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GitHub Hot — 20 April 2026

20 April 2026·24 min readGitHubOpen SourceTools

Top 10 repos trending on GitHub this week — what they do, why they matter, and how to use them in your projects.

1. kyegomez/OpenMythos

4,070 stars this week · Python · ai anthropic attention claude

A PyTorch implementation of a speculative 'Recurrent-Depth Transformer' architecture reverse-engineered from public research to approximate what Claude's internal model structure might look like.

Use case

Researchers and engineers who want to experiment with looped/recurrent transformer architectures without waiting for Anthropic to publish internals can use this as a testbed. For example, if you want to benchmark whether recurrent depth (iterating the same block N times) gives better reasoning on math tasks than stacking unique layers, this gives you a runnable baseline to fork and modify rather than building from scratch.

Why it's trending

Anthropic's 'Claude Mythos' internal architecture name leaked/surfaced in community discussions this week, and GPT-5 speculation is at a fever pitch — any repo claiming to reconstruct frontier model internals from first principles immediately attracts attention from the ML reverse-engineering community. The 'looped transformer' design also aligns with recent published research on recurrent depth scaling, giving it legitimate academic credibility beyond pure speculation.

How to use it

  1. Install the package: pip install open-mythos
  2. Import and instantiate the model with its three-stage architecture:
from open_mythos import OpenMythos

model = OpenMythos(
    dim=512,
    depth=6,          # Prelude transformer blocks
    recurrent_depth=12, # How many times the recurrent block loops
    heads=8,
    dim_head=64
)
  1. Run a forward pass with a token sequence:
import torch
x = torch.randint(0, 50257, (1, 128))  # batch=1, seq_len=128
logits = model(x)
print(logits.shape)  # (1, 128, 50257)
  1. Swap in your own dataset and compare loss curves against a vanilla transformer of equivalent parameter count to actually measure whether the recurrent depth hypothesis holds.
  2. Read the referenced papers in the README (looped transformers, universal transformers) to understand what architectural claims are grounded vs. speculative before drawing any conclusions.

How I could use this

  1. Write a deep-dive blog post titled 'What is Claude Mythos? Dissecting OpenMythos's Recurrent-Depth Transformer' — run actual experiments comparing perplexity of OpenMythos vs. a standard GPT-2-scale transformer on a small corpus, publish the loss curves, and position it as evidence-based ML journalism rather than hype. This kind of 'I actually ran it' content performs extremely well on Hacker News.
  2. Build a 'Architecture Explorer' interactive tool on your blog using a Next.js canvas or React Flow diagram that visualizes the Prelude → Recurrent Block → Coda three-stage pipeline with adjustable loop depth — readers can see how information flows through N recurrent iterations. Gate the interactive version behind an email signup to grow your newsletter list.
  3. Use OpenMythos as a local fine-tuning target for a small domain-specific model (e.g., trained on your own blog posts + Stack Overflow TypeScript answers) and expose it as a 'Ask my blog AI' endpoint in Supabase Edge Functions — then write a post benchmarking response quality vs. calling Claude's API directly, which gives you both a portfolio piece and a genuine cost-vs-quality analysis companies care about.

2. browser-use/browser-harness

3,498 stars this week · Python

A minimal CDP-based browser automation harness where the LLM writes its own missing helper functions mid-task, making it genuinely self-healing rather than just retry-looping.

Use case

Traditional browser automation frameworks (Playwright scripts, Selenium, even higher-level agents) break when they hit an unexpected UI state and have no recovery path. Browser Harness solves this by letting the LLM edit helpers.py on the fly — if it needs to handle a file upload dialog it's never seen, it writes upload_file() itself and continues. Concrete example: an agent scraping job postings hits a CAPTCHA variant it wasn't trained for, writes a solve_captcha() helper inline, and keeps going without human intervention.

Why it's trending

It dropped this week riding the wave of Claude Code and Codex agentic workflows — the setup prompt is literally designed to be pasted into Claude Code, making it a zero-config on-ramp for developers already living in those tools. The '3 free concurrent remote browsers, no card required' offer also removes the biggest friction point for trying browser agents.

How to use it

  1. Follow install.md to enable Chrome remote debugging: launch Chrome with --remote-debugging-port=9222 and tick the checkbox in the setup tab so the harness can connect via CDP. 2. Paste the provided setup prompt directly into Claude Code or OpenAI Codex — it will read install.md, SKILL.md, and helpers.py automatically. 3. Give the agent a task in plain English (e.g., 'Go to my Supabase dashboard, find the last 10 signups, and paste them into a Google Sheet'). 4. Watch helpers.py grow — if the agent hits a missing action (e.g., selecting a dropdown), it appends the function itself and retries. 5. For deployment/sub-agents, swap the local browser for a remote one: grab a free API key at cloud.browser-use.com and point the harness at the remote CDP endpoint instead of localhost:9222.

How I could use this

  1. Auto-populate your blog's 'Reading List' or 'Bookmarks' section: give the agent a list of URLs and have it scrape title, og:description, and hero image from each, then write the results to a Supabase table that your Next.js blog reads from — no manual curation needed.
  2. Build a job-application sub-agent for your portfolio site: the agent visits a job board URL you paste, fills out the application form using your stored resume data from Supabase, handles file upload dialogs (writing upload_file() if missing), and logs the submission status back to a 'applications' table you can display as a live tracker.
  3. Wire it into your blog's AI features as a 'live research' tool: when you're drafting a post, trigger the agent to open competing articles on the topic, extract their headings and key claims into a structured JSON, and surface them in your editor sidebar via a Supabase realtime channel — so you can see what you're up against without leaving your writing flow.

3. Robbyant/lingbot-map

3,226 stars this week · Python

LingBot-Map is a real-time 3D scene reconstruction model that processes streaming video at ~20 FPS to build accurate 3D maps without the typical drift and latency problems of existing methods.

Use case

Traditional 3D reconstruction pipelines (SLAM, NeRF, 3DGS) either require expensive iterative optimization after capture or accumulate drift over long sequences — making live, online reconstruction impractical. LingBot-Map solves this by running feed-forward inference on a video stream frame-by-frame, maintaining geometric consistency over 10,000+ frames using a paged KV cache architecture. Concrete example: point a phone camera around a room and get a dense, drift-corrected 3D point cloud in real time without post-processing.

Why it's trending

Spatial computing interest is surging with Apple Vision Pro adoption and Meta's AR push, making real-time 3D reconstruction a hot research-to-product pipeline. This repo dropped alongside an arXiv paper and a HuggingFace model release, giving developers a ready-to-run checkpoint rather than just theory.

How to use it

  1. Set up the environment: conda create -n lingbot-map python=3.10 -y && conda activate lingbot-map, then install PyTorch for CUDA 12.8 per the repo instructions.,2. Pull the pretrained model from HuggingFace: from huggingface_hub import snapshot_download; snapshot_download('robbyant/lingbot-map', local_dir='./weights'),3. Feed a video or image sequence through the inference script — the model accepts streaming frames and outputs per-frame pointmaps + camera poses: python infer.py --input ./my_video.mp4 --weights ./weights --output ./output_3d,4. Visualize the resulting point cloud or mesh with the bundled viewer or export to .ply for use in Blender/Three.js.,5. For integration into a web app, post-process the .ply output with open3d or pipe camera pose JSON directly into a Three.js scene for browser-based 3D viewing.

How I could use this

  1. Blog portfolio showcase: Record a walkthrough video of your home office or dev setup, run it through LingBot-Map to generate a 3D point cloud, then embed the result as an interactive Three.js scene on your blog's About page — far more memorable than a headshot.
  2. Career tool — interactive resume space: Reconstruct a physical 'war room' whiteboard covered in your project notes or architecture diagrams, export the 3D scene, and link it from your portfolio as a navigable 3D space where hiring managers can 'walk through' your project history.
  3. AI blog feature — 'Scene of the Week': Build a Next.js API route that accepts a short uploaded video clip, shells out to a LingBot-Map inference container (e.g. via Modal or Replicate), and returns a 3D embed — turning any reader-submitted environment video into a shareable 3D artifact with zero post-processing effort.

4. vercel-labs/wterm

2,205 stars this week · TypeScript

A high-performance web terminal emulator from Vercel Labs with a Zig/WASM core and a first-class React component, giving you a real PTY-connected terminal in the browser with native text selection and accessibility.

Use case

Embedding a real terminal in a web app has always meant wrestling with xterm.js canvas rendering (no native find/select), or shipping a heavy iframe. wterm solves this by rendering to the DOM so clipboard, browser find (Ctrl+F), and screen readers just work — ideal for a blog with live code demos, an interactive CLI tool showcase, or a portfolio that lets visitors SSH into a sandboxed environment to run your projects.

Why it's trending

It dropped from Vercel Labs this week with a React package and a ~12KB WASM core, which is a direct shot at xterm.js dominance; the DOM-rendering approach is a genuinely new architectural choice that the frontend community is actively debating and benchmarking.

How to use it

  1. Install the React package: npm install @wterm/react @wterm/core
  2. Spin up a PTY WebSocket backend (e.g. node-pty + ws): npx @wterm/just-bash for a zero-config in-browser bash demo.
  3. Drop the component into your Next.js page:
import { Terminal } from '@wterm/react';

export default function TerminalPage() {
  return (
    <Terminal
      wsUrl="ws://localhost:3001"
      theme="monokai"
      rows={24}
      cols={80}
      className="rounded-lg shadow-xl"
    />
  );
}
  1. For the in-browser bash demo (no backend needed), swap to @wterm/just-bash and render <JustBash /> — useful for static hosting on Vercel.
  2. Style with CSS custom properties: --wterm-bg, --wterm-fg, --wterm-cursor to match your blog's design system.

How I could use this

  1. Embed a live @wterm/just-bash terminal in each code-heavy blog post so readers can run the exact commands you describe (e.g. a post about jq lets them pipe sample JSON right in the browser) — no CodeSandbox iframe, no round-trip to a server, zero cold start.
  2. Build an interactive CLI resume: pipe henry --help, henry experience, henry contact as commands in a wterm instance backed by a tiny Node PTY server on a $5 VPS, and link it from your portfolio — it's a memorable differentiator in job applications and screen-reader accessible out of the box.
  3. Wire wterm to a Supabase Edge Function that streams a sandboxed AI shell session: user types a prompt, the edge function calls OpenAI with streaming and writes stdout back over the WebSocket, so the AI's chain-of-thought reasoning and code output renders exactly like a terminal session — a far more visceral demo than a chat bubble UI.

5. lewislulu/html-ppt-skill

1,645 stars this week · HTML

A zero-build-step AgentSkill that lets an AI agent generate polished HTML slide decks with 36 themes, 31 layouts, and a full presenter mode — all as pure static files.

Use case

When you want an AI agent (Claude, GPT, etc.) to produce a shareable presentation — not a PowerPoint blob or a Google Slides link — but a self-contained HTML file you can host on any CDN instantly. Concrete example: a user types 'generate a 10-slide pitch deck for my SaaS idea' into Henry's blog AI chat, and the agent uses this skill to output a styled, animated HTML file the user can download and present from a browser with speaker notes and timer included.

Why it's trending

The 'AgentSkill' framing is landing at exactly the moment developers are wiring tools to autonomous agents (Claude Computer Use, OpenAI Assistants, LangChain tool-calling), and HTML-as-output is a clean, sandboxable artifact that doesn't require any runtime install on the receiving end.

How to use it

  1. Clone the repo: git clone https://github.com/lewislulu/html-ppt-skill && cd html-ppt-skill
  2. Browse /templates — each file is a standalone HTML deck. Open any in a browser; press S to launch presenter mode with speaker notes + timer.
  3. To use as an AgentSkill, expose the template files as context to your LLM and instruct it to fill in slide content by replacing placeholder tokens (title, body, theme class) in the chosen template.
  4. In a Next.js API route, have the LLM return a filled HTML string, write it to Supabase Storage as a .html object, and return a signed URL — the user gets a shareable link in seconds.
  5. To pick a theme programmatically, pass a ?theme=neon query param or swap the <body data-theme=''> attribute; no recompilation needed.

How I could use this

  1. Add a '/generate-deck' page on Henry's blog where a reader pastes a blog post URL, an API route fetches the post content, sends it to Claude with this skill's template as context, and returns a download link for a matching HTML slide deck — instant 'turn my article into a presentation' feature.
  2. Build a portfolio case-study presenter: for each project on Henry's portfolio, auto-generate a branded HTML deck (using the 'dark-pro' theme) with slides for Problem, Solution, Tech Stack, and Results, then embed the deck in an iframe on the project page so recruiters can click through it without leaving the site.
  3. Wire this as a tool in a Supabase Edge Function + LangChain agent: when Henry publishes a new post, the agent automatically generates a 5-slide summary deck, uploads it to Supabase Storage, and tweets the static URL — a fully automated content repurposing pipeline with zero manual slide work.

6. Nightmare-Eclipse/RedSun

1,639 stars this week · C++

RedSun is a Windows Defender privilege escalation PoC that exploits a logic flaw where Defender restores malicious files to their original location instead of removing them, enabling system file overwrites.

Use case

This repo is not a tool to integrate — it's a security vulnerability disclosure. The real problem it exposes is a defender-against-itself flaw: Windows Defender's cloud-tag remediation logic actively re-writes a flagged file back to disk, which an attacker can weaponize to overwrite protected system files and escalate to ADMIN. Example: a low-privilege process drops a payload, Defender 'cleans' it by restoring it to a sensitive path like System32, granting the attacker a privileged write primitive.

Why it's trending

This is trending because it's a genuinely embarrassing logic flaw in one of the most widely deployed AV products — Defender making the system less safe by design. The humorous, irreverent README tone went viral on security Twitter/X and Hacker News this week, amplifying reach beyond typical CVE disclosures.

How to use it

  1. DO NOT deploy or test this against systems you do not own — this is a live privilege escalation vulnerability with no CVE patch confirmed yet. 2. Read the README and study the C++ source to understand the cloud-tag remediation code path being abused. 3. If you run a Windows environment, audit whether Defender's cloud-delivered protection + automatic sample submission is enabled (Settings > Windows Security > Virus & threat protection > Manage settings). 4. As a mitigation, consider disabling 'Automatic sample submission' or restricting Defender's write permissions to sensitive directories via AppLocker/WDAC policies until a patch ships. 5. Follow the repo for a PoC drop and track Microsoft's MSRC advisory feed for a patch.

How I could use this

  1. Write a blog post titled 'When Your AV Is the Vulnerability' — do a deep technical breakdown of the cloud-tag remediation flaw, explain the write primitive concept with diagrams, and tie it to the broader pattern of security tools introducing attack surface. This kind of security-adjacent content drives serious dev traffic from Hacker News.
  2. Build a 'Security Pulse' sidebar widget for your blog that auto-fetches trending CVEs and vulnerability repos from GitHub's trending page + NVD API using a Supabase cron job + Next.js API route, so your blog always has a live security news feed without manual curation.
  3. Create an AI-powered 'Vulnerability Explainer' tool as a blog feature — paste in a CVE description or GitHub README excerpt, and GPT-4 returns a plain-English breakdown, affected systems, and suggested mitigations. Store explained CVEs in Supabase so they're searchable, and surface them as blog posts automatically.

7. Manavarya09/design-extract

1,143 stars this week · JavaScript · accessibility agent-skill ai chrome-extension

designlang scrapes any live website's computed styles via headless browser and outputs a complete, structured design system — tokens, Tailwind config, shadcn themes, Figma variables, WCAG audit — in one CLI command.

Use case

When building a blog or portfolio, you often want to match or adapt the visual language of a site you admire (or a client's existing site) without manually reverse-engineering their CSS. For example, Henry could run designlang against his own deployed blog to extract a canonical token file, then use it as a drift-check to ensure his local Tailwind config hasn't silently diverged from production — or run it against a competitor's blog to bootstrap a shadcn/ui theme that mirrors their spacing and typography scale without copying their code.

Why it's trending

MCP (Model Context Protocol) server support for Claude Code, Cursor, and Windsurf landed recently, making it directly usable as an AI agent skill — designers and developers can now pipe live design token extraction straight into their AI coding assistant's context, which is exactly the workflow the current AI-native dev tooling wave is chasing.

How to use it

  1. Run extraction on any live URL with no install required:
npx designlang@latest https://yoursite.com --out ./tokens
  1. Inspect the 11 output files in ./tokens/ — focus on tokens.json (W3C DTCG format), tailwind.config.ts, and shadcn-theme.json for immediate use in a Next.js project.
  2. Import the generated Tailwind config as a preset in your existing config:
// tailwind.config.ts
import extractedPreset from './tokens/tailwind.config'
export default { presets: [extractedPreset], content: ['./src/**/*.{ts,tsx}'] }
  1. To use as an MCP server inside Cursor/Claude Code, add to your .cursor/mcp.json:
{ "mcpServers": { "designlang": { "command": "npx", "args": ["designlang@latest", "--mcp"] } } }
  1. Run a drift-check between your codebase tokens and the live site to catch visual regressions before deploy:
npx designlang@latest https://yoursite.com --drift ./tokens/tokens.json

How I could use this

  1. Run designlang against Henry's deployed blog on every Vercel preview deployment via a GitHub Action, then diff the output tokens.json against the committed baseline — surface any token drift (e.g., a color or font-size accidentally changed) as a PR comment before it hits production.
  2. Build a 'design inspiration' sidebar feature for the blog: when Henry writes a post about a company or product, a server action calls designlang on that company's URL, extracts their brand voice summary and color palette, and renders a branded color swatch card inline in the post — automatically, no manual screenshot needed.
  3. Wire designlang's MCP server into a Claude-powered 'redesign assistant' page on the blog where visitors paste any URL, the MCP tool extracts the design tokens in real-time, and Claude Code suggests specific shadcn/ui component overrides Henry could use to match that aesthetic — turning it into a shareable AI design tool that drives traffic.

8. BuilderPulse/BuilderPulse

1,017 stars this week · various · ai builders indiehackers

BuilderPulse is a daily AI-curated brief that scans 300+ public signals (HN, Reddit, breaches, trending repos) and outputs one specific, time-sensitive build idea with a concrete 'why now' justification.

Use case

Indie hackers and solo developers waste hours trying to identify what's worth building next — BuilderPulse solves the signal-to-idea pipeline by cross-referencing trending pain points (e.g., a 609-point HN thread about an OAuth breach) and surfacing a specific, actionable build with a closing market window. Example: instead of vaguely knowing 'security tools are hot', you get 'build a CLI that audits zombie Google Workspace OAuth grants — today, because Vercel's breach just made this urgent'.

Why it's trending

It's hitting 1K stars this week because the Apr 20 entry directly tied a real Vercel security breach (live IOC, 609 HN points) to a 2-hour buildable tool — that kind of dated, specific, verifiable accuracy is rare and shareable. Developers are bookmarking it as a daily ritual, not just a one-time read.

How to use it

  1. Star and watch the repo so GitHub notifies you of daily commits — each day's brief is a new markdown file at en/YYYY/YYYY-MM-DD.md.,2. Read the 'Why now' section first — it contains the time-sensitive signal (HN thread, breach, product launch) that makes the idea viable this week but not next month.,3. Use the signal as a prompt seed: paste the full daily brief into Claude or GPT-4 and ask it to generate a project scaffold, tech stack recommendation, and a 2-hour MVP plan for your specific stack (e.g., 'Next.js + Supabase + TypeScript').,4. Cross-reference the linked HN threads or sources in the report to validate real user pain — check comment sentiment before committing to build.,5. If the idea fits your niche, set a 2-hour Pomodoro timer and ship a landing page or CLI prototype the same day — the 7-day window the repo references is real, trend leverage decays fast.

How I could use this

  1. Build a 'Signal to Post' pipeline on Henry's blog: a Supabase Edge Function that hits the BuilderPulse GitHub raw markdown URL daily, parses the build idea and 'why now' section, and auto-drafts a blog post in Henry's CMS with his take on whether he'd build it — turns a passive read into consistent content with minimal effort.
  2. Career tool angle: use BuilderPulse's daily build ideas as interview prep material. Build a small Next.js page that fetches the last 14 days of briefs, extracts the tech stack implied in each idea, and maps them against a resume skills list stored in Supabase — surfaces which trending problem spaces Henry is already qualified to pitch as side-project experience in job applications.
  3. AI feature for the blog: create a 'Build Radar' widget that embeds on Henry's blog sidebar, pulls the current day's BuilderPulse idea via GitHub raw API, and uses a streaming OpenAI call to rewrite the brief in Henry's voice with his opinion on viability — updates automatically each morning, gives readers a reason to return daily, and positions Henry as someone actively tracking the builder zeitgeist.

9. wbh604/UZI-Skill

965 stars this week · Python

A Claude-powered stock analysis engine that runs 22 data dimensions, 17 institutional methods, and 51 investor personas against any A-share/HK/US stock to produce a full Bloomberg-style HTML report in 5-8 minutes.

Use case

Manually researching a stock requires jumping between 5+ platforms (fundamentals, charts, analyst reports, DCF models) and still yields inconsistent results. This repo automates that entire workflow into a single Claude slash command — you type /stock-deep-analyzer:analyze-stock 国盾量子 and get a self-contained HTML report, a shareable image, and a copy-paste summary. Concrete example: a retail investor doing due diligence on a Chinese tech stock gets a multi-model consensus view (value investing + momentum + quant rules) without touching a spreadsheet.

Why it's trending

Trending this week because it's one of the first real-world Claude Code skill packs that demonstrates YAML persona-based agent role-play at scale (51 investor archetypes in one run), which directly maps to the agentic AI workflows Claude is pushing in its latest product updates. It's also riding the wave of Chinese retail investor interest in AI-assisted trading tools following volatile A-share market conditions.

How to use it

  1. Install via Hermes: hermes skills install wbh604/UZI-Skill/skills/deep-analysis — or clone the repo and point Claude Code at the skills directory manually.
  2. Ensure Python 3.9+ is installed and run pip install -r requirements.txt from the repo root.
  3. Configure your data source API keys (the repo supports 16+ sources — check skills/deep-analysis/config/sources.yaml for the list) and set them as environment variables.
  4. Open Claude Code and run the slash command: /stock-deep-analyzer:analyze-stock AAPL (swap ticker for any A/HK/US stock).
  5. Wait 5-8 minutes — output lands in output/ as a standalone HTML report, a 1080x1920 PNG, and a plain-text summary you can paste anywhere.

How I could use this

  1. Build a 'Portfolio Pulse' blog widget: run the analyzer weekly on 3-5 tech stocks Henry is tracking, parse the HTML report output with Cheerio, store the consensus score and key signals in Supabase, and render a live '/watchlist' page on the blog showing trend arrows and top bull/bear arguments — gives the blog a data-driven angle beyond opinion posts.
  2. Create a career tool that repurposes the 51-persona scoring pattern for resume review: replace investor personas with 51 hiring manager archetypes (FAANG eng manager, startup CTO, agency recruiter, etc.), feed a job description + resume text, and output a consensus 'hirability score' with dimension breakdowns — the same YAML role-play architecture from UZI-Skill applies directly.
  3. Add an AI 'Contrarian Take' feature to blog posts: whenever Henry publishes a post about a tech company or product (e.g., Supabase, Vercel, OpenAI), trigger a mini version of the multi-persona pipeline via Claude API with 5-7 analyst personas (bull, bear, quant, macro, insider) and append a collapsible 'What the bears are saying' section at the bottom of each post — automated devil's advocate powered by the same role-play pattern.

10. cathrynlavery/diagram-design

938 stars this week · HTML

A Claude Code skill that generates 13 editorial-quality, self-contained HTML+SVG diagram types that actually look good — no Mermaid, no build step, no generic rounded boxes.

Use case

When you write a technical blog post explaining a Next.js + Supabase architecture or an AI pipeline, you need diagrams that match your site's visual identity. Instead of wrestling with Figma or accepting Claude's default ugly output, you point this skill at your site URL, it reads your brand tokens, and produces a pixel-sharp SVG diagram in ~60 seconds. Example: you're explaining how your blog's RAG pipeline works — you get a clean architecture diagram with your exact accent color highlighting the vector store, not a grey box soup.

Why it's trending

Claude Code's agentic capabilities just hit mainstream adoption, and developers are building shareable 'skills' (prompt + asset bundles) for it the same way people shared GPT plugins in 2023. This repo is one of the first high-quality design-focused Claude Code skills to surface publicly, filling an obvious gap everyone has hit.

How to use it

  1. Clone the repo: git clone https://github.com/cathrynlavery/diagram-design and open any of the 13 HTML files directly in a browser to study the template structure and available variants (light/dark/editorial).
  2. Copy the Claude Code skill prompt from the repo's CLAUDE.md or skill definition file into your Claude Code project's .claude/ config, or paste it as a system prompt in a Claude Code session.
  3. Trigger it with a natural language request: 'Generate an architecture diagram for my blog's AI pipeline — embedding model → Supabase pgvector → Edge Function → Next.js API route. Match brand from https://yourblog.com.'
  4. Claude reads your site's CSS/colors, fills in the SVG template, and outputs a self-contained HTML file. Drop it into your Next.js public/ folder or inline the SVG directly in your MDX post.
  5. For programmatic use, strip the SVG from the HTML output and import it as a React component: import ArchDiagram from './diagrams/arch.svg' using @svgr/webpack or Next.js's built-in SVG support.

How I could use this

  1. Auto-generate a branded architecture diagram for every technical blog post at build time: create a /diagrams route in your Next.js blog where each post's frontmatter includes a diagram_type and nodes array, then use Claude Code with this skill to pre-render the SVG during next build and embed it as an inline component — consistent visual style across every post, zero Figma time.
  2. Build a 'system design explainer' tool for your portfolio: visitors input a job description mentioning a specific tech stack (e.g., 'event-driven microservices with Kafka'), your app calls Claude with this diagram skill to generate a sequence or swimlane diagram of how you'd architect that system, then displays it alongside a written breakdown — a concrete, interactive portfolio piece that signals system design competence to hiring managers.
  3. Add a 'visualize this' button to your blog's AI chat feature: when a reader asks your blog's AI assistant a question about a concept (e.g., 'how does JWT auth work in your app?'), detect if the answer involves a flow or state machine, invoke Claude with this skill's prompt to generate the matching SVG diagram, and stream it into the chat response alongside the text — turning abstract explanations into visual ones on demand.
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