Detailed Analysis
A Reddit user has proposed the addition of a "Scenarios" tab to Claude's sidebar interface — a curated, profession-specific library of ready-made prompt flows designed to onboard non-technical users more effectively. The proposal identifies a structural gap in Claude's current user experience: while developers benefit from Claude Code as a purpose-built entry point, professionals in marketing, finance, HR, design, and operations encounter only a blank chat interface with no scaffolding to guide their first interactions. The suggested tab would organize prompt templates by profession, pair each with example outputs, and include a second layer of community publishing and monetization, allowing power users to share high-performing prompt flows and earn revenue through a sharing model.
The proposal carries practical weight because the friction it describes is well-documented in AI adoption research: most enterprise users underutilize AI tools not due to lack of interest, but due to a failure to discover relevant use cases for their specific roles. The blank-slate chat interface, while flexible, places the full burden of prompt engineering on the user — a significant cognitive barrier for professionals unfamiliar with how to frame requests effectively. By providing concrete, profession-aligned starting points with visible example outputs, a Scenarios tab would collapse the distance between "I have access to Claude" and "I know how to extract value from Claude" for a substantial portion of the workforce.
Research context reveals that Anthropic has already moved meaningfully in this general direction, though not through the specific mechanism proposed. The Claude Cowork tab — positioned alongside the standard Chat tab in Claude.ai — represents the closest existing analogue, shifting Claude from a conversational tool to one that actively executes tasks and delivers usable files such as spreadsheets and presentations. Agent Skills, launched with an open standard in December 2025, further extend this by offering modular, loadable expertise packs portable across Claude.ai, Claude Code, and the desktop application. Claude Projects provide persistent shared context useful for repeatable professional tasks like competitor analysis. Together, these features suggest Anthropic is actively layering structure atop the blank-slate interface, but without the explicit profession-based discoverability the proposal envisions.
The community publishing and monetization dimension of the proposal reflects a broader trend visible across the AI tools ecosystem — one in which prompt engineering is increasingly treated as a skilled, shareable, and monetizable form of intellectual labor. Platforms like GPT Store and Poe have experimented with creator monetization tied to AI tool usage, with mixed results. The proposal's model, tying revenue sharing to the quality and adoption of community-published prompt flows, would require careful governance to avoid low-quality content flooding the library. However, the underlying logic is sound: professionals who develop highly effective workflows for their specific domains represent a knowledge base that, if captured and organized, would accelerate adoption far more efficiently than any top-down curation effort Anthropic could execute alone.
Ultimately, the Scenarios proposal surfaces a tension central to Anthropic's product strategy at this stage of AI adoption: Claude's power is broad, but its discoverability is narrow. The gap between Claude Code — which requires a terminal and a paid subscription — and the general chat interface leaves a large middle segment of the workforce without an adequate on-ramp. Whether through a Scenarios tab, expanded Cowork functionality, or profession-specific Agent Skills packs, the underlying demand the proposal articulates is clear and commercially significant. As enterprise AI adoption accelerates through 2026, the platforms that best solve the "I don't know where to start" problem for non-technical professionals are likely to capture disproportionate engagement and retention among knowledge workers.
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