character Count: 745 / 3,000
Image: Split-screen comparison: manual vs AI-assisted diagnosis (1200x627 px)
===================================================
TWITTER / X (3 posts)
---------------------------------------------------
[Twitter Post 1 of 3] -- Type: Statistic Lead
Recommended Time: Tuesday 9:00 AM
73% of healthcare orgs now use AI diagnostics.
In 2024? Just 12%.
AI diagnostic accuracy exceeds human radiologists by 14% for
early-stage cancers. Average diagnosis time: 4 min vs 45 min.
The shift is happening fast.
https://acme-corp.com/blog/ai-healthcare-2026
#AIinHealthcare #HealthTech
Character Count: 267 / 280
Image: Data visualization card (1200x675 px, PNG)
---------------------------------------------------
[Twitter Post 2 of 3] -- Type: Thread Starter
Recommended Time: Wednesday 11:00 AM
Northlake Clinic (a fictional example hospital) cut diagnostic wait times by 78% with AI triage.
Here is what they did differently (thread):
1/ Started with radiology -- highest ROI, lowest integration complexity
2/ Benchmarked AI against their own specialists, not vendor claims
3/ Built clinician override protocols before launch
Full breakdown: https://acme-corp.com/blog/ai-healthcare-2026
#HealthcareAI #ClinicalAI
Character Count: 274 / 280
Image: Northlake Clinic (a fictional example hospital) case study card (1200x675 px, PNG)
---------------------------------------------------
[Twitter Post 3 of 3] -- Type: Question/Engagement
Recommended Time: Friday 8:00 AM
AI diagnostics now outperform human radiologists by 14%.
When a technology demonstrably outperforms manual methods,
does choosing not to use it become a liability?
Genuine question for healthcare leaders.
#AIinHealthcare #MedTech #HealthTech
Character Count: 228 / 280
Image: None (text-only engagement post)
===================================================
INSTAGRAM (3 posts)
---------------------------------------------------
[Instagram Post 1 of 3] -- Type: Carousel
Recommended Time: Monday 11:00 AM
Slide 1 (Cover): "AI in Healthcare: The Numbers That Matter in 2026"
Slide 2: 73% of healthcare orgs now use AI diagnostics (was 12% in 2024)
Slide 3: AI accuracy exceeds human radiologists by 14%
Slide 4: Diagnosis time: 4 minutes (AI) vs 45 minutes (manual)
Slide 5: $4.2 billion saved annually from AI triage
Slide 6: 78% reduction in wait times at Northlake Clinic (a fictional example hospital)
Slide 7: CTA -- "Save this for your next strategy meeting"
Caption:
The AI healthcare revolution is not coming. It is here.
These numbers tell the story better than any prediction could.
Swipe through for the data that is reshaping how medicine
gets practiced in 2026.
The full analysis (14 verified sources, zero speculation)
is linked in bio.
Which stat surprised you the most? Drop a number in the comments.
#AIinHealthcare #HealthTech #DigitalHealth #MedTech
#PrecisionMedicine #HealthcareInnovation #FutureOfMedicine
#DataDriven #AIdiagnostics #HealthcareLeadership
Character Count: 562 / 2,200
Image: 7-slide carousel (1080x1080 px per slide, PNG)
---------------------------------------------------
[Instagram Post 2 of 3] -- Type: Single Image
Recommended Time: Wednesday 6:00 PM
Caption:
Manual diagnostic processes could become a malpractice
liability by 2030.
When AI outperforms human radiologists by 14% for early-stage
cancer detection, the legal question shifts from "why did you
use AI?" to "why didn't you?"
This is not about replacing physicians. It is about giving them
tools that catch what tired eyes miss.
Link in bio for the full analysis.
#AIinHealthcare #PatientSafety #HealthTech #MedTech
#HealthcareInnovation #ClinicalAI #FutureOfMedicine
#RadiologyAI #DigitalHealth #HealthcareLeaders
Character Count: 475 / 2,200
Image: Bold text overlay on medical-tech background (1080x1080 px, PNG)
---------------------------------------------------
[Instagram Post 3 of 3] -- Type: Reel Script
Recommended Time: Thursday 12:00 PM
Hook (0-3 sec): "One hospital cut wait times by 78%."
Body (3-20 sec): "Northlake Clinic (a fictional example hospital) implemented AI triage and
reduced diagnostic wait times by 78%. Here is the 3-step
framework they used. Step 1: Start with radiology.
Step 2: Benchmark against your own data.
Step 3: Build override protocols first."
CTA (20-30 sec): "Full case study linked in bio. Follow for
more healthcare innovation insights."
Caption:
Northlake Clinic (a fictional example hospital)'s AI triage playbook, broken down.
78% faster diagnostics. Not theory -- real results.
Save this if you are evaluating AI for your organization.
#AIinHealthcare #HealthTech #NorthlakeClinic #HealthcareAI
#MedTech #Diagnostics #Innovation #ClinicalAI
Character Count: 278 / 2,200
Video: Vertical 9:16 (1080x1920 px), 30 seconds
===================================================
FACEBOOK (3 posts)
---------------------------------------------------
[Facebook Post 1 of 3] -- Type: Link Share
Recommended Time: Tuesday 10:00 AM
The numbers on AI in healthcare are staggering.
73% of healthcare organizations now use AI-powered diagnostics --
up from just 12% in 2024. That is not a gradual trend. That is
a complete transformation of how medicine gets practiced.
We dug into the data with 14 verified sources to understand
what is driving this shift and what it means for healthcare
leaders, practitioners, and patients.
Key findings:
-- AI diagnostic accuracy exceeds human radiologists by 14%
-- Average diagnosis time drops from 45 minutes to 4 minutes
-- Hospitals are saving $4.2 billion annually with AI triage
-- Northlake Clinic (a fictional example hospital) cut diagnostic wait times by 78%
Read the full analysis:
https://acme-corp.com/blog/ai-healthcare-2026
#AIinHealthcare #HealthTech #DigitalHealth
Character Count: 712 / 63,206
Image: Article preview card (auto-generated from link, 1200x630 px)
---------------------------------------------------
[Facebook Post 2 of 3] -- Type: Question/Poll
Recommended Time: Thursday 2:00 PM
Quick question for healthcare professionals:
AI diagnostics now outperform human radiologists by 14% for
early-stage cancer detection. Diagnosis time drops from 45
minutes to 4 minutes.
Do you think:
A) AI-assisted diagnostics should be standard of care by 2028
B) We need more long-term data before mandating AI tools
C) It should remain a physician's choice, regardless of accuracy data
There is no wrong answer here -- we are genuinely curious where
the industry stands.
Our full analysis on the state of AI in healthcare:
https://acme-corp.com/blog/ai-healthcare-2026
#AIinHealthcare #HealthcarePoll #MedTech
Character Count: 595 / 63,206
Image: Poll graphic with A/B/C options (1200x630 px)
---------------------------------------------------
[Facebook Post 3 of 3] -- Type: Story/Case Study
Recommended Time: Saturday 9:00 AM
Northlake Clinic (a fictional example hospital) had a problem.
Diagnostic wait times were climbing. Patient satisfaction
was dropping. And their best radiologists were burning out
from sheer volume.
Their solution? AI-powered triage.
The results:
-- 78% reduction in diagnostic wait times
-- AI accuracy exceeding their own specialists by 14%
-- Radiologists freed up for complex cases that need human judgment
But here is what most people miss: they did not just plug in
an AI tool and hope for the best. They followed a disciplined
3-step framework. And it started with one department.
The full case study (and the framework you can copy):
https://acme-corp.com/blog/ai-healthcare-2026
#HealthcareInnovation #AIinHealthcare #NorthlakeClinic
Character Count: 701 / 63,206
Image: Northlake Clinic (a fictional example hospital) case study graphic (1200x630 px)
===================================================
THREADS (3 posts)
---------------------------------------------------
[Threads Post 1 of 3] -- Type: Hot Take
Recommended Time: Tuesday 7:00 PM
73% of healthcare orgs use AI diagnostics now. Was 12% two years ago.
That is not a trend. That is a stampede.
And the orgs still running fully manual diagnostic workflows
are about to find out what "competitive disadvantage" looks like
in healthcare.
#AIinHealthcare #HealthTech
Character Count: 275 / 500
Image: None (text-only, conversational)
---------------------------------------------------
[Threads Post 2 of 3] -- Type: Quick Tip
Recommended Time: Wednesday 8:00 AM
If you are evaluating AI diagnostic tools, start with radiology.
Highest ROI. Lowest integration complexity. Largest evidence base.
Northlake Clinic (a fictional example hospital) started there and cut wait times by 78%.
Do not try to boil the ocean. Pick the highest-impact department first.
#HealthcareAI #MedTech
Character Count: 268 / 500
Image: None (text-only)
---------------------------------------------------
[Threads Post 3 of 3] -- Type: Conversation Starter
Recommended Time: Friday 6:00 PM
AI outperforms human radiologists by 14% for early-stage cancer detection.
At what point does NOT using AI become the bigger risk?
Genuinely want to hear from people in healthcare on this one.
#AIinHealthcare #PatientSafety
Character Count: 224 / 500
Image: None (text-driven platform)
===================================================
SUMMARY
===================================================
Total Posts Generated: 15
Platforms: 5
Posts Per Platform: 3
Character Limit Compliance: 15/15 (100%)
Self-Contained Posts: 15/15 (100%)
Posts with CTA: 15/15 (100%)
Recommended Publishing Schedule:
Week 1: LinkedIn 1, Twitter 1, Instagram 1, Facebook 1, Threads 1
Week 2: LinkedIn 2, Twitter 2, Instagram 2, Facebook 2, Threads 2
Week 3: LinkedIn 3, Twitter 3, Instagram 3, Facebook 3, Threads 3
Estimated Reach (based on typical organic performance):
LinkedIn: 2,000-5,000 impressions per post
Twitter/X: 500-2,000 impressions per post
Instagram: 1,000-3,000 impressions per post
Facebook: 800-2,500 impressions per post
Threads: 300-1,000 impressions per post
===================================================
Error: REQ-001 not found in Google Drive or local output
Action: Verify requirement ID or provide direct file path
Error: Content quality score 4.8/10 (below 7.0 threshold)
Action: Content must pass quality review before social adaptation.
Run /contentforge to complete the pipeline first.
Error: Platform "<name>" is not in config/social-platform-specs.json
Supported: <list the platform keys actually present in the config>
Action: Use a supported platform, or add a spec block for the new
platform to config/social-platform-specs.json
Warning: No published URL provided for link-sharing posts.
Action: Posts generated without link. Add --url parameter to include article link.
Some post types (link share, CTA) will use "[link in bio]" placeholder.
Major platforms provide AI-content labeling options, and EU AI Act Article 50 (applicable from 2 Aug 2026) requires disclosure of AI-generated content shown to EU audiences:
AI-label: recommended (EU targeting).agents/10-social-adapter.mdconfig/social-platform-specs.jsontemplates/social-post-templates.mdBefore Social Adaptation:
/contentforge:create-content -- Produce the source article/contentforge:cf-publish -- Publish the article to your CMS (get the URL for social posts)After Social Adaptation:
Agent: Social Adapter (10-social-adapter)
Config: config/social-platform-specs.json (single source of truth for platform rules)
Templates: templates/social-post-templates.md
Default: 3 posts per platform