Add automated writing style review

[whusterj]

Adds a GitHub Action that automatically reviews markdown content in PRs using Claude.

Features:

• Reviews notes, essays, and posts against style guidelines
• Posts as code review (non-blocking)
• Critical but constructive feedback
• Reviews each file individually

Setup required: Add ANTHROPIC_API_KEY to repository secrets (see .github/SETUP.md)

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📝 Writing Style Review

Summary

This piece explores an important strategic question about transforming agency learnings into scalable products. While the conversational excerpts provide authentic insight into your thinking process, the argument needs stronger development and clearer positioning on key claims.

Strengths

• The "learning triangle" concept effectively reframes Hormozi's leverage model for your specific context, showing original thinking rather than just borrowing frameworks
• The concrete example of PT billing workflows → agent launcher framework → 50+ organizations demonstrates the reinvestment cycle clearly

Critical Issues

1. Unsupported leverage claim (lines 8-10): "We've built hundreds of apps and provided leverage to our entreprenuers. We've learned a lot along the way. But for every learning we've had, it's extremely difficult to reinvest it." This core premise needs evidence or examples. What specific learnings couldn't be reinvested? Why was it difficult?

2. Contradictory startup success narrative (lines 15-18): You claim working with dozens of entrepreneurs didn't create "outsize impact" because "only a few survive," but then attribute failure to "timing, connections, competition" rather than the quality of your work. This undermines your leverage argument—if external factors dominate, maybe the issue isn't reinvestment patterns but market realities.

3. Vague "distribution test" (lines 68-75): The "new year bet" lacks specifics. How do you identify 50+ organizations? What constitutes "same problem"? How do you measure success? This reads more like aspiration than actionable strategy.

Important Issues

4. Missing context on triangles comparison (lines 41-50): You reference "that triangle [from Every]" without explaining what it is, then compare it to Hormozi's. Readers need the Every triangle explained or linked to understand the comparison.

5. Unexamined assumption about scaling (line 63): "Find a broad problem, create a solution, and then go down customization rabbit holes" assumes this approach is inferior, but many successful companies (Salesforce, Slack) started broad and specialized. Why is your reverse approach better?

6. Weak conclusion (lines 76-78): The final questions about "tools that are extremely relevant to the average knowledge worker" shift focus away from your core argument about learning reinvestment to generic productivity software.

Minor Issues

7. Conversation formatting: The conversation blocks are interesting but long. Consider excerpting key exchanges and summarizing the rest.

8. Missing wiki links: Several concepts could connect to other notes - "leverage," "agency work," "pattern extraction," etc.

Specific Line-by-Line Feedback

Line 2: "We love our agency clients" - This opening feels unnecessarily defensive. You could start stronger with the strategic challenge: "After building hundreds of apps for entrepreneurs, we face a leverage problem..."

Lines 24-26: The conversation about leverage vs. monetization vs. learning triangles gets confusing. Consider a clearer transition: "But Marcy saw a different triangle entirely—one focused on learning rather than monetization."

Lines 51-55: "The key insight: These triangles should feed each other" needs development. How exactly should they feed each other? What does this look like in practice beyond your one example?

Line 67: "Often, companies emerge in an opposite way" - This comparison could be stronger with specific examples of companies that started broad vs. those that started deep.

This piece has the bones of an important strategic insight, but needs more evidence, clearer arguments, and concrete examples to fully convince readers of your approach.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review

Summary

This piece tackles a genuinely important shift in how knowledge work gets valued as AI changes productivity dynamics. The personal, conversational tone works well for exploring an evolving idea, though the argument would benefit from clearer structure and more concrete evidence to support the proposed pricing framework.

Strengths

• Honest uncertainty: The acknowledgment that "Will that work in the real world? I don't know" builds trust and accurately signals this is exploratory thinking rather than a proven framework
• Concrete example: The side-by-side pricing comparison ($60K vs $112K) makes the abstract concept tangible and actionable

Critical Issues

1. Unclear audience and scope (throughout): Who is this for? The piece jumps between "we" (ThinkNimble), "I" (Marcy), and general advice without clarifying whether this applies broadly to consultants, specifically to dev shops, or just to your situation. This makes it hard to evaluate the framework's applicability.

2. Unsupported percentage ranges (lines 45-75): The value premiums (10-30%, 15-50%, etc.) appear arbitrary without any basis provided. Where do these numbers come from? Industry benchmarks? Your experience? Client feedback? Without grounding, they read like wishful thinking rather than actionable guidance.

3. "Dark Side of the Moon" metaphor confusion (lines 20-35): This section introduces a compelling concept—unmeasurable but real value—but then abandons it without connecting it to the pricing framework. Either develop this fully or cut it; as written, it disrupts the argument flow.

Important Issues

4. Missing evidence for "half the MVPs can be vibe coded" (line 5): This is a significant claim about AI's current capabilities that needs backing. What types of MVPs? What does "vibe coded" mean exactly? Without specifics, readers can't evaluate whether this applies to their situation.

5. Weak connection to broader trends (line 85): "Others who have deep expertise are too" and predictions about "major changes in the next 2-3 years" need support. Who else? What evidence suggests this timing? Link to other research or examples.

6. Inconsistent framing: The piece oscillates between personal reflection ("Peter and I were brainstorming") and general advice without clear transitions. Choose one primary frame or make the shifts more intentional.

Specific Line-by-Line Feedback

Line 8: "Half the MVPs we used to build can be vibe coded. Half the other half..." - This mathematical construction is confusing. Consider: "Many MVPs we used to build can now be vibe coded, and many of the remaining ones can be handled with..."

Line 24: "How does one pre-show value that is like seeing the dark side of the moon?" - Explain the metaphor. What specifically about your value is like the moon's dark side? Hidden but influential? Impossible to directly observe?

Line 42: "[we've taken dozens pre-revenue companies through their seed rounds]" - This is powerful evidence for your expertise. Move it earlier and develop it more—this credibility should frame the entire piece.

Line 79: "The frame: 'You're not buying hours...'" - This is a strong value proposition. Consider leading with this framing rather than burying it.

Minor Suggestions

• The title could be more specific: "Pricing for Value Instead of Time" → "How AI-Augmented Consultants Should Price for Value, Not Hours"
• Consider restructuring: Lead with the problem (AI changing productivity), establish your credibility (dozens of seed rounds), present the framework, then acknowledge limitations
• The related concept link to [[Tour of Duty in the AI Era]] is good wiki-style connecting, but explain briefly why it's relevant

This feels like valuable thinking in progress. The core insight—that AI-enhanced productivity breaks hourly pricing—is important and under-explored. With more evidence and clearer structure, this could become a compelling framework for other consultants facing the same challenge.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review

Summary

This piece explores an intriguing evolution of the "tour of duty" career concept for the AI era, but suffers from unclear structure and underdeveloped arguments. The sports analogy is compelling, but the connection between AI augmentation and this new career model needs more explicit development.

Strengths

• The sports model provides a concrete, relatable framework for understanding compressed, high-value career arcs
• Including actual conversation snippets effectively shows the collaborative thinking process behind the ideas

Critical Issues

1. Status confusion (front matter): This is marked as "budding" status but reads more like raw notes than a coherent argument. The brackets like "[link to infinite games]" and editorial asides suggest this needs significant development before publication.

2. Missing AI connection (throughout): The title promises insights about the "AI era" but the piece never clearly explains how AI enables or necessitates this career model. Why couldn't this sports model have worked pre-AI? What specifically about AI augmentation makes shorter, higher-impact engagements possible?

3. Incomplete evidence (line 8): "There will be long-tail careers... but there will also be a huge market for talent grabbing. [there's a lot of evidence on this... Note to self to gather these]" - This undermines credibility. Either include the evidence or remove the unsupported claim.

Important Issues

1. Unclear value proposition (lines 95-120): The "agents as hiring signal" section introduces an interesting idea but doesn't connect it clearly to the tour of duty concept. How do personal AI agents enable the sports model? This connection needs explicit development.

2. Vague implementation (lines 140-150): "Define what zone of genius someone has demonstrated" and "Understand the gaps in your competitive strategy" - these are too abstract. What would this actually look like in practice? How is this different from current consulting engagements?

3. Unsubstantiated claims (line 165): "Professional sports already work this way" - while true, you need to explain why this model would translate to knowledge work. What are the key similarities and differences?

Specific Line-by-Line Feedback

• Line 8: Remove the bracketed note to self or replace with actual citations
• Lines 38-42: The original 2019 framework could be condensed - the lengthy quote doesn't add much beyond what you summarize afterward
• Line 95: "Agents as Hiring Signal" - this section title doesn't clearly connect to tours of duty. Consider: "How AI Agents Signal Professional Capability"
• Lines 180-185: The conversation snippets here are valuable but need a stronger transition. Why is this relevant to the sports model?
• Line 210: "Deep engagement model where experts:" - this bulleted list is your clearest articulation of the concept. Consider moving it earlier and expanding on each point

Minor Suggestions

• The "Open Questions" section is valuable but feels like notes rather than polished content. Consider integrating these questions throughout the piece to drive the argument forward.
• Wiki links like "[[Agency of Agents]]" appear without context. Either develop these connections or remove them until the linked concepts exist.

This piece has the seeds of an important insight about AI-era career structures, but it needs significant development to move from raw exploration to a coherent argument that readers can understand and act upon.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/distribution-vs-depth.md

Summary

This piece effectively captures a strategic inflection point through candid internal conversation and clear triangular frameworks. However, it needs stronger evidence to support key claims and clearer positioning of ideas as exploration rather than proven strategy.

Strengths

• The raw conversation snippets provide authentic insight into strategic thinking-in-progress, making abstract concepts tangible
• The dual triangle framework (Hormozi's leverage vs. your learning triangle) creates a clear mental model for understanding the distribution-depth tension

Critical Issues

1. Unsupported leverage claims (opening paragraph): "We've built hundreds of apps and provided leverage to our entrepreneurs" - this needs quantification. What kind of leverage? How do you measure it? Without evidence, this undermines the entire premise.

2. Survival rate assertion needs backing (line under "The Leverage Problem"): "Only a few survive, and that can have everything to do with timing, connections, competition, and nothing to do with skill or idea or effort or tech." This is a strong empirical claim about startup failure that requires citation or clear marking as your hypothesis based on experience.

3. Vague "New year bet" (final sections): The 50-client test is intriguing but underspecified. How would you identify these 50 organizations? What defines "same problem"? What would success look like? This reads more like brainstorming than actionable strategy.

Important Issues

4. Missing idea maturity signals: You're clearly in exploration mode ("we're asking," "cool bet would be"), but the piece jumps between speculation and assertion. Be more explicit: "We're exploring whether..." or "Our hypothesis is that..."

5. Weak connection to "larger good" (opening): You mention using the agency "for a larger good" but never define what that good is. This weakens the motivational framework.

6. Terminology without definition (line 45): "agent launcher framework" appears without context. Either define it or link to [[Agent Launcher Framework]].

Minor Issues

7. Conversation formatting: The conversation blocks work well but could use brief context. When did this conversation happen? Was it prompted by a specific client situation?

8. Missing wiki links: This connects to several concepts that likely exist elsewhere in your knowledge base - [[Leverage]], [[Client Consulting Strategy]], [[Product Development]], etc.

Specific Line-by-Line Feedback

Line 23: "We've learned a lot along the way. But for every learning we've had, it's extremely difficult to reinvest it." - This is the core insight but needs unpacking. What specific learnings? What makes reinvestment difficult? Give readers a concrete example.

Line 67: "A cool bet for the new year would be..." - This casual phrasing undermines what could be a significant strategic pivot. Consider: "We're testing the hypothesis that requiring 50+ potential applications for each agency engagement would force intentional learning reinvestment."

Line 89: "Could we build a set of tools that are extremely relevant to the average knowledge worker..." - This shifts from your specific PT/consulting examples to a much broader vision without transition. Either develop this connection or focus on your narrower application.

Attribution note: Well done marking this as "seed" status and "human-written" - this clearly signals the exploratory nature of the ideas.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/distribution-vs-depth.md

Summary

This piece effectively uses concrete examples and personal experience to explore a compelling business strategy question. The conversational excerpts provide authentic insight into your thinking process, and the framework comparison creates useful conceptual clarity. However, the piece would benefit from clearer positioning on idea maturity and more explicit connections to broader business strategy concepts.

Strengths

• The contrast between Hormozi's leverage triangle and your "learning triangle" creates a clear conceptual framework that readers can apply beyond your specific context
• The embedded Slack conversations effectively show rather than tell your thought process, making abstract strategy concepts tangible

Critical Issues

1. Unclear idea maturity (throughout): This reads like active brainstorming rather than tested framework, but the confidence level isn't explicitly signaled. Consider opening with something like "We're exploring a hypothesis about how consulting firms can build leverage..." to set proper expectations.

2. Missing broader context (lines 1-15): The piece jumps into ThinkNimble-specific challenges without establishing why this distribution vs depth tension matters to readers outside your company. A brief paragraph about how this applies to other consulting firms, agencies, or service businesses would strengthen the opening.

Important Issues

3. Uncited framework reference (line 85): "That triangle [from Every]" and later reference to "Every's Master Plan Part I Triangle" should be properly linked. Readers can't follow the intellectual thread without seeing the original Every piece that inspired this thinking.

4. Weak connection to related concepts (bottom section): The [[Wiki Links]] are good practice, but they feel tacked on rather than integrated. Consider weaving these connections into the main text where relevant, then using the Related Concepts section for additional connections.

5. Vague success metrics (lines 95-110): The "New year bet" section lists criteria but doesn't define what success looks like. How will you know if this approach works? What would convince you to double down vs pivot?

Minor Issues

6. Inconsistent voice (line 1): Opens with "We love our agency clients" but shifts to more analytical tone. The enthusiastic opening works well - consider carrying that energy through more of the piece.

7. Conversation formatting (multiple instances): The embedded conversations are valuable but break up the flow. Consider summarizing key insights after each conversation block to help readers extract the main points.

8. Typos (line 8): "entreprenuers" should be "entrepreneurs" (appears twice)

The core insight about transforming deep client work into broad applications is genuinely valuable and could help many service-based businesses. With clearer positioning and stronger connections to existing frameworks, this could become a robust piece of business strategy thinking.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/pricing-value-not-time.md

Summary

This piece tackles a genuinely important problem—how expertise pricing evolves in an AI-augmented world—with concrete examples and clear thinking. However, the argument needs stronger evidence backing the value calculations, clearer positioning of ideas as exploratory, and more precise terminology around key concepts.

Strengths

• Concrete value framework: The five-dimensional breakdown (lines 46-80) transforms abstract pricing theory into actionable categories with specific percentages
• Honest uncertainty: The acknowledgment "Will that work in the real world? I don't know" (line 89) demonstrates intellectual honesty about untested ideas

Critical Issues

1. Uncited value multipliers (lines 47-80): The specific percentages (10-30%, 15-50%, etc.) appear authoritative but lack supporting evidence. Are these based on ThinkNimble's experience, industry research, or theoretical estimates? Consider: "Based on our experience with [X] clients..." or "Industry research suggests..." with appropriate links.

2. Vague expertise claim (line 35): "We've become experts at helping real knowledge workers become real entrepreneurs" needs evidence. How many clients? What outcomes? Success rates? The bracketed claim "[we've taken dozens pre-revenue companies through their seed rounds]" (line 43) is a good start but needs expansion.

3. Missing idea maturity signal: This reads like established framework rather than exploratory thinking, but the conclusion suggests it's still theoretical. Consider opening with "We're exploring a framework..." or similar framing.

Important Issues

4. Undefined "vibe coded" (line 7): This term may confuse readers unfamiliar with current development slang. Consider: "Half the MVPs we used to build can now be created through intuitive, AI-assisted development (what developers call 'vibe coding')"

5. "Dark side of the moon" metaphor (lines 25-26): The metaphor isn't immediately clear—counterfactual value that's hard to demonstrate? Consider a more direct explanation before the metaphor.

6. Calculation presentation (lines 82-88): The math breakdown is helpful but feels disconnected from the preceding framework. Consider explicitly connecting each line item to the five dimensions.

Minor Issues

7. Conversational transitions: "Peter and I were brainstorming..." (line 3) works for the exploratory tone, but the piece could benefit from a clearer thesis statement early on.

8. Link opportunity missed: The "Tour of Duty in the AI Era" reference (line 93) is properly linked, but consider linking to pricing strategy resources or value-based consulting literature.

9. Passive construction (line 19): "None of these capture what AI-augmented workers actually deliver" could be more direct: "These models fail to capture..."

Specific Line-by-Line Feedback

Line 43: The bracketed insertion feels like a placeholder. Either integrate this credential naturally into the text or expand it with specific metrics.

Lines 46-47: "If we start to break down the value..." - consider "When we analyze the value..." for more confidence in your framework.

Line 62: "Fundable prototype that demonstrates viability" - this is crucial value but could use an example: "A working demo that shows investors the core technical risk is solved."

The piece effectively identifies a real problem and proposes a thoughtful solution framework. With stronger evidence for the value calculations and clearer signaling of the idea's development stage, this could be a compelling resource for others facing similar pricing challenges.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/pricing-value-not-time.md

Summary

This piece tackles a genuinely important problem—how AI changes value creation and pricing—with personal experience and concrete examples. However, it needs more rigorous evidence to support its claims and clearer definitions of key concepts to be truly compelling.

Strengths

• The "dark side of the moon" metaphor effectively captures the difficulty of proving counterfactual value—outcomes that didn't happen because of your intervention
• The five-dimensional value framework provides a concrete alternative to hourly pricing, with specific premium percentages that make the concept actionable

Critical Issues

1. Uncited foundational claims (lines 7-9): "Half the MVPs we used to build can be vibe coded. Half the other half can use a good one shot prompt..." This is a core premise of your argument but needs evidence. What data supports this? Link to studies on AI coding capabilities or provide specific examples.

2. Undefined technical terms (line 8): "Vibe coded" and "one shot prompt" appear without explanation. Consider: "Half the MVPs we used to build can now be 'vibe coded'—roughed out quickly using AI assistants with minimal direction—while others need just a single well-crafted prompt..."

3. Missing evidence for expertise claims (line 30): "[we've taken dozens pre-revenue companies through their seed rounds]" needs substantiation. This is your key credential for the value framework that follows.

Important Issues

1. Speculation vs. claims (line 53): "I think we'll see major changes in outcomes based pricing...in the next 2-3 years" should be marked more clearly as prediction: "We predict major changes..." or "Based on our experience, we expect..."

2. Weak value calculation (lines 34-48): The percentage premiums (10-30%, 15-50%, etc.) appear arbitrary. What's the basis for these ranges? Industry benchmarks? Your experience? Client willingness to pay?

3. Unclear methodology (line 49): The example calculation jumps from percentages to fixed dollar amounts without explaining the logic. Why is "Funding catalyst" calculated differently (as percentage of raise) while others are percentage premiums on base cost?

Minor Issues

1. Awkward phrasing (line 6): "a lot of halves for one whole" breaks the flow. Consider cutting this aside.

2. Missing link opportunity (line 55): "Tour of Duty in the AI Era" is referenced but this concept could benefit from more context about how it relates to value-based pricing.

3. Passive construction (line 22): "Sometimes, our work ends in failed startups" could be more direct: "Sometimes, the startups we work with fail."

The core insight here—that AI changes the relationship between time and value—is solid and timely. But to make this piece truly compelling, you need to ground your framework in more concrete evidence and clearer definitions of what you're actually measuring.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/tour-of-duty-ai-era.md

Summary

This piece explores a fascinating evolution of career models and presents compelling original thinking about the intersection of AI, labor markets, and compensation. However, it reads more like working notes than a coherent argument, with multiple ideas that need development and clearer connections between concepts.

Strengths

• The sports analogy is genuinely insightful and well-developed—it provides a concrete framework for understanding compressed, high-value career arcs
• Including the Slack conversation adds authenticity and shows the collaborative thinking process, making abstract concepts more relatable

Critical Issues

1. Scattered argument structure: The piece jumps between historical framework, hiring practices, sports analogies, and open questions without clear transitions. Consider reorganizing around a central thesis: "How AI-era labor markets are creating a third career path between traditional employment and consulting."

2. Undefined key concepts (throughout): Terms like "agents," "zone of genius," and "disaggregation of economic accumulation" are used without explanation. A reader unfamiliar with AI agents or business strategy would struggle to follow the argument.

3. Missing evidence for major claims (lines 7-9): "We expect that the trend toward consolidation..." and the mention of "insane signing bonuses" need citations. The bracket note shows you know this, but readers need the evidence now to take the argument seriously.

Important Issues

4. Weak opening (line 1): The block quote feels like a disclaimer rather than an engaging hook. Consider starting with the sports analogy or a concrete example of how AI is changing career paths.

5. Underdeveloped core concept (lines 85-95): The "Tours of [Traded] Duty" section—which seems to be your main proposal—gets only five bullet points. This deserves substantial development with examples, implementation details, and potential obstacles.

6. Unclear AI connection: While you mention AI agents throughout, the connection between AI capabilities and why this career model works now (versus five years ago) isn't explicit. Why does AI make compressed expertise more valuable?

Minor Issues

7. Inconsistent formatting: The conversation blocks work well, but mixing them with quoted text from your 2019 piece creates visual confusion.

8. Unlinked references: Several [[wiki links]] point to notes that don't exist yet ("infinite games," "value note"). Consider noting when links are placeholders.

Specific Line-by-Line Feedback

Line 1: The opening quote frame distances readers from your ideas. Consider: "Five years ago, we wrote about tours of duty as an alternative to traditional careers. Today, AI is accelerating this shift in ways we didn't anticipate."

Lines 23-25: This paragraph introduces your main insight but gets buried. Consider moving it up and expanding: "The emergence of AI agents creates an intriguing possibility: what if experts brought their entire digital workforce with them to each engagement?"

Lines 94-95: "Deep engagement model" needs examples. What would this look like for a product manager? A data scientist? A marketing leader?

This has the bones of a compelling piece about the future of work. The sports analogy is genuinely novel, and your experience gives you credibility to make these predictions. Focus on developing one clear argument rather than exploring multiple threads simultaneously.

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Review generated by writing-style-reviewer agent

[github-actions]

📝 Writing Style Review: _notes/tour-of-duty-ai-era.md

Summary

This piece presents an intriguing evolution of the "tour of duty" career model for the AI era, using the sports industry as an analogy. While the core concept is compelling and the personal conversation excerpts add authentic insight, the piece feels more like brainstorming notes than a fully developed argument, with several unsupported claims and structural gaps that limit reader understanding.

Strengths

• Authentic voice and development: The inclusion of actual Slack conversations effectively shows the thinking process behind the concept, making abstract ideas feel grounded and real
• Compelling central metaphor: The sports model analogy (athletes being traded, front-loaded compensation, expertise-based matching) provides a clear framework for understanding how careers might work differently

Critical Issues

1. Unsupported foundational claims (lines 7-8): "We expect that the trend toward consolidation of resources...will continue" and the reference to "insane signing bonuses for AI talent, to layoffs of middle management" need citations or links to evidence. The author even notes "[Note to self to gather these]" - this signals the idea isn't ready for publication.

2. Undefined core concepts (line 15): "creating AI agents to take the workload that is delegable and taking those agents as a part of your whole work package" - what exactly are these agents? How do they work? The "Agents as Hiring Signal" section helps, but comes much later.

3. Incomplete argument structure (lines 77-79): The piece jumps from describing problems to proposing solutions without clearly establishing why the sports model specifically addresses the challenges identified in the AI era.

Important Issues

4. Status contradiction (frontmatter vs. content): The piece is marked as "budding" status but reads more like "seedling" - it's clearly still in active development with placeholder notes and incomplete thoughts.

5. Missing connections (line 102): "[link to infinite games]" appears as a placeholder, and several other wiki-links reference notes that may not exist yet ("[[Agency of Agents]]", "[[Pricing for Value Instead of Time]]").

6. Unclear audience transition (lines 47-57): The shift from quoting the 2019 framework to presenting the new 2026 version feels abrupt. Consider a transitional paragraph explaining what's changed since 2019.

Minor Issues

7. Formatting inconsistency: The conversation blocks are well-formatted, but some sections (like "Tours of [Traded] Duty") feel underdeveloped compared to others.

8. Terminology precision (line 93): "Deep engagement model" could be more specific - what makes it "deep" compared to current consulting models?

Line-by-Line Feedback

Line 7: "There will be long-tail careers available in large companies, but there will also be a huge market for talent grabbing" - This key premise needs evidence. What data supports the consolidation claim?

Lines 24-25: Consider defining "tours of duty" briefly before diving into the original framework, since some readers may be encountering this concept for the first time.

Line 69: "What could a Tour of Duty look like? (2026 version):" - This section feels rushed compared to the detailed 2019 explanation. The two points need more development to match the depth of analysis in the original framework.

Lines 98-101: "Could this model work only outside the top 100 companies? Does it only work if the top 100 companies participate and 'fund' it?" - These are crucial questions that deserve more exploration rather than just being listed as open questions.

This piece has the bones of a valuable contribution to thinking about future work models, but needs more evidence, clearer definitions, and fuller development of the argument before publication.

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Review generated by writing-style-reviewer agent