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NeverSight/learn-skills.dev

Use for frontend/UI builds, redesigns, polish, or extensions. Before coding, generate a UI reference image, then closely match it instead of inventing a new direction.

¿Qué es learn-skills.dev?

learn-skills.dev is a Codex agent skill that use for frontend/UI builds, redesigns, polish, or extensions. Before coding, generate a UI reference image, then closely match it instead of inventing a new direction.

Compatible con~Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/NeverSight/learn-skills.dev/tree/HEAD/data/skills-md/adamholter/hive-mind-landing-page/frontend-from-generated-image

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Documentación

Frontend From Generated Image

Use this skill for any task that includes a frontend, screen, page, component set, dashboard, landing page, app shell, modal, chat UI, or other visible interface.

When the task is an iteration on an existing UI rather than a full redesign, treat the current UI as an important constraint.

This skill is intentionally strict: the generated or user-provided image is not a moodboard. It is the target. The implementation should look like a faithful translation of the image into working code.

Core rule

Before writing frontend code:

  1. Use the imagegen skill and the built-in OpenAI image generation tool.
  2. If iterating on an existing UI, first gather reference screenshots of the current experience unless the user explicitly wants a full redesign.
  3. Generate or accept at least one UI design reference image for the requested interface.
  4. Treat the reference image as the design source of truth, while preserving existing-product constraints when the task is iterative.
  5. Implement the frontend by copying the image's layout, hierarchy, spacing, typography, color relationships, asset placement, and overall composition as closely as practical in real code.
  6. Verify with screenshots and refine until the rendered result visibly matches the reference.

Do not skip the image-generation step unless the user explicitly tells you not to.

Workflow

  1. Read the product/task requirements and infer the key UI surfaces that need to exist.
  2. If the task is modifying an existing frontend, collect reference screenshots of the current UI first.
  3. Unless the user asked for a full redesign, use those screenshots as reference inputs and constraints for the design-generation prompt.
  4. Write a design-generation prompt for the interface.
  5. Generate the reference image with the built-in OpenAI image tool.
  6. Inspect the result and write a concrete visual brief before coding:
    • layout structure
    • alignment and spacing
    • type scale
    • component shapes
    • color palette
    • background treatment
    • visual density
    • major asset locations and dimensions
    • element ordering and z-index/layer relationships
    • motion cues implied by the composition
  7. Build the real frontend to match that image closely. Work one section or screen at a time when the reference contains multiple sections.
  8. Capture a screenshot of the implementation at the same viewport/aspect ratio as the reference.
  9. Compare screenshot to reference and fix mismatches in layout, spacing, scale, color, asset placement, and typography before moving on.
  10. If the generated image is weak, generate another one and use the stronger reference.

Prompting guidance for the image

When prompting the image model:

  • Describe the product type and exact screen to render.
  • Ask for a polished product UI mockup, not an abstract moodboard.
  • Specify the platform when relevant: desktop web app, mobile app, landing page, chat tool, admin panel, etc.
  • Include the desired tone and brand feel.
  • Ask for clear hierarchy, refined spacing, and realistic interface composition.
  • Request a full-screen UI screenshot/mockup perspective.
  • If the tool already has an established product direction, preserve it rather than reinventing it.
  • If iterating on an existing UI, provide the current UI screenshots as references whenever the image workflow supports that.
  • For iterative work, prompt for an improved version of the current design rather than a replacement design, unless the user explicitly asked for a full redesign.

Image-to-code translation rules

  • Do not rebuild illustrated/photo/3D assets with CSS, SVG primitives, gradients, or decorative divs. Use real image assets for those parts.
  • If the reference contains embedded product art, character art, photos, renderings, textures, icons, or abstract image panels, create explicit asset slots in the project and tell the user or future agent exactly where those files belong.
  • If the user supplied extracted assets, use those assets directly and clone the surrounding layout, components, typography, spacing, and effects.
  • If assets need transparent backgrounds, use the local fal-media-gen Pixelcut Background Remover workflow; do not send the user to external web tools, fake transparency with white boxes, or rebuild image assets in CSS.
  • For multi-section pages, translate one section at a time. Do not rough in all sections and hope the overall impression carries the design.
  • Preserve the exact visual hierarchy of the reference: what is biggest, what is quietest, what aligns to what, and where the eye lands first.
  • Measure proportions from the reference when possible: viewport coverage, section height, image aspect ratios, column widths, gutters, nav height, button height, and card density.
  • Use CSS variables or design tokens for colors, spacing, radii, and shadows that visibly repeat in the reference.
  • Avoid adding extra explanatory copy, extra cards, extra badges, or extra decoration that is not present in the reference.
  • When text in the generated image is unreadable or nonsensical, replace it with concise realistic copy while preserving the same line lengths, weight, hierarchy, and density.

Implementation guidance

  • Copy the generated image, do not merely "take inspiration from it."
  • For iterative work, preserve the recognizable product structure and only change what the task calls for.
  • Match the big decisions first: composition, scale, contrast, and layout rhythm.
  • Then match the smaller decisions: radii, borders, shadows, gaps, card proportions, and type treatment.
  • Preserve usability and accessibility while translating the image into production code.
  • If the generated image includes impossible or unclear details, infer the closest practical implementation without changing the visual direction more than necessary.

Fidelity checklist

Before calling the implementation done, check these against the reference image:

  • Same viewport framing: hero/section height, first-fold composition, and visible next-section hint when applicable.
  • Same macro layout: column count, alignment, negative space, overlap, asymmetry, and section rhythm.
  • Same visual weight: heading scale, supporting text scale, button prominence, image size, and card density.
  • Same color relationships: background, text contrast, accent amount, borders, shadows, and gradients.
  • Same component geometry: radii, stroke weights, padding, gaps, and icon/button sizes.
  • Same asset behavior: images use correct aspect ratio, cropping, object position, transparency, and layering.
  • Same responsive intent: mobile should feel like a faithful adaptation, not a different design.
  • No accidental additions: remove default nav text, generic feature cards, filler badges, random icons, and decorative effects that were not in the reference.

If the screenshot does not match, keep iterating. Name the top mismatches, patch them, and screenshot again.

Priority over default design behavior

If another frontend skill suggests inventing a fresh visual direction, this skill takes priority for UI direction. The generated image should anchor the design.

Deliverable behavior

For frontend tasks covered by this skill, the expected flow is:

  1. Generate the reference image.
  2. Extract or prepare any needed assets and define their project paths.
  3. Build the UI to match the reference section-by-section or screen-by-screen.
  4. Verify the implementation visually with screenshots.
  5. Refine toward the reference until the main mismatches are resolved.

Individual skills in this repo

This repo contains 17 individual skills — each has its own dedicated page.

NeverSight/learn-skills.dev

Landing page conversion optimization with layout rules, hero section design, and CTA psychology. Covers above-the-fold formula, social proof placement, mobile design, and F-pattern reading. Use for: startup landing pages, product pages, SaaS marketing, conversion optimization. Triggers: landing page, hero section, above the fold, conversion optimization, landing page design, cta button, hero image, landing page layout, saas landing page, product page design, conversion rate, landing page best practices

NeverSight/learn-skills.dev

Static artifact craft skill for self-contained HTML/CSS/JS documents: docs, sheets, dashboards, explainers, slides, tools, and landing pages. Use when the user asks for a durable, openable, shareable web deliverable they'll keep or hand off — a report, a dashboard, a slide deck, a data table, a page. Local folder first, temporary public link via tunnel (localhost.run), optional durable publish to Surge, GitHub Pages, or Cloudflare. Not for quick look renders, inline snippets, or throwaway scratch. Not for SPA frameworks, backend APIs, database apps, or production product UI.

NeverSight/learn-skills.dev

This skill should be used when building a personal productivity or operating system for a CEO, founder, or executive. Triggers on "personal OS", "annual review", "life planning", "goal setting system", "Bill Campbell", "Trillion Dollar Coach", "startup failure patterns", "Good to Great", "Level 5 Leadership", "Buy Back Your Time", "E-Myth", "Customer Development", "Steve Blank", "Small Is Beautiful", "Schumacher", "human-scale", "subsidiarity", "Buddhist economics", "permanence".

NeverSight/learn-skills.dev

This skill should be used when the user asks to 'check my wallet balance', 'show my token holdings', 'how much OKB do I have', 'what tokens do I have', 'check my portfolio value', 'view my assets', 'how much is my portfolio worth', 'what\\'s in my wallet', or mentions checking wallet balance, total assets, token holdings, portfolio value, remaining funds, DeFi positions, or multi-chain balance lookup. Supports XLayer, Solana, Ethereum, Base, BSC, Arbitrum, Polygon, and 20+ other chains. Do NOT use for general programming questions about balance variables or API documentation. Do NOT use when the user is asking how to build or integrate a balance feature into code.

NeverSight/learn-skills.dev

Guide to cryptocurrency portfolio management — asset allocation, rebalancing strategies, risk-adjusted returns, benchmarking, and tax-loss harvesting. Use when helping users build portfolios, rebalance holdings, or evaluate portfolio performance.

NeverSight/learn-skills.dev

Complete personal finance system — budgeting, debt payoff, investing, tax optimization, net worth tracking, and financial independence planning. Use when managing money, building wealth, paying off debt, planning retirement, or optimizing taxes. Zero dependencies.

NeverSight/learn-skills.dev

Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.

NeverSight/learn-skills.dev

Research-driven landing page generator. Pulls data from social media, websites, Google Maps, YouTube, forums, and optionally a CV to build a complete profile of a company or person. Analyzes the data as a marketer to extract competitive advantages, value props, audience insights, and brand voice. Then generates a compelling landing page with AI-generated images tailored to the audience. Use this skill when the user says things like: 'create a landing page for...', 'build a website for this business', 'make a landing page from their social media', 'generate a page for this company', 'research and build a site for...', 'landing page for my client', 'portfolio page for...', 'personal brand page', or provides social media handles, a website URL, or a CV and wants a page built from it.

NeverSight/learn-skills.dev

Use when the user asks to "personalize the email", "add merge tags / dynamic content", "set up conditional blocks per segment", or "make first-name and product-recommendation fields fall back safely"; produces a merge-tag map with per-tag fallbacks, conditional-block rules with per-segment variations, a fallback-safety audit, and a PII guard on what may render, informing the SEND E (Engagement/personalization) dimension. Not for building the segments — use list-segment-builder; not for writing the base copy — use email-creative-builder; not for scoring EQS or running vetoes — use email-quality-auditor. 邮件个性化/合并标签/条件内容块/兜底默认值

NeverSight/learn-skills.dev

Use when the user asks to "pre-launch check the landing page", "run a Quality-Score preflight", or "verify ad-to-page message match before launch"; produces an ad↔page continuity report — message-match gaps, above-the-fold check, page-speed read, form-friction count, mobile-render flags — as a pass/fix punch list. Not for redesigning or rewriting the page — use landing-optimizer; not for scoring the account or the RQS — use ad-account-auditor. 落地页体验预检/广告落地页一致性检查

NeverSight/learn-skills.dev

Operate the user's personal WeChat account through their self-hosted Wisdom service (BYOC) — check login status, list contacts/conversations, read and summarize history, search contacts, refresh the local history DB, and send messages only after explicit confirmation. Use when the user mentions 个人微信, 我的微信, WeChat personal chat, 微信聊天记录, 微信联系人, reading/summarizing WeChat messages, or sending a WeChat message.

NeverSight/learn-skills.dev

Use when designing multi-tenant OCI environments, standing up landing zone Terraform stacks, enforcing Security Zones, or planning hub-spoke network topology. Covers OCI-specific compartment hierarchies, multi-tenant IAM decision trees, Security Zone automation, CIS Foundations compliance, and DRG routing. Keywords: landing zone, compartments, Security Zone, hub-spoke, DRG, CIS, multi-tenant, tenancy, IAM policy.

NeverSight/learn-skills.dev

Avoid stereotypical AI-generated UI and produce cleaner, grounded frontend design. Use for frontend generation, redesign, and polish.

NeverSight/learn-skills.dev

Brand-first landing page designer — interviews the user to discover brand identity (adjectives, colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML output. Preferred over frontend-design for standalone landing/marketing pages where the user hasn't established visual direction yet. TRIGGER when: user asks to "create/design/build a landing page", "make a homepage for my project/product/service", "build a marketing page", or wants to promote an app/side project. Especially when they haven't defined brand colors, fonts, or visual style — the guided brand interview is the core value. DO NOT TRIGGER when: user has a specific design mockup to implement, wants a dashboard or app UI, needs component-level frontend work (buttons, forms, navbars), is building a multi-page application, or is restyling an existing page with known design tokens. Use frontend-design for those cases.

NeverSight/learn-skills.dev

Use when planning or restructuring homepage, service page, about page, campaign page, or section order for this marketing website. Trigger for narrative flow changes, section sequencing, proof placement, CTA pacing, and page-level composition decisions.

NeverSight/learn-skills.dev

Analyze innovation project workbooks and cost tracking data to surface portfolio-level insights, trends, and recommendations for where to focus innovation efforts.

NeverSight/learn-skills.dev

Maintain automatic personalization writeback from agent trajectories, logs, sidecar artifacts, and repeated user preferences. Use when a task produces reusable preferences, lessons, private user memory, project contracts, or candidate public skill rules without interrupting the user.

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