feat(linkedin-thought-leadership): v1.0.0 — initial open-source import
Build LinkedIn thought leadership with algorithmic understanding, strategic consistency, and AI-assisted content creation. Updated for the January 2026 360Brew algorithm change. 16 agents, 25 commands, 6 skills, 9 hooks, 24 reference docs. Personal data sanitized: voice samples generalized to template, high-engagement posts cleared, region-specific references replaced with placeholders. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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# URL Processing Templates
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Templates and examples for converting external URLs into LinkedIn content. Use alongside the URL-to-Content Workflow in the main skill.
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## Template by URL Type
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### News Article Template
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**Input:** News article URL
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**Output:** Commentary post (800-1,400 characters)
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```
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HOOK (110-140 chars):
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[Attention-grabbing statement about what the news really means]
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CONTEXT (100-150 chars):
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[Brief summary of what happened - 1-2 sentences max]
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ANALYSIS (400-700 chars):
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[Your expert perspective]
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- What this actually means
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- What most coverage misses
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- Why your audience should care
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- Connection to your expertise
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IMPLICATIONS (150-250 chars):
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[What happens next / what to do]
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- Prediction or recommendation
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- Practical next step
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CTA (50-100 chars):
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[Question inviting perspective]
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---
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Comment #1: [Link to original article]
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```
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**Example transformation:**
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Source: "Microsoft announces new Copilot pricing tiers"
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```
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The new Copilot pricing isn't about the money. It's about strategy.
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Microsoft just restructured their Copilot licensing. Most headlines focus on the $30/user price point.
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Here's what they're missing:
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The real story is differentiation. By splitting Copilot into tiers, Microsoft is:
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1. Creating an upgrade path (land and expand)
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2. Protecting high-margin enterprise deals
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3. Addressing the "too expensive for testing" problem
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For organizations evaluating Copilot, this changes the conversation from "can we afford it?" to "which tier makes sense?"
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My prediction: Expect competitors to follow with similar tiered models within 6 months.
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What's your read on this move?
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```
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### Research Paper/Report Template
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**Input:** Research/report URL
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**Output:** Data translation post (1,200-1,800 characters)
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```
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HOOK (110-140 chars):
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[The most surprising or counterintuitive finding]
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SOURCE ATTRIBUTION (50-100 chars):
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[Brief, credible source mention]
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KEY FINDINGS (400-600 chars):
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[3-5 bullet points, simplified]
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- Finding 1 (with number if available)
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- Finding 2
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- Finding 3
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- Finding 4 (optional)
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- Finding 5 (optional)
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YOUR INTERPRETATION (300-500 chars):
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[What this means based on your experience]
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- Pattern you've observed
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- Why this matters
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- What it confirms/challenges
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PRACTICAL APPLICATION (200-300 chars):
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[What to do with this knowledge]
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- Action item 1
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- Action item 2
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CTA (50-100 chars):
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[Question about their experience]
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---
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Comment #1: Full report here: [Link]
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```
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**Example transformation:**
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Source: McKinsey report on AI implementation success rates
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```
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67% of AI projects fail to meet expectations.
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But here's the finding that should worry AI leaders more:
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McKinsey's latest analysis of 1,000+ AI implementations reveals:
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- 67% fail to achieve expected ROI
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- 53% of failures happen in the first 6 months
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- Top predictor of success isn't technology - it's organizational readiness
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- Companies with dedicated AI change management see 2.3x success rates
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- Most failures could have been predicted at project kickoff
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After leading 50+ AI projects, this matches what I've seen:
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The projects that fail rarely fail for technical reasons. They fail because:
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- Expectations weren't calibrated
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- Change management was afterthought
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- Success metrics were never defined
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- Leadership engagement dropped after kickoff
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What this means for your next AI project:
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1. Invest 20% of budget in change management
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2. Define success metrics BEFORE procurement
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3. Keep executive sponsor actively engaged
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What's been your experience with AI project success rates?
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```
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### Blog Post/Article Template
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**Input:** Blog post or external article
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**Output:** Extension or reframe post (1,000-1,600 characters)
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```
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HOOK (110-140 chars):
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[Your angle on the topic - agree, disagree, or extend]
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REFERENCE (100-150 chars):
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[Brief mention of source and their take]
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YOUR PERSPECTIVE (500-800 chars):
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[Where you agree, disagree, or add]
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- Point of agreement/disagreement
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- Your experience that supports this
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- The nuance that's missing
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- The additional consideration
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SYNTHESIS (200-300 chars):
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[Bringing it together]
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- The balanced view
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- What you'd add to their argument
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CTA (50-100 chars):
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[Invite discussion]
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---
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Comment #1: Original post by [Author]: [Link]
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```
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**Example transformation:**
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Source: Blog post arguing "AI will replace most knowledge workers"
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```
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"AI will replace knowledge workers" gets the timeline wrong.
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Just read [Author]'s piece arguing for mass displacement. The logic is sound, but the conclusion misses something important.
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Where I agree:
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AI CAN do many knowledge work tasks. Often better than humans. The capability is real.
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Where I disagree:
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Capability isn't adoption. Between "AI can do this" and "AI does this at scale" sits:
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- Regulatory compliance
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- Organizational change capacity
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- Integration complexity
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- Trust and verification needs
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- Edge case handling
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After implementing AI across 15 organizations, here's what I've seen:
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AI augments far more than it replaces. The jobs that disappear are replaced by new jobs managing, training, and overseeing AI.
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The better question isn't "what will AI replace?"
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It's "what will human-AI collaboration look like?"
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Where do you see the balance falling?
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```
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### YouTube Video/Talk Template
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**Input:** YouTube video or conference talk URL
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**Output:** Key takeaways post (1,000-1,400 characters)
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```
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HOOK (110-140 chars):
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[The insight that stopped you - the "aha" moment]
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CONTEXT (100-150 chars):
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[Where you encountered this, brief credibility of source]
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KEY TAKEAWAYS (400-600 chars):
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[3-5 lessons, your interpretation]
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1. Takeaway 1 (with your lens)
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2. Takeaway 2
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3. Takeaway 3
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4. Takeaway 4 (optional)
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5. Takeaway 5 (optional)
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APPLICATION (200-300 chars):
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[How you'll apply or already have]
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- Specific action you're taking
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- How it changes your approach
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CTA (50-100 chars):
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[Ask if others have watched/learned from this]
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---
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Comment #1: Full talk here: [Link]
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```
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**Example transformation:**
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Source: Conference keynote on AI governance
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```
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"The biggest AI risk isn't bias or hallucinations. It's organizational amnesia."
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Just watched [Speaker]'s keynote at [Conference]. This line stopped me cold.
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Key insights that will change how I approach AI governance:
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1. We're building AI systems on institutional knowledge that's not documented. When key people leave, the AI keeps running but nobody knows why it makes decisions.
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2. Audit trails aren't enough. We need "decision archaeology" - understanding the full context of how AI systems were designed.
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3. Governance isn't a checkpoint, it's continuous. The AI that passed review 6 months ago may be operating in a completely different context today.
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4. The governance question isn't "is this AI safe?" It's "can we explain and defend this AI's decisions in 3 years?"
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I'm immediately adding "documentation decay" as a risk category in our AI governance framework.
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Has anyone else encountered this organizational amnesia problem?
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```
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### Company Announcement Template
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**Input:** Company press release or announcement
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**Output:** Strategic analysis post (1,000-1,600 characters)
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```
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HOOK (110-140 chars):
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[What the announcement really signals - beyond the PR]
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WHAT HAPPENED (100-150 chars):
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[Brief factual summary]
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ANALYSIS (500-800 chars):
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[Your strategic read]
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- What this means for the company
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- What this means for the industry
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- What's NOT in the announcement
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- Who benefits/loses
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IMPLICATIONS FOR YOUR AUDIENCE (200-300 chars):
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[What your followers should do with this]
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- If you're a customer...
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- If you're a competitor...
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- If you're evaluating...
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CTA (50-100 chars):
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[Question about their read]
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---
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Comment #1: [Link to announcement]
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```
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## Attribution Language Examples
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### Direct Quotes
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```
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As [Author] writes in [Publication]: "[exact quote]"
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In [Author]'s words: "[exact quote]"
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"[Quote]" - [Author], [Publication]
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```
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### Paraphrasing
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```
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Research from [Source] shows that...
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According to [Publication]'s analysis...
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[Author] argues that... (my interpretation: ...)
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Building on [Author]'s work at [Organization]...
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```
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### General Reference
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```
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A recent study found...
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New research suggests...
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Industry data indicates...
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```
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### Credit and Extension
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```
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[Author] nailed the diagnosis. Let me add to the prescription...
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Inspired by [Author]'s post on [topic]. Here's my experience...
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Great thread from [Author] on [topic]. Adding my perspective...
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```
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## Transformation Examples by Domain
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### AI/Technology Source
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**Original headline:** "OpenAI releases new reasoning model"
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**Weak transformation:**
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"OpenAI's new model is amazing! The future of AI is here."
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**Strong transformation:**
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"OpenAI's new reasoning model changes one thing for enterprise AI. Here's what it is and why it matters for your roadmap..."
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### Business/Strategy Source
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**Original headline:** "Companies cutting AI budgets despite hype"
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**Weak transformation:**
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"AI budgets being cut! Is the hype over?"
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**Strong transformation:**
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"AI budgets are shrinking. As someone who helps organizations plan AI investments, here's what's actually happening (and it's not what headlines suggest)..."
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### Research/Academic Source
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**Original headline:** "Study finds AI increases productivity 40%"
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**Weak transformation:**
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"New study shows AI boosts productivity by 40%! Adopt AI now!"
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**Strong transformation:**
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"The new 40% AI productivity study is both right and misleading. After implementing AI for 50+ teams, here's the nuance the headlines miss..."
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## Quality Checklist for URL Transformations
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Before publishing URL-based content:
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### Attribution
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- [ ] Source clearly credited
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- [ ] Link in comment, not post body
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- [ ] Author tagged if on LinkedIn
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- [ ] Quote marks for direct quotes
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### Value Addition
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- [ ] At least 30% original content
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- [ ] My perspective clearly stated
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- [ ] Connected to my expertise
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- [ ] Actionable for my audience
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### Accuracy
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- [ ] Facts double-checked
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- [ ] Numbers verified
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- [ ] Context preserved
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- [ ] No misrepresentation
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### Format
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- [ ] Appropriate length for content type
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- [ ] Strong hook in first 140 chars
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- [ ] Proper formatting (paragraphs, bullets)
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- [ ] Clear CTA
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## Common Mistakes to Avoid
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| Mistake | Why It's Bad | Fix |
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|---------|--------------|-----|
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| Just summarizing | No unique value | Add perspective |
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| Copying structure | Looks like plagiarism | Restructure for LinkedIn |
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| Burying the source | Appears deceptive | Credit early |
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| Over-quoting | Looks lazy | Paraphrase more |
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| Link in post body | Reach penalty | Move to comment |
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| Missing CTA | Lower engagement | Add discussion question |
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| Wrong angle | Doesn't fit expertise | Choose relevant angle |
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| Too timely | Loses relevance fast | Add evergreen insight |
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