Add infographics field to ClaimData interface
Fixes TypeScript build error in automation: - Added infographics array field to ClaimData interface - Updated existing claims with empty infographics arrays - Enables automation scripts to add image data This resolves: "infographics does not exist in type ClaimData" 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@ -22,6 +22,11 @@ export interface ClaimData {
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stat: string; // The statistic (e.g., "90% accuracy")
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context: string; // Context explanation
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}>;
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infographics: Array<{
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filename: string; // File name in /public/assets/ (e.g., "amazon-process-diagram.png")
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alt: string; // Alt text describing the image
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caption?: string; // Optional caption
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}>;
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supportingContext: string; // Context section paragraph explaining methodology/application
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}
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@ -60,6 +65,11 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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{ stat: "83% accuracy", context: "Excel Skill passed 5 of 7 expert-level financial modeling tests" },
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{ stat: "8MB limit", context: "Maximum total file size for uploaded Skills per user" },
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],
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infographics: [
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{ filename: "claude-skills-workflow-diagram.png", alt: "Claude Skills workflow diagram showing how Skills integrate into Claude workflows and load instruction sets", caption: "How Skills integrate into Claude workflows - selective instruction loading" },
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{ filename: "claude-skills-implementation.png", alt: "Claude Skills implementation process diagram showing practical workflow steps without manual template copying", caption: "Skills implementation process - eliminating repetitive instruction pasting" },
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{ filename: "claude-skills-excel-performance.png", alt: "Excel Skills performance metrics chart showing 83% accuracy on expert-level financial modeling tests", caption: "Excel Skill benchmark: 83% accuracy on expert-level financial modeling" },
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],
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supportingContext: `Claude Skills represent a productivity feature launched October 16, 2025, enabling users to create reusable instruction sets. Rather than pasting templates or repeating preferences in each conversation, users define a Skill once with a SKILL.md file and folder structure, then activate relevant Skills automatically when needed. This approach targets high-volume repetitive work where structure remains consistent but data varies—monthly reports, client communications, and standardized analyses—with measurable time savings validated by enterprise adoption.`,
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},
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{
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@ -96,6 +106,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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{ stat: "95% code completion", context: "AI autocomplete handles proportion of routine coding; developer fixes bugs and adjusts for infrastructure" },
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{ stat: "$25", context: "Cost to build self-contained year-end summary feature entirely through vibe coding platform" },
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],
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infographics: [],
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supportingContext: `Orel Zilberman's experience building WriteStack demonstrates that AI coding tools create meaningful leverage for developers with existing technical expertise. The distinction between AI-assisted development (where developers provide architecture and debugging) and pure vibe coding (for self-contained features) reveals that speed gains from AI come alongside unchanged requirements for system understanding. Non-technical founders can prototype quickly but face maintenance challenges when scaling, making the cost-benefit analysis favor buying established SaaS tools over building custom internal software.`,
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},
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{
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@ -126,9 +137,8 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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"Detects goal-performance conflicts"
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],
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quote: `One-time interactive interview replaces multiple scheduling sessions`,
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keyStatistics: [
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],
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keyStatistics: [],
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infographics: [],
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supportingContext: `The structured prompt approach gathers information across employee basics, performance data, role requirements, development goals, available resources, and organizational needs before generating analysis. This prevents unrealistic plans and detects conflicts between what employees want and what their performance indicates they need.`,
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},
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{
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@ -162,6 +172,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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keyStatistics: [
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{ stat: "ChatGPT usage doubles the week after a hackathon", context: "" }
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],
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infographics: [],
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supportingContext: `The methodology focuses on experiential learning through hands-on prototype building, cross-functional collaboration, and rapid three-hour sprints that transform theoretical AI knowledge into practical tool usage.`,
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},
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{
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@ -192,9 +203,8 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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"Dependency surfacing"
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],
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quote: `When you automate a process without mapping dependencies, you shift work somewhere else.`,
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keyStatistics: [
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],
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keyStatistics: [],
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infographics: [],
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supportingContext: `The framework helps professionals and consultants prevent expensive implementation mistakes by mapping dependencies before automation. By applying systems thinking, teams can identify leverage points where small changes create system-wide improvements and avoid narrow optimizations that shift work to other parts of the organization.`,
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},
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{
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@ -225,9 +235,8 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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"Question sequence primacy"
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],
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quote: `Consultants sell question sequence, not proprietary data`,
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keyStatistics: [
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],
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keyStatistics: [],
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infographics: [],
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supportingContext: `The structured prompt approach covers seven research domains: market sizing, competitive landscape, regulatory environment, customer requirements, operational setup, financial viability, and risk assessment. The methodology produces executive summary, market scoring matrix, detailed entry plan with phases, budgets, timelines, and KPIs in 3,000-5,000 words. Output requires validation, source checking, and assumption stress-testing.`,
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},
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{
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@ -258,9 +267,8 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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"Framework prevents pilot failures"
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],
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quote: `Routine delivery unbundled from human coaching frees three hours daily`,
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keyStatistics: [
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],
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keyStatistics: [],
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infographics: [],
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supportingContext: `The approach requires role restructuring rather than simple task automation. Teachers transition to performance coaching roles, managers shift to decision arbitration functions, and training curriculum gets redesigned around outcomes rather than activity measures. The model reportedly transfers to operations teams, customer service, and compliance functions.`,
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},
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{
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@ -294,6 +302,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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keyStatistics: [
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{ stat: "2x revenue growth and 1.4x cost reductions", context: "High-performing functions that focus AI on revenue-generating activities versus laggards who optimize internal processes" }
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],
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infographics: [],
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supportingContext: `The framework is based on research from BCG's study of 1,250 companies and McKinsey's analysis of AI implementation patterns. It provides three audit questions to evaluate whether AI spending targets high-impact work: Does this cut costs or grow revenue? Did time savings convert to business results? Does this workflow touch customers or product? Top performers concentrate investment in R&D, sales, marketing, manufacturing, and IT—functions that directly impact the bottom line.`,
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},
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{
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@ -325,10 +334,11 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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],
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quote: `Write the press release before building anything.`,
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keyStatistics: [
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{ stat: "\$200 billion", context: "Annual sales from recommendation engine (35% of e-commerce revenue)" },
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{ stat: "$200 billion", context: "Annual sales from recommendation engine (35% of e-commerce revenue)" },
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{ stat: "25% cost reduction", context: "Warehouse operations savings through AI robotics" },
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{ stat: "\$100 billion", context: "Annual AI investment commitment" }
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{ stat: "$100 billion", context: "Annual AI investment commitment" }
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],
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infographics: [],
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supportingContext: `Amazon's five-phase approach covers: Working Backwards methodology, data foundation requirements, clear KPIs, organizational transformation, and governance frameworks. These strategies apply to organizations of any size implementing AI systems.`,
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},
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{
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@ -364,6 +374,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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{ stat: "30% faster improvement", context: "Forecasters who track accuracy vs. those who don't (Good Judgment Project)" },
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{ stat: "40% reduction", context: "Strategic blindspots through scenario planning" }
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],
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infographics: [],
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supportingContext: `The article addresses how AI automation affects specific competencies (0.7% of job skills) rather than entire roles, creating a divide between prediction (AI's strength) and judgment (human responsibility). It examines the compression of junior roles, the importance of decision documentation, and forecasting practice for building calibration. These insights apply to professionals navigating career resilience in AI-augmented environments.`,
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},
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{
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@ -399,6 +410,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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{ stat: "15x growth", context: "McDonald's China monthly AI transactions: 2,000 → 30,000" },
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{ stat: "68% report division", context: "C-suite executives say rushed AI integration creates organizational friction" }
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],
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infographics: [],
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supportingContext: `The article examines the shift from using AI as a task-completing assistant ("coworker" mode) to collaborative strategic advisor ("co-thinker" mode). Microsoft's research on Copilot users shows iterative collaboration drives high-value outcomes. Evidence from medical diagnosis, enterprise deployments (McDonald's China 15x growth), and organizational research (68% of C-suite report friction from rushed integration) demonstrates that staged implementation and intentional configuration maximize AI value while preventing organizational division.`,
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},
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{
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@ -429,9 +441,8 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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"Summary conclusions betray generation"
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],
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quote: ``,
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keyStatistics: [
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],
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keyStatistics: [],
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infographics: [],
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supportingContext: `These claims address how to transform AI-generated writing into natural-sounding prose. The techniques focus on eliminating mechanical patterns: using active voice instead of passive constructions, varying sentence length to avoid rhythmic predictability, removing clichéd phrases that saturate training data, minimizing unnecessary bullet points, and ending with crisp final lines rather than summary recaps.`,
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},
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{
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@ -465,6 +476,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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keyStatistics: [
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{ stat: "Framework + Context + Adjustments = Effective Prompts", context: "Combine specific analysis methods (like Lean 5 Whys), reference prior business context, and set response constraints for structured, actionable insights" }
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],
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infographics: [],
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supportingContext: `The framework emphasizes that configuration—not the underlying AI model—determines value extraction. By combining frameworks (like "Lean 5 Whys"), context (business details stored in memory), and adjustments (response constraints), users generate structured insights they can actually implement rather than generic advice that sits unused.`,
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},
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{
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@ -499,6 +511,7 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
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{ stat: "89% time savings", context: "Marketing director reduced report prep from 3 hours to 20 minutes" },
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{ stat: "70% faster", context: "Custom GPTs reduce project planning time" }
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],
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infographics: [],
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supportingContext: `The article emphasizes strategic feature mastery targeting specific workflow bottlenecks rather than broad feature exploration, demonstrating measurable productivity improvements and career advantages through focused ChatGPT utilization.`,
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},
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];
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