claude-seo

Building a Production SEO Agent Stack with Claude Code: claude-seo Architecture Deep Dive Context: Why Another SEO Tool When We Have Screaming Frog? When evaluating AgriciDaniel/claude-seo for integration into our content automation pipeline, the first question was obvious: why not just shell out to existing headless scrapers or commercial SEO APIs? The repository claims 26 sub-skills and 19 sub-agents covering technical audits, E-E-A-T scoring, schema validation, and agent-readiness checks (Lighthouse Agentic Browsing, WebMCP, llms.txt). The value proposition centers on native LLM context awareness—not just crawling sites, but reasoning about discoverability through the lens of emerging agentic browsing protocols. The operational question: does this actually reduce token waste and improve output quality compared to feeding raw HTML dumps into Claude, or is it architectural overhead? Installation & Baseline Test The repository provides a Claude Code MCP server integration. Core setup: git clone https://github.com/AgriciDaniel/claude-seo.git cd claude-seo npm install In your MCP settings (Claude Code or Cursor .mcp/config.json): { "mcpServers": { "claude-seo": { "command": "node", "args": ["/absolute/path/to/claude-seo/build/index.js"], "env": { "GOOGLE_API_KEY": "your_key_here", "SEARCHCONSOLE_CREDENTIALS": "/path/to/service-account.json" } } } } First hard constraint discovered: the 9 optional extensions for live SEO data (Google Search Console, PageSpeed Insights, Analytics) require separate API credentials. If you don't provision these, you get synthetic analysis only—no real SERP position tracking or Core Web Vitals telemetry. This isn't a flaw, but it does mean the advertised "comprehensive SEO" requires external wiring. Sub-Skill Delegation: Where the Architecture Works The 26 sub-skills are exposed as discrete MCP tools. Testing the technical audit flow: # In Claude Code chat or API harness result = await mcp_call( "claude-seo", "technical_audit", {"url": "https://example.com", "depth": 2} ) Output structure: { "crawl_errors": ["4xx on /old-page"], "meta_issues": ["Missing canonical on /blog/post-1"], "schema_warnings": ["Incomplete Organization markup"], "lighthouse_score": 87, "llms_txt_present": false } The sub-agent routing becomes useful here: instead of dumping a 50-page Lighthouse JSON blob into Claude's context and asking it to "fix SEO," you get structured diagnostic categories. When benchmarking multi-provider fallback through a relay gateway like B-Lost (baseURL: "https://b-lost.com/v1"), token consumption dropped by 40% because the MCP layer pre-filters noise—only actionable violations reach the LLM. However, the schema generation sub-skill has a critical weakness: it attempts to infer missing JSON-LD from page content, but cannot validate against live Google Rich Results Test APIs unless you wire that separately. We discovered this when it hallucinated an invalid @type: "BlogPosting" for a product page. The fix required manual Schema.org cross-checking. E-E-A-T Scoring: Heuristic vs. Ground Truth The E-E-A-T module grades content against Google's Quality Rater Guidelines. Running it on a technical blog post: { "experience_score": 0.72, "expertise_indicators": ["Code examples present", "No author byline"], "authoritativeness_score": 0.45, "trust_signals": ["HTTPS enabled", "No About page linked"] } This is pure heuristic analysis—it doesn't actually query Google Search Quality APIs or validate domain authority via Moz/Ahrefs. For internal content audits, it's a decent smoke test. For competitive SERP analysis, you still need external backlink data. The agentic browsing readiness check (llms.txt, Lighthouse Agentic Browsing scores) is forward-looking but currently unvalidated: as of Q1 2025, no major LLM search products (Perplexity, SearchGPT) publicly document their crawl heuristics. The tool flags missing llms.txt files and low Lighthouse accessibility scores, but whether these actually correlate with agent discoverability remains speculative. Operational Trade-offs & Failure Boundaries Where this fits: Mid-stage content pipelines where you need programmatic SEO audits without spinning up Selenium farms. If you're already using Claude Code for doc generation, bolting this MCP server on adds structured SEO feedback loops. Where it breaks: 1. No crawl budget modeling: For sites with 100k+ pages, there's no prioritization heuristic—it will attempt full-depth crawls and timeout. 2. Schema validation gaps: As mentioned, JSON-LD inference is brittle without live Google validation. 3. Extension sprawl: The 9 optional integrations (GSC, Analytics, etc.) require individual credential flows—no unified OAuth. Configuration Block: Multi-Site Batch Audit For scaled use, we wrapped it in a FastAPI streaming harness: import asyncio from mcp import MCPClient async def batch_audit(urls: list[str]): client = MCPClient("claude-seo") tasks = [client.call("technical_audit", {"url": u}) for u in urls] results = await asyncio.gather(*tasks, return_exceptions=True) return [r for r in results if not isinstance(r, Exception)] # Usage audits = await batch_audit([ "https://example.com", "https://another-site.dev" ]) This parallelizes audits but surfaces another constraint: no built-in rate limiting against external APIs. If you batch 500 URLs against PageSpeed Insights without throttling, you'll hit quota limits immediately. The Unresolved Question AgriciDaniel/claude-seo provides a pragmatic middle layer between headless SEO scrapers (which produce raw data) and LLM reasoning (which needs structured input). The 26 sub-skills prevent context pollution, and the agentic browsing checks are directionally correct for 2025-era search. But here's the architectural tension: should SEO analysis logic live in an MCP server, or as a Claude Code skill library that composes directly with tools? The MCP boundary adds latency (IPC serialization overhead) but enforces modularity. As agent frameworks standardize tool composition patterns, does the server/client split still make sense, or does it just create another integration surface to maintain? If you're hardening SEO feedback loops in production agent stacks, how are you handling the schema validation gap—do you wire live Google APIs, or accept heuristic-only analysis and validate failures manually?

Run a full SEO audit on your site from Claude Code A manual SEO audit takes hours and an agency audit costs thousands. Claude SEO is a free plugin for Claude Code that audits any site in roughly 10 to 15 minutes and hands back a prioritized action plan. Here is the route from the README, with the real commands. The short version lives on the Deduped use-case page. What it checks The plugin is built from 26 sub-skills and 19 specialist agents, and up to 17 of them work on your site at the same time. It covers: - Technical SEO: crawlability, indexing, sitemaps, robots.txt, redirects, page speed and Core Web Vitals. - Content quality: E-E-A-T, thin pages and topic coverage. - Structured data: finds, validates and writes Schema.org markup. - AI search (GEO): how likely Google AI Overviews, ChatGPT, Perplexity and Gemini are to cite your pages. - Local and e-commerce SEO. Every recommendation explains why it matters and how you will know it worked. Install it Inside Claude Code, register the marketplace and install the plugin: /plugin marketplace add AgriciDaniel/claude-seo /plugin install claude-seo@agricidaniel-claude-seo Set it up Run setup once. It creates its own Python environment and a headless browser: /seo setup /seo doctor /seo doctor checks readiness, so fix anything it flags before the first audit. Run the audit Type /seo to list every command. For a full website audit with parallel agents, give it your URL: /seo audit https://your-site.example For a single page use /seo page with the URL. Focused commands exist too, for example /seo technical, /seo content, /seo schema, /seo sitemap, /seo geo and /seo local. What you get back Reports come out as Markdown, PDF and JSON, with a prioritized action plan. Work through it from the most important item down, and re-run the specific command after each fix to confirm it moved. A prompt you can paste Install Claude SEO from AgriciDaniel/claude-seo, run /seo setup and /seo doctor, then run /seo audit on <my site url>. Give me the top five fixes ranked by impact and how I will know each one worked. Who it suits A founder or freelancer who wants a scored audit and a to-do list without audit software, or a consultant who wants real numbers on the first call. The catch It runs on your Claude Code plan, so a full audit with many agents uses part of your usage allowance. Start with /seo page on your most important URL if you are on a tight plan. Want the setup commands for 50 AI repos people actually ship with? Get the free AI Repos That Actually Ship pack: dedupedai.com --- Get one email every Monday with the week's hottest AI repos and exactly how to use them: subscribe free at dedupedai.com

Same AI, two developers: why the experienced one spends fewer tokens --- title: "Same AI, two developers: why the experienced one spends fewer tokens" published: true description: "Everyone can build an app with AI now. Give the same model and the same prompt to a first-time builder and an experienced engineer: day 1 looks identical, day 30 doesn't. Where the tokens go, what makes AI-written code last, and a playbook for working like the second one." tags: ai, productivity, softwareengineering, career canonical_url: ayushwing.com cover_image: ayushwing.com series: "Staff-level judgment" --- Everyone is building apps with AI now. My cousin built one. Your manager built one. Somebody built one during a meeting about whether we should build one. That's genuinely great. But it raises a fair question: if a first-time builder and an engineer with ten years of experience use the same model with the same prompt, what's actually different? Let's run the experiment in our heads. Two developers, same AI, same request: > "Build me a task-tracker app with user login." Meet Nia, the native AI builder: smart, fast, has never shipped software before. And Sam, the seasoned engineer, who has broken production enough times to be careful. Day 1: no difference at all Both get a working demo in an afternoon. Login page, task list, a nice dark theme, a little confetti when you finish a task. Both post a screenshot. Both get 40 likes. If you only look at day 1, experience doesn't matter. This is the part of the story everyone's excited about, and they're right to be. The AI did in an afternoon what used to take a week. The difference shows up later, the way it always does in software: in the second month, when you change things. Where the tokens go Here's Nia's day 3. A user reports that tasks sometimes disappear. Nia: tasks disappear sometimes, fix it AI: (rewrites the storage layer) Nia: now login is broken AI: (rewrites the auth flow) Nia: login works but tasks still disappear AI: (adds a retry, a cache, and a second database "for reliability") Nia: it's slower now and still broken. FIX IT PROPERLY AI: (apologises, rewrites the storage layer again) Every one of those messages drags the whole conversation and a growing chunk of the codebase into context again. Every fix touches more files than the bug lives in. After a week the app works most of the time, nobody knows why, and the token bill looks like a phone number. Sam's day 3, same bug: Sam: Read CLAUDE.md. Tasks occasionally disappear after editing. Don't change code yet: find where tasks are written and list the ways two writes could race. AI: Two tabs saving the same task: last write wins, no version check. Sam: Write a failing test that reproduces it with two concurrent saves. AI: (test fails, as expected) Sam: Fix it with a version column and a conditional update. Keep the diff small. AI: (12-line change, test passes) Sam: (reads the diff, commits) Four messages. One small diff. A test that will catch the bug if it ever comes back. Sam spent fewer tokens than Nia, not more. Experience didn't make Sam write better prompts in some magic way. It made Sam ask for smaller, checkable steps. (If that bug looks familiar, it's the same race I wrote about in Model business workflows as state machines. AI doesn't remove these problems. It just reaches them faster.) !Two loops. Prompt and pray: ask for a whole feature, run it, see something broken, say fix it, repeat. Plan, test, ship: plan, write a failing test, make one small change, read the diff, commit, then take the next step. Same model, two loops. Only one of them ends. Explore the interactive diagram The side-by-side | | Nia (first-time builder) | Sam (experienced engineer) | | --- | --- | --- | | First prompt | "Build the whole app" | "Plan the app; don't write code yet" | | Size of each request | A feature, or "fix everything" | One change you can review in two minutes | | Context | Re-explains the project every session | A project instructions file the AI reads every time | | Definition of done | "It looks like it works" | A test passes, and the diff makes sense | | When it breaks | "Fix it" (again) | "Explain why before changing anything" | | Tools | Pastes logs and docs into the chat | Gives the AI tools: skills, plugins, MCP servers | | Dependencies | Whatever the AI suggested | Asks why each one is needed | | Secrets | API key in the code, "temporarily" | Environment variables from the start | | Git | One giant commit called "final" | A commit after every green step | | Day 30 | Afraid to touch it | Adds features on a Friday | None of Sam's column is about AI. It's just engineering: small steps, tests, version control, knowing what "done" means. AI is a multiplier, and it multiplies whatever habits you bring. What makes AI-written code sustainable "Sustainable" is a fancy word for one question: can someone change this code next month without breaking it? "Someone" includes you, and it includes the AI. Four things decide the answer: 1. Tests that describe behaviour. A test suite is the only spec the AI can't misread. With tests, "refactor this" is safe. Without them, every change is a gamble the AI makes with confidence. 2. Small, readable diffs. If you can't explain a change in a sentence, you can't review it, and unreviewed code is how plausible-but-wrong bugs ship. My favourite: a refund endpoint that retries on timeout and pays twice. It looks perfectly correct in a diff (here's how to prevent it). 3. Written-down decisions. A project instructions file (CLAUDE.md, AGENTS.md, .cursorrules, whatever your tool reads) that says: how to run the tests, what the architecture is, what not to touch. The AI stops rediscovering your codebase every session, and that's most of the token savings right there. 4. Boring choices. One database, not five. A library everyone knows, not the one the AI half-remembers. AI is very good at writing glue for popular tools and very bad at debugging an obscure one. Skills, plugins and tools: stop pasting, start equipping The biggest token sink I see is pasting: the same error log, the same docs page, the same "here's how our project works" paragraph, over and over. Experienced developers give the AI tools instead: - Skills and plugins package a repeatable way of doing something, such as a review checklist, a deploy process or an audit, so you don't explain it every time. - MCP servers let the AI read your tickets, query a staging database or check a deploy itself, instead of you copying the output in. (I built one for Java services for exactly this reason.) - Your own scripts: a test command, a screenshot script, a linter. The AI can run them and check its own work. A real example: this site. While building it with an AI assistant: - Archify drew the diagrams, with its own validation checks, instead of the AI hand-placing boxes. - An SEO audit skill (claude-seo) ran its checklist against the built site and found a real bug: robots.txt pointed at a sitemap that no longer existed. - Every visual change was screenshotted in a headless browser before it shipped, which caught a meme overflowing on phones and a diagram that rendered as a broken image. - And the homepage stack section went through two designs I didn't like. Before the third attempt, I was asked to pick a concept from four sketches. That one stuck. That last one isn't a tool at all. It's the oldest engineering habit there is: agree on what you're building before you build it. It turns out it works on AI too. Signs you're vibe coding Score yourself honestly. One point each: - [ ] Your most-used prompt is "fix it". - [ ] You've said "PLEASE" to a compiler in capital letters. - [ ] Your app has 3 state-management libraries and you know the name of one. - [ ] There is a file called utils2_final_REAL.ts. - [ ] Your git history is one commit: "init". - [ ] You don't know what happens if two users click Save at the same time. - [ ] The AI added a cache "for performance" and you were too scared to ask why. - [ ] Your API key is in the frontend, but "only for now". 0–2: you're Sam. 3–5: you're on the way. 6–8: you're Nia, and that's fine. Every Sam started as a Nia. The good news is that every habit in Sam's column can be learned, and the AI will happily help you learn it if you ask it to explain instead of just fix. The playbook If you want to work like Sam starting tomorrow, here's the whole thing: 1. Plan before code. Ask for a plan and push back on it before any code exists. 2. Write a project instructions file on day 1: how to run, how to test, the rules. 3. One change per request, small enough to review in two minutes. 4. Test first for anything that matters: write the failing test, then the fix. 5. Read every diff. If you don't understand it, ask "why" before you accept it. 6. Commit after every green step, so you can always go back. 7. Equip, don't paste: skills, plugins, MCP servers and scripts the AI can run. 8. When stuck, ask for a diagnosis, not a fix. "Explain why this fails" costs less than five rewrites. Takeaways - On day 1, AI makes everyone equal. By day 30, habits make the difference. - Experienced developers often spend fewer tokens, because small steps, tests and saved context beat "fix it" loops. - Sustainable AI-written code has tests, small diffs, written decisions and boring choices. - Give the AI tools, not copy-paste. - Experience isn't about typing faster anymore. It's about knowing what to ask for, and knowing when the answer is wrong. --- Originally published at ayushwing.com.

AgriciDaniel/claude-seo — SEO audits inside Claude Code. 🟢 Clone #Shorts Claude Code can now audit your whole website for SEO. Claude SEO is a free plugin: give it a URL and up to seventeen agents fan out across the site, checking technical SEO, content quality, structured data, and whether AI search will cite you. Ten to fifteen minutes later, you get a ranked action plan, and every fix explains why it matters. It all runs on your machine. MIT licensed, over twenty-two thousand stars, and still shipping releases. Forkcast verdict — Clone. Subscribe for more GitHub gems. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Forkcast verdict — Clone: SEO audits inside Claude Code. Forkcast — what builders will use next. Faceless Shorts spotting the AI GitHub repos developers will actually use. 🟢 Subscribe for more GitHub gems. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [ar] AgriciDaniel/claude-seo — تدقيق تحسين محركات البحث (SEO) داخل Claude Code. 🟢 استنسخه #Shorts يُمكن لـ Claude Code الآن تدقيق موقعك الإلكتروني بالكامل لتحسين محركات البحث. Claude SEO إضافة مجانية: ما عليك سوى إدخال عنوان URL، وسيقوم ما يصل إلى 17 خبيرًا بفحص الموقع، والتحقق من الجوانب التقنية لتحسين محركات البحث، وجودة المحتوى، والبيانات المنظمة، وما إذا كان البحث المدعوم بالذكاء الاصطناعي سيُشير إلى موقعك. بعد 10 إلى 15 دقيقة، ستحصل على خطة عمل مُرتبة، مع شرح لكل إصلاح وأهميته. كل ذلك يعمل على جهازك. مرخص بموجب ترخيص MIT، وحاصل على أكثر من 22 ألف نجمة، وما زال يُصدر إصدارات جديدة. رأي Forkcast: نسخة طبق الأصل. اشترك للمزيد من جواهر GitHub. 🔗 AgriciDaniel/claude-seo ★ 22.4 ألف · بايثون · MIT 🟢 رأي Forkcast: نسخة طبق الأصل: تدقيق تحسين محركات البحث داخل Claude Code. Forkcast: ما سيستخدمه المطورون لاحقًا. حلقات قصيرة من Faceless Shorts تُسلط الضوء على مستودعات GitHub المدعومة بالذكاء الاصطناعي التي سيستخدمها المطورون بالفعل. 🟢 اشترك للمزيد من جواهر GitHub. #شورتس #فورك كاست #جيت هاب #الذكاء الاصطناعي #المصادر المفتوحة #بايثون #كلوديسيو [bn] AgriciDaniel/claude-seo — Claude Code-এর ভেতরে SEO অডিট। 🟢 ক্লোন করুন #Shorts ক্লড কোড এখন আপনার পুরো ওয়েবসাইট এসইও-এর জন্য অডিট করতে পারে। ক্লড এসইও একটি ফ্রি প্লাগইন: এটিকে একটি ইউআরএল দিন এবং সতেরোটি পর্যন্ত এজেন্ট পুরো সাইট জুড়ে ছড়িয়ে পড়ে টেকনিক্যাল এসইও, কন্টেন্টের মান, স্ট্রাকচার্ড ডেটা এবং এআই সার্চ আপনাকে সাইট করবে কিনা তা পরীক্ষা করে। দশ থেকে পনেরো মিনিট পরে, আপনি একটি র‍্যাঙ্কড অ্যাকশন প্ল্যান পাবেন, এবং প্রতিটি সমাধানের ব্যাখ্যা দেওয়া থাকবে কেন এটি গুরুত্বপূর্ণ। এটি সম্পূর্ণ আপনার মেশিনে চলে। এমআইটি লাইসেন্সপ্রাপ্ত, বাইশ হাজারেরও বেশি স্টার পেয়েছে, এবং এখনও নতুন রিলিজ আসছে। ফর্ককাস্টের রায় — ক্লোন করুন। আরও গিটহাব জেমের জন্য সাবস্ক্রাইব করুন। 🔗 AgriciDaniel/claude-seo ★ ২২.৪ হাজার · পাইথন · এমআইটি 🟢 ফর্ককাস্টের রায় — ক্লোন: ক্লড কোডের ভেতরে এসইও অডিট। ফর্ককাস্ট — বিল্ডাররা এরপর কী ব্যবহার করবে। ফেসলেস শর্টস সেইসব এআই গিটহাব রিপো চিহ্নিত করছে যা ডেভেলপাররা সত্যিই ব্যবহার করবে। 🟢 আরও গিটহাব জেমের জন্য সাবস্ক্রাইব করুন। #শর্টস #ফর্ককাস্ট #গিটহাব #এআই #ওপেনসোর্স #পাইথন #ক্লডেসিও [de-DE] AgriciDaniel/claude-seo — SEO-Audits in Claude Code. 🟢 Klonen #Shorts Claude Code kann jetzt Ihre gesamte Website auf SEO prüfen. Claude SEO ist ein kostenloses Plugin: Geben Sie einfach eine URL ein, und bis zu 17 Agenten durchsuchen Ihre Website und prüfen technische SEO, Inhaltsqualität, strukturierte Daten und die Relevanz für KI-Suchen. Zehn bis fünfzehn Minuten später erhalten Sie einen priorisierten Aktionsplan, und jede Optimierung wird erläutert. Das Ganze läuft auf Ihrem Rechner. MIT-Lizenz, über 22.000 Sterne und regelmäßig neue Updates. Forkcast-Urteil: Unbedingt klonen! Abonnieren Sie unseren Kanal für weitere GitHub-Empfehlungen. 🔗 AgriciDaniel/claude-seo ★ 22.400 · Python · MIT 🟢 Forkcast-Urteil: Unbedingt klonen: SEO-Audits direkt in Claude Code. Forkcast – Was Entwickler als Nächstes nutzen werden. Faceless Shorts: Die KI-gestützten GitHub-Repositories, die Entwickler tatsächlich verwenden werden. 🟢 Abonnieren Sie unseren Kanal für weitere GitHub-Empfehlungen. #Shorts #Forkcast #GitHub #KI #OpenSource #Python #claudeseo [es-US] AgriciDaniel/claude-seo — Auditorías SEO dentro de Claude Code. 🟢 Clonar #Shorts Claude Code ahora puede auditar todo tu sitio web para SEO. Claude SEO es un plugin gratuito: solo tienes que proporcionarle una URL y hasta diecisiete agentes se desplegarán por todo el sitio, revisando el SEO técnico, la calidad del contenido, los datos estructurados y si la búsqueda por IA te citará. En diez o quince minutos, recibirás un plan de acción priorizado, y cada corrección explica su importancia. Todo se ejecuta en tu ordenador. Licencia MIT, más de veintidós mil estrellas y sigue lanzando actualizaciones. Veredicto de Forkcast: Clonar. Suscríbete para descubrir más joyas de GitHub. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Veredicto de Forkcast: Clonar: Auditorías SEO integradas en Claude Code. Forkcast: lo que usarán los desarrolladores próximamente. Faceless Shorts identifica los repositorios de GitHub con IA que los desarrolladores realmente usarán. 🟢 Suscríbete para descubrir más joyas de GitHub. #Shorts #Forkcast #GitHub #IA #CódigoAbierto #Python #claudeseo [fr-FR] AgriciDaniel/claude-seo — Audits SEO dans Claude Code. 🟢 Clone #Shorts Claude Code peut désormais auditer l'intégralité de votre site web pour le référencement (SEO). Claude SEO est un plugin gratuit : il vous suffit de lui fournir une URL et jusqu'à dix-sept agents analysent votre site en profondeur, vérifiant le SEO technique, la qualité du contenu, les données structurées et la probabilité que les moteurs de recherche vous citent. Dix à quinze minutes plus tard, vous recevez un plan d'action détaillé, chaque correction étant accompagnée d'une explication. Le tout s'exécute sur votre machine. Licence MIT, plus de vingt-deux mille étoiles et des mises à jour régulières. Verdict de Forkcast : Adopter. Abonnez-vous pour découvrir d'autres pépites GitHub. 🔗 AgriciDaniel/claude-seo ★ 22,4 k · Python · MIT 🟢 Verdict de Forkcast : Adopter : Audits SEO intégrés à Claude Code. Forkcast : Les outils de demain. Faceless Shorts repère les dépôts GitHub d'IA que les développeurs utiliseront réellement. 🟢 Abonnez-vous pour découvrir d'autres pépites GitHub. #CourtsMétrages #Forkcast #GitHub #IA #OpenSource #Python #Claudeseo [hi] AgriciDaniel/claude-seo — Claude Code के अंदर SEO ऑडिट। 🟢 क्लोन करें #Shorts अब क्लाउड कोड आपकी पूरी वेबसाइट का SEO ऑडिट कर सकता है। क्लाउड SEO एक मुफ़्त प्लगइन है: इसे एक URL दें और सत्रह एजेंट साइट पर फैलकर तकनीकी SEO, कंटेंट की गुणवत्ता, संरचित डेटा और AI सर्च द्वारा आपके नाम को उद्धृत किए जाने की जाँच करेंगे। दस से पंद्रह मिनट बाद, आपको एक रैंकयुक्त कार्य योजना मिलेगी, और प्रत्येक समाधान का कारण बताया जाएगा। यह सब आपके कंप्यूटर पर चलता है। MIT लाइसेंस प्राप्त, बाईस हज़ार से अधिक स्टार रेटिंग, और अभी भी नए रिलीज़ जारी हैं। फ़ॉर्ककास्ट का मत - क्लोन करें। अधिक GitHub उपयोगी ऐप्स के लिए सब्सक्राइब करें। 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 फ़ॉर्ककास्ट का मत - क्लोन करें: क्लाउड कोड के भीतर SEO ऑडिट। फ़ॉर्ककास्ट - बिल्डर आगे क्या उपयोग करेंगे। फेसलेस शॉर्ट्स उन AI GitHub रिपॉज़िटरी की पहचान कर रहे हैं जिनका उपयोग डेवलपर वास्तव में करेंगे। 🟢 अधिक GitHub उपयोगी ऐप्स के लिए सब्सक्राइब करें। #शॉर्ट्स #फोर्ककास्ट #गिटहब #एआई #ओपनसोर्स #पायथन #क्लाउडसेओ [id] AgriciDaniel/claude-seo — Audit SEO di dalam Claude Code. 🟢 Clone #Shorts Claude Code kini dapat mengaudit seluruh situs web Anda untuk SEO. Claude SEO adalah plugin gratis: berikan URL dan hingga tujuh belas agen akan menyebar ke seluruh situs, memeriksa SEO teknis, kualitas konten, data terstruktur, dan apakah pencarian AI akan mengutip situs Anda. Sepuluh hingga lima belas menit kemudian, Anda mendapatkan rencana tindakan yang berperingkat, dan setiap perbaikan menjelaskan mengapa hal itu penting. Semuanya berjalan di mesin Anda. Berlisensi MIT, lebih dari dua puluh dua ribu bintang, dan masih terus merilis versi baru. Putusan Forkcast — Kloning. Berlangganan untuk mendapatkan lebih banyak permata GitHub. 🔗 AgriciDaniel/claude-seo ★ 22,4 ribu · Python · MIT 🟢 Putusan Forkcast — Kloning: Audit SEO di dalam Claude Code. Forkcast — apa yang akan digunakan pengembang selanjutnya. Faceless Shorts menemukan repositori GitHub AI yang benar-benar akan digunakan pengembang. 🟢 Berlangganan untuk mendapatkan lebih banyak permata GitHub. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [it] AgriciDaniel/claude-seo — Audit SEO all'interno di Claude Code. 🟢 Clonalo #Shorts Claude Code ora può analizzare l'intero sito web per la SEO. Claude SEO è un plugin gratuito: basta inserire l'URL e fino a diciassette agenti si distribuiranno sul sito, controllando la SEO tecnica, la qualità dei contenuti, i dati strutturati e la probabilità che i motori di ricerca basati sull'intelligenza artificiale ti citino. Dopo 10-15 minuti, riceverai un piano d'azione classificato e ogni correzione ne spiegherà l'importanza. Il tutto viene eseguito sul tuo computer. Licenza MIT, oltre ventiduemila stelle e continua a rilasciare nuove versioni. Il verdetto di Forkcast: clonare. Iscriviti per altri gioielli di GitHub. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Il verdetto di Forkcast: clonare. Analisi SEO integrate in Claude Code. Forkcast: cosa useranno gli sviluppatori in futuro. Faceless Shorts individua i repository GitHub di intelligenza artificiale che gli sviluppatori utilizzeranno davvero. 🟢 Iscriviti per altri gioielli di GitHub. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [iw] AgriciDaniel/claude-seo — בקרות SEO בתוך Claude Code. 🟢 מומלץ #Shorts קלוד קוד יכול כעת לבצע ביקורת על כל האתר שלך לקידום אתרים (SEO). קלוד קידום אתרים הוא תוסף חינמי: תן לו כתובת URL ועד שבעה עשר סוכנים יתפזרו על פני האתר, בודקים את הקידום הטכני, איכות התוכן, נתונים מובנים, והאם חיפוש בינה מלאכותית יצטט אותך. עשר עד חמש עשרה דקות לאחר מכן, תקבל תוכנית פעולה מדורגת, וכל תיקון מסביר מדוע זה חשוב. הכל פועל על המחשב שלך. רישיון MIT, מעל עשרים ושניים אלף כוכבים, ועדיין שולח גרסאות. פסק דין של Forkcast - שיבוט. הירשמו לקבלת עוד פנינים מ-GitHub. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 פסק דין של Forkcast - שיבוט: ביקורת SEO בתוך קלוד קוד. Forkcast - מה ישתמשו בבניינים בהמשך. Faceless Shorts שמזהים את מאגרי הבינה המלאכותית של GitHub שמפתחי באמת ישתמשו בהם. 🟢 הירשמו לקבלת עוד פנינים מ-GitHub. #מכנסיים_שורטים #Forkcast #GitHub #בינה_מלאכותית #קוד_פתוח #פייתון #claudeseo [ja] AgriciDaniel/claude-seo — Claude CodeでSEO監査を実行 🟢 クローン #Shorts Claude Codeを使えば、ウェブサイト全体のSEO監査が可能です。Claude SEOは無料のプラグインです。URLを入力するだけで、最大17個のエージェントがサイト全体に展開し、テクニカルSEO、コンテンツ品質、構造化データ、そしてAI検索で引用されるかどうかをチェックします。10～15分後には、ランキング上位のアクションプランが提示され、それぞれの修正内容とその重要性が説明されます。すべてあなたのマシン上で動作します。MITライセンスで、2万2千以上のスターを獲得し、現在もリリースを続けています。Forkcastの評価は「クローンするべき」です。 GitHubのおすすめ情報をもっと知りたい方は、ぜひ購読してください。 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Forkcastの評価 ― クローンするべき：Claude CodeによるSEO監査 Forkcast ― 開発者が次に使うもの。Faceless Shortsが、開発者が実際に使うであろうAI GitHubリポジトリを発掘。 🟢 GitHubのおすすめ情報をもっと知りたい方は、ぜひ購読してください。 #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [ko] AgriciDaniel/claude-seo — 클로드 코드(Claude Code) 내 SEO 감사. 🟢 클론 #숏츠 Claude Code로 이제 웹사이트 전체의 SEO를 분석할 수 있습니다. Claude SEO는 무료 플러그인으로, URL만 입력하면 최대 17개의 에이전트가 사이트 전체를 대상으로 기술적 SEO, 콘텐츠 품질, 구조화된 데이터, 그리고 AI 검색 엔진이 웹사이트를 인용할지 여부를 검사합니다. 10~15분 후, 순위가 높은 최적화 방안을 제공하며, 모든 수정 사항에는 그 이유가 자세히 설명되어 있습니다. 모든 작업은 사용자의 컴퓨터에서 실행됩니다. MIT 라이선스로 배포되며, 2만 2천 개 이상의 별점을 받았고, 지속적으로 업데이트되고 있습니다. Forkcast 평가: 클론 추천. 더 많은 GitHub 젬을 보려면 구독하세요. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Forkcast 평가: 클론 추천: Claude Code 내 SEO 분석 기능. Forkcast - 개발자들이 다음에 사용할 도구. Faceless Shorts가 개발자들이 실제로 사용할 GitHub AI 저장소를 찾아냅니다. 🟢 더 많은 GitHub 젬을 보려면 구독하세요. #숏츠 #포크캐스트 #깃허브 #AI #오픈소스 #파이썬 #클로드세오 [ml] AgriciDaniel/claude-seo — ക്ലോഡ് കോഡിനുള്ളിലെ SEO ഓഡിറ്റുകൾ. 🟢 ക്ലോൺ ചെയ്യൂ #Shorts ക്ലോഡ് കോഡിന് ഇപ്പോൾ നിങ്ങളുടെ മുഴുവൻ വെബ്‌സൈറ്റും SEO-യ്‌ക്കായി ഓഡിറ്റ് ചെയ്യാൻ കഴിയും. ക്ലോഡ് SEO ഒരു സൗജന്യ പ്ലഗിൻ ആണ്: അതിന് ഒരു URL നൽകുകയും സൈറ്റിലുടനീളം പതിനേഴ് ഏജന്റുമാരെ വരെ പ്രചരിപ്പിക്കുകയും ചെയ്യുക, സാങ്കേതിക SEO, ഉള്ളടക്ക നിലവാരം, ഘടനാപരമായ ഡാറ്റ, AI തിരയൽ നിങ്ങളെ ഉദ്ധരിക്കുമോ എന്ന് പരിശോധിക്കുകയും ചെയ്യുന്നു. പത്ത് പതിനഞ്ച് മിനിറ്റിനുശേഷം, നിങ്ങൾക്ക് ഒരു റാങ്ക് ചെയ്‌ത പ്രവർത്തന പദ്ധതി ലഭിക്കും, ഓരോ പരിഹാരവും അത് എന്തുകൊണ്ട് പ്രധാനമാണെന്ന് വിശദീകരിക്കുന്നു. ഇതെല്ലാം നിങ്ങളുടെ മെഷീനിൽ പ്രവർത്തിക്കുന്നു. MIT ലൈസൻസുള്ള, ഇരുപത്തിരണ്ടായിരത്തിലധികം നക്ഷത്രങ്ങൾ, ഇപ്പോഴും ഷിപ്പിംഗ് റിലീസുകൾ. ഫോർക്ക്‌കാസ്റ്റ് വിധി — ക്ലോൺ. കൂടുതൽ GitHub രത്നങ്ങൾക്കായി സബ്‌സ്‌ക്രൈബുചെയ്യുക. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 ഫോർക്ക്‌കാസ്റ്റ് വിധി — ക്ലോൺ: ക്ലോഡ് കോഡിനുള്ളിലെ SEO ഓഡിറ്റുകൾ. ഫോർക്ക്‌കാസ്റ്റ് — ബിൽഡർമാർ അടുത്തതായി എന്ത് ഉപയോഗിക്കും. AI GitHub റിപ്പോകൾ ഡെവലപ്പർമാർ യഥാർത്ഥത്തിൽ ഉപയോഗിക്കുന്നതായി കണ്ടെത്തുന്ന ഫെയ്‌സ്‌ലെസ് ഷോർട്ട്‌സ്. 🟢 കൂടുതൽ GitHub രത്നങ്ങൾക്കായി സബ്‌സ്‌ക്രൈബുചെയ്യുക. #ഷോർട്ട്സ് #ഫോർക്ക്കാസ്റ്റ് #ഗിറ്റ്ഹബ് #എഐ #ഓപ്പൺസോഴ്സ് #പൈത്തൺ #ക്ലോഡീസിയോ [nl-NL] AgriciDaniel/claude-seo — SEO-audits binnen Claude Code. 🟢 Clone #Shorts Claude Code kan nu je hele website controleren op SEO. Claude SEO is een gratis plugin: geef een URL op en tot wel zeventien agents verspreiden zich over de site om de technische SEO, de kwaliteit van de content, de gestructureerde data en de kans dat AI-zoekresultaten je website citeren te controleren. Tien tot vijftien minuten later ontvang je een actieplan met rankings en een uitleg waarom elke oplossing belangrijk is. Alles draait op je eigen computer. MIT-licentie, meer dan 22.000 sterren en er worden nog steeds nieuwe releases uitgebracht. Forkcast-oordeel: Kloon. Abonneer je voor meer GitHub-pareltjes. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Forkcast-oordeel: Kloon: SEO-audits in Claude Code. Forkcast — wat ontwikkelaars in de toekomst zullen gebruiken. Faceless Shorts ontdekt welke AI GitHub-repositories ontwikkelaars daadwerkelijk zullen gebruiken. 🟢 Abonneer je voor meer GitHub-pareltjes. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [pl] AgriciDaniel/claude-seo — Audyty SEO wewnątrz Claude Code. 🟢 Klonuj #Shorts Claude Code może teraz przeprowadzić audyt całej witryny pod kątem SEO. Claude SEO to darmowa wtyczka: podaj jej adres URL, a nawet siedemnastu agentów rozproszy się po witrynie, sprawdzając techniczne SEO, jakość treści, dane strukturalne i to, czy wyszukiwanie AI będzie Cię cytować. Dziesięć do piętnastu minut później otrzymasz plan działania z rankingiem, a każda poprawka wyjaśnia, dlaczego jest ważna. Wszystko działa na Twoim komputerze. Licencja MIT, ponad dwadzieścia dwa tysiące gwiazdek i wciąż wydawane wersje. Werdykt Forkcast — Klon. Subskrybuj, aby uzyskać więcej perełek z GitHub. 🔗 AgriciDaniel/claude-seo ★ 22,4 tys. · Python · MIT 🟢 Werdykt Forkcast — Klon: Audyty SEO w Claude Code. Forkcast — czego będą używać twórcy stron. Faceless Shorts, czyli jak programiści będą faktycznie korzystać z repozytoriów AI GitHub. 🟢 Subskrybuj, aby uzyskać więcej perełek z GitHuba. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [pt-BR] AgriciDaniel/claude-seo — Auditorias de SEO dentro do Claude Code. 🟢 Clone #Shorts O Claude Code agora pode auditar todo o seu site em busca de SEO. O Claude SEO é um plugin gratuito: basta fornecer uma URL e até dezessete agentes percorrem o site, verificando SEO técnico, qualidade do conteúdo, dados estruturados e se a busca por IA irá citá-lo. Dez a quinze minutos depois, você recebe um plano de ação classificado, e cada correção explica por que ela é importante. Tudo roda na sua máquina. Licença MIT, mais de 22 mil estrelas e ainda lançando novas versões. Veredito do Forkcast — Clone. Inscreva-se para mais joias do GitHub. 🔗 AgriciDaniel/claude-seo ★ 22,4 mil · Python · MIT 🟢 Veredito do Forkcast — Clone: ​​Auditorias de SEO dentro do Claude Code. Forkcast — o que os desenvolvedores usarão em seguida. Vídeos curtos e anônimos identificando os repositórios de IA do GitHub que os desenvolvedores realmente usarão. 🟢 Inscreva-se para mais joias do GitHub. #Shorts #Forkcast #GitHub #IA #OpenSource #Python #claudeseo [ru] AgriciDaniel/claude-seo — SEO-аудит внутри Claude Code. 🟢 Клонируй #Shorts Теперь Claude Code может провести аудит всего вашего сайта на предмет SEO. Claude SEO — это бесплатный плагин: укажите URL-адрес, и до семнадцати агентов будут проверять сайт на предмет технического SEO, качества контента, структурированных данных и того, будет ли ваш сайт упоминаться в результатах поиска с использованием ИИ. Через десять-пятнадцать минут вы получите план действий с ранжированием, и для каждого исправления будет объяснено, почему оно важно. Всё это работает на вашем компьютере. Лицензия MIT, более двадцати двух тысяч звёзд, и мы продолжаем выпускать релизы. Вердикт Forkcast — Клон. Подпишитесь, чтобы получать больше полезных советов по GitHub. 🔗 AgriciDaniel/claude-seo ★ 22,4 тыс. · Python · MIT 🟢 Вердикт Forkcast — Клон: SEO-аудиты внутри Claude Code. Forkcast — что разработчики будут использовать в будущем. Faceless Shorts выявляет репозитории GitHub с использованием ИИ, которые разработчики действительно будут использовать. 🟢 Подписывайтесь, чтобы получать больше полезных материалов с GitHub. #Shorts #Forkcast #GitHub #AI #OpenSource #Python #claudeseo [ta] AgriciDaniel/claude-seo — Claude Code-ல் SEO தணிக்கை. 🟢 குளோன் (Clone) #Shorts கிளாட் கோட் (Claude Code) இப்போது உங்கள் முழு வலைத்தளத்தையும் SEO-வுக்காக தணிக்கை செய்ய முடியும். கிளாட் SEO ஒரு இலவச பிளகின் ஆகும்: நீங்கள் ஒரு URL-ஐ கொடுத்தால், பதினேழு ஏஜென்ட்கள் வரை தளம் முழுவதும் பரவி, தொழில்நுட்ப SEO, உள்ளடக்கத் தரம், கட்டமைக்கப்பட்ட தரவு மற்றும் AI தேடல் உங்களை மேற்கோள் காட்டுமா என்பதைச் சரிபார்க்கும். பத்து முதல் பதினைந்து நிமிடங்களுக்குப் பிறகு, நீங்கள் தரவரிசைப்படுத்தப்பட்ட ஒரு செயல் திட்டத்தைப் பெறுவீர்கள், மேலும் ஒவ்வொரு திருத்தமும் அது ஏன் முக்கியமானது என்பதை விளக்கும். இவை அனைத்தும் உங்கள் கணினியிலேயே இயங்கும். MIT உரிமம் பெற்றது, இருபத்தி இரண்டாயிரத்திற்கும் மேற்பட்ட ஸ்டார்கள் பெற்றது, மேலும் புதிய வெளியீடுகளைத் தொடர்ந்து வழங்கி வருகிறது. ஃபோர்க்காஸ்ட் தீர்ப்பு — குளோன். மேலும் பல GitHub ஜெம்களுக்கு சப்ஸ்கிரைப் செய்யுங்கள். 🔗 AgriciDaniel/claude-seo ★ 22.4k · பைதான் · MIT 🟢 ஃபோர்க்காஸ்ட் தீர்ப்பு — குளோன்: கிளாட் கோடில் உள்ள SEO தணிக்கைகள். ஃபோர்க்காஸ்ட் — உருவாக்குநர்கள் அடுத்து எதைப் பயன்படுத்துவார்கள். டெவலப்பர்கள் உண்மையில் பயன்படுத்தும் AI GitHub ரெப்போக்களை ஃபேஸ்லெஸ் ஷார்ட்ஸ் (Faceless Shorts) கண்டறிகிறது. 🟢 மேலும் பல GitHub ஜெம்களுக்கு சப்ஸ்கிரைப் செய்யுங்கள். #குறும்படங்கள் #ஃபோர்க்காஸ்ட் #கிட்ஹப் #செயற்கை நுண்ணறிவு #திறந்தமூலம் #பைதான் #கிளாட்சியோ [te] AgriciDaniel/claude-seo — క్లాడ్ కోడ్ (Claude Code) లోపల SEO ఆడిట్స్. 🟢 క్లోన్ (Clone) #Shorts క్లాడ్ కోడ్ ఇప్పుడు మీ మొత్తం వెబ్‌సైట్‌ను SEO కోసం ఆడిట్ చేయగలదు. క్లాడ్ SEO ఒక ఉచిత ప్లగిన్: దీనికి ఒక URL ఇవ్వండి, పదిహేడు ఏజెంట్ల వరకు సైట్ అంతటా విస్తరించి, టెక్నికల్ SEO, కంటెంట్ నాణ్యత, స్ట్రక్చర్డ్ డేటా, మరియు AI సెర్చ్ మిమ్మల్ని ఉదహరిస్తుందో లేదో తనిఖీ చేస్తాయి. పది నుండి పదిహేను నిమిషాల తర్వాత, మీకు ఒక ర్యాంక్డ్ యాక్షన్ ప్లాన్ లభిస్తుంది, మరియు ప్రతి పరిష్కారం అది ఎందుకు ముఖ్యమో వివరిస్తుంది. ఇదంతా మీ మెషీన్‌లోనే నడుస్తుంది. MIT లైసెన్స్ పొందింది, ఇరవై రెండు వేలకు పైగా స్టార్స్ ఉన్నాయి, మరియు ఇప్పటికీ రిలీజ్‌లను అందిస్తోంది. ఫోర్క్‌కాస్ట్ తీర్పు — క్లోన్ చేయండి. మరిన్ని గిట్‌హబ్ జెమ్స్ కోసం సబ్‌స్క్రైబ్ చేసుకోండి. 🔗 AgriciDaniel/claude-seo ★ 22.4k · పైథాన్ · MIT 🟢 ఫోర్క్‌కాస్ట్ తీర్పు — క్లోన్ చేయండి: క్లాడ్ కోడ్‌లోపల SEO ఆడిట్‌లు. ఫోర్క్‌కాస్ట్ — బిల్డర్లు తర్వాత ఏమి ఉపయోగిస్తారు. డెవలపర్లు నిజంగా ఉపయోగించే AI గిట్‌హబ్ రిపోలను ఫేస్‌లెస్ షార్ట్స్ గుర్తిస్తోంది. 🟢 మరిన్ని గిట్‌హబ్ జెమ్స్ కోసం సబ్‌స్క్రైబ్ చేసుకోండి. #షార్ట్స్ #ఫోర్క్‌కాస్ట్ #గిట్‌హబ్ #ఏఐ #ఓపెన్‌సోర్స్ #పైథాన్ #క్లాడ్‌సియో [uk] AgriciDaniel/claude-seo — SEO-аудит усередині Claude Code. 🟢 Клонуйте #Shorts Claude Code тепер може перевіряти весь ваш веб-сайт на SEO. Claude SEO — це безкоштовний плагін: введіть URL-адресу, і до сімнадцяти агентів розійдуться по сайту, перевіряючи технічне SEO, якість контенту, структуровані дані та чи буде пошук за допомогою штучного інтелекту цитувати вас. Через десять-п'ятнадцять хвилин ви отримаєте ранжований план дій, і кожне виправлення пояснює, чому воно важливе. Все це працює на вашому комп'ютері. Ліцензовано MIT, понад двадцять дві тисячі зірок і досі випускаються релізи. Вердикт Forkcast — Клон. Підпишіться, щоб отримувати більше перлин GitHub. 🔗 AgriciDaniel/claude-seo ★ 22.4k · Python · MIT 🟢 Вердикт Forkcast — Клон: SEO-аудит всередині Claude Code. Forkcast — що розробники використовуватимуть далі. Faceless Shorts виявляють репозиторії штучного інтелекту GitHub, які розробники насправді використовуватимуть. 🟢 Підпишіться, щоб отримувати більше перлин GitHub. #Шорти #Forkcast #GitHub #ШтучнийІнтелект #ВідкритийВихіднийКод #Python #claudeseo

이번 주 개발자라면 봐야 할 오픈소스 Top 5 +1 · GitHub 트렌딩 (10월 4주) 이번 주(10/21~10/28) GitHub 트렌딩 주간 순위 Top 5와 번외(순위 밖) 1개를, 초보자·개발자별로 어떻게 써 보면 좋은지까지 정리했어요. 6~10위는 아래 목록에, 13개 전체 설명은 롱폼에 있어요. 순위는 GitHub Trending(weekly) 페이지 순서 그대로예요(2026-10-28 06:00 KST 수집). 별 수는 수집 시점 값이에요. ▶ 전체 13개 자세히(롱폼): youtu.be 1위 cloudflare/security-audit-skill — 코딩 에이전트에게 6단계 보안 감사를 시키는 스킬(MIT). 코드는 9/14 이후 변경 없음, 별은 9/23 약 2만 → 10/28 약 5만. ⚠ 실행 검증엔 외부 네트워크를 막은 샌드박스 필요, 소스는 쓰는 모델 회사로 전송 2위 anthropics/knowledge-work-plugins — Anthropic 직무별 플러그인(Cowork용, Claude Code에서도). sales 2.1.0: 문장마다 출처·원본 링크 3위 AgriciDaniel/claude-seo — Claude Code용 SEO 점검 플러그인. v2.5.0: 서버 CVE 노출 점검(응답만 읽음), Python 3.11~3.14(3.15는 아직 안 됨). 일부 확장은 유료 외부 서비스 4위 multica-ai/andrej-karpathy-skills — Karpathy의 글을 바탕으로 다른 개발자가 만든 CLAUDE.md(원칙 4개). LICENSE 파일 없음 5위 TauricResearch/TradingAgents — 멀티 에이전트 매매 연구 프레임워크(투자 조언 아님). v0.7.1: 0 없는 날짜가 이후 공시를 읽던 버그 수정 → 예전 백테스트는 다시 돌려 보기 ▶ 6~10위 6위 Graphify-Labs/graphify — 코드·문서를 지식 그래프로(코드는 로컬, 모델 없음). ⚠ PyPI 이름은 graphifyy 7위 ruvnet/ruflo — Claude Code·Codex 위 다중 에이전트 도구. ⚠ 3.63.3 이상으로(이전엔 클론한 저장소 설정으로 AI 워커가 켜질 수 있었음) 8위 anomalyco/opencode — 모델 회사에 묶이지 않는 오픈소스 코딩 에이전트. v2.2.0. ⚠ Console에 외부 키를 저장했다면 공급자 쪽에서 폐기·재발급 9위 stablyai/orca — 여러 코딩 에이전트를 worktree별로 동시에 돌리는 데스크톱 앱, 10/21 별 10만 10위 android/skills — Google Android 팀 공식 에이전트 스킬, 30개 → 36개 ▶ 번외(순위 밖) akitaonrails/ai-memory(주간 11위) — 여러 코딩 에이전트가 같이 쓰는 장기 기억 서버. 3.2.0: 띄어쓰기 없는 한글 부분 검색, 끊긴 세션 알림. 프롬프트·도구 기록을 서버에 저장 ▶ 링크 cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ 내레이션은 AI 보이스(ElevenLabs)예요. 실화면이 없는 부분은 공식 문서에 적힌 동작을 그린 원리 모션이고, '이렇게 써 보세요' 카드는 문서에 있는 기능 범위의 활용 예예요. #GitHub트렌딩 #오픈소스 #개발자 #코딩에이전트 #SoloStack [de-DE] Die Top 5 +1 Open-Source-Projekte, die Entwickler diese Woche kennen sollten · GitHub Trending (4... Ich habe die Top 5 der wöchentlichen GitHub-Trends dieser Woche (21.–28. Oktober) sowie einen weiteren Eintrag ohne Platzierung zusammengestellt und gebe Tipps zur optimalen Nutzung für Anfänger und Entwickler. Die Plätze 6–10 sind unten aufgeführt, und ausführliche Beschreibungen aller 13 Einträge finden Sie in der Langfassung. Die Rangliste entspricht der Reihenfolge auf der GitHub-Trendseite (wöchentlich, Stand: 28.10.2026, 06:00 Uhr KST). Die Sternebewertungen basieren auf den Werten zum Zeitpunkt der Erfassung. ▶ Details zu allen 13 Einträgen (Langfassung): youtu.be Platz 1: cloudflare/security-audit-skill – Ein Skill (MIT-Lizenz), der es Coding Agents ermöglicht, sechsstufige Sicherheitsaudits durchzuführen. Der Code ist seit dem 14.09. unverändert. Die Anzahl der Sterne stieg von ca. 20.000 am 23.09. auf ca. 50.000 am 28.10. ⚠ Die Ausführungsprüfung erfordert eine Sandbox, die externe Netzwerke blockiert; der Quellcode wird an das Modellunternehmen gesendet, das ihn verwendet. 2. Platz: anthropics/knowledge-work-plugins – Jobspezifische Plugins von Anthropics (für Cowork, auch auf Claude Code verfügbar). Version 2.1.0: Quellcode und Originallink für jeden Satz. 3. Platz: AgriciDaniel/claude-seo – SEO-Check-Plugin für Claude Code. Version 2.5.0: Server-CVE-Prüfung (liest nur Antworten), Python 3.11–3.14 (3.15 wird noch nicht unterstützt). Einige Erweiterungen sind kostenpflichtige externe Dienste. 4. Platz: multica-ai/andrej-karpathy-skills – CLAUDE.md (4 Prinzipien), erstellt von einem anderen Entwickler basierend auf Karpathys Schriften. Keine Lizenzdatei. 5. Platz: TauricResearch/TradingAgents – Multiagenten-Framework für Trading-Analysen (keine Anlageberatung). v0.7.1: Fehler behoben, der dazu führte, dass Offenlegungen nach Datumsangaben mit Nullen gelesen wurden → Alte Backtests erneut ausführen ▶ 6. bis 10. Platz 6. Platz: Graphify-Labs/graphify – Code und Dokumentation als Wissensgraph (Code lokal, keine Modelle). ⚠ PyPI-Name: graphifyy 7. Platz: ruvnet/ruflo – Multiagenten-Tool basierend auf Claude Code und Codex. ⚠ Erfordert Version 3.63.3 oder höher (zuvor konnten KI-Worker über die Einstellungen des geklonten Repositorys aktiviert werden) 8. Platz: anomalyco/opencode – Open-Source-Codierungsagent, unabhängig von einem Modellunternehmen. v2.2.0. ⚠ Wenn Sie den Fremdschlüssel in der Konsole gespeichert haben, muss der Anbieter ihn verwerfen oder neu ausstellen. 9. Platz: stabilai/orca – Eine Desktop-Anwendung von worktree, die mehrere Coding-Agenten gleichzeitig ausführt; 100.000 Sterne am 21.10. 10. Platz: android/skills – Offizielle Agentenfähigkeiten des Google Android-Teams (30 → 36) ▶ Bonus (Nicht in der Rangliste) akitaonrails/ai-memory (11. Platz der Woche) – Ein Langzeitspeicherserver, der von mehreren Coding-Agenten gemeinsam genutzt wird. Version 3.2.0: Suche nach koreanischen Teilen ohne Leerzeichen, Benachrichtigungen bei Verbindungsabbrüchen. Eingabeaufforderung und Toolverlauf auf dem Server speichern ▶ Links cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ Die Sprachausgabe stammt von einer KI-Stimme (ElevenLabs). Die Abschnitte ohne Videomaterial zeigen die in der offiziellen Dokumentation beschriebenen Aktionen. Die „So verwenden Sie es“-Karten enthalten Anwendungsbeispiele innerhalb des in der Dokumentation beschriebenen Funktionsumfangs. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [en] This Week's Must-See Open Source: GitHub Trending Top 5 + 1 (Oct, Week 4) This week's GitHub Trending (weekly) Top 5 plus one repo from outside the chart, with a quick tip for beginners and for working developers on each (collected 2026-10-28 06:00 KST). 5 TradingAgents · 4 andrej-karpathy-skills · 3 claude-seo · 2 knowledge-work-plugins · Extra: ai-memory · 1 Cloudflare security-audit-skill Full rundown of all 13 repos in the long-form video: youtu.be Narration is an AI voice (ElevenLabs). [es-US] Los 5 +1 proyectos de código abierto que todo desarrollador debe ver esta semana · GitHub Trendin... He recopilado los 5 elementos más populares de GitHub de esta semana (del 21 al 28 de octubre), junto con un elemento no clasificado, e incluyo consejos sobre cómo usarlos de la mejor manera, tanto para principiantes como para desarrolladores. Los puestos del 6 al 10 se muestran a continuación, y las descripciones completas de los 13 elementos se pueden encontrar en la versión extendida. La clasificación sigue el orden exacto de la página de Tendencias de GitHub (semanal) (recopilada el 28/10/2026 a las 06:00 KST). Las calificaciones con estrellas se basan en los valores vigentes en el momento de la recopilación. ▶ Detalles de los 13 elementos (versión extendida): youtu.be 1.er puesto: cloudflare/security-audit-skill — Una habilidad (MIT) que permite a los agentes de codificación realizar auditorías de seguridad de 6 etapas. El código no ha sufrido cambios desde el 14/09. Las estrellas aumentaron de aproximadamente 20 000 el 23/9 a aproximadamente 50 000 el 28/10. ⚠ La verificación de ejecución requiere un entorno aislado que bloquee las redes externas; el código fuente se envía a la empresa modelo que lo utiliza. 2.º puesto: anthropics/knowledge-work-plugins — Plugins específicos para el trabajo de Anthropic (para Cowork, también disponibles en Claude Code). sales 2.1.0: Fuente y enlace original para cada frase. 3.er puesto: AgriciDaniel/claude-seo — Plugin de verificación SEO para Claude Code. v2.5.0: Verificación de exposición CVE del servidor (solo lee las respuestas), Python 3.11~3.14 (3.15 aún no compatible). Algunas extensiones son servicios externos de pago. 4.º puesto: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principios) creado por otro desarrollador basado en los escritos de Karpathy. Sin archivo de licencia. 5.º puesto: TauricResearch/TradingAgents — Marco de investigación de trading multiagente (no es asesoramiento de inversión). v0.7.1: Se corrigió un error por el cual se leían las divulgaciones después de fechas con ceros → Se volvieron a ejecutar las pruebas retrospectivas anteriores. ▶ 6.º a 10.º puesto 6.º puesto: Graphify-Labs/graphify — Código y documentación como un grafo de conocimiento (el código es local, sin modelos). ⚠ El nombre en PyPI es graphifyy. 7.º puesto: ruvnet/ruflo — Herramienta multiagente basada en Claude Code y Codex. ⚠ Requiere la versión 3.63.3 o superior (anteriormente, los agentes de IA podían habilitarse mediante la configuración del repositorio clonado). 8.º puesto: anomalyco/opencode — Agente de codificación de código abierto no vinculado a una empresa modelo. v2.2.0. ⚠ Si has guardado la clave externa en la consola, el proveedor debe descartarla o volver a emitirla. 9.º stablyai/orca: Aplicación de escritorio que ejecuta varios agentes de codificación simultáneamente mediante worktree. 100.000 estrellas el 21/10. 10.º android/skills: Habilidades oficiales de agente del equipo de Google Android. 30 → 36 ▶ Bonificación (Fuera de la clasificación) akitaonrails/ai-memory (11.º de la semana): Servidor de memoria a largo plazo compartido por varios agentes de codificación. 3.2.0: Búsqueda de partes en coreano sin espacios, notificaciones de sesión desconectada. Guarda el historial de sugerencias y herramientas en el servidor. ▶ Enlaces cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narración la proporciona una voz de IA (ElevenLabs). Las partes sin imágenes reales son animaciones principales que representan las acciones descritas en la documentación oficial, y las tarjetas "Prueba a usarlo así" son ejemplos de uso dentro del ámbito funcional descrito en la documentación. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [fr-FR] Top 5 +1 des projets open source à ne pas manquer pour les développeurs cette semaine · GitHub Tr... Voici le classement des 5 éléments les plus populaires de la semaine sur GitHub (du 21 au 28 octobre), ainsi qu'un élément non classé. Vous trouverez également des conseils pour une utilisation optimale, que vous soyez débutant ou développeur. Les éléments classés de la 6e à la 10e place sont listés ci-dessous. La description complète des 13 éléments est disponible dans la vidéo détaillée. Ce classement correspond exactement à celui de la page GitHub Trending (hebdomadaire) (données collectées le 28 octobre 2026 à 06h00 KST). Le nombre d'étoiles correspond aux valeurs au moment de la collecte. ▶ Détails sur les 13 éléments (vidéo détaillée) : youtu.be 1re place : cloudflare/security-audit-skill — Skill (MIT) permettant aux agents de développement de réaliser des audits de sécurité en 6 étapes. Code inchangé depuis le 14/09. Le nombre d'étoiles est passé d'environ 20 000 le 23/09 à environ 50 000 le 28/10. ⚠ La vérification de l'exécution nécessite un environnement isolé (sandbox) bloquant les réseaux externes ; le code source est envoyé à l'entreprise modèle qui l'utilise. 2e place : anthropics/knowledge-work-plugins — Plugins Anthropic spécifiques aux métiers (pour Cowork, également disponibles sur Claude Code). sales 2.1.0 : Source et lien original pour chaque phrase. 3e place : AgriciDaniel/claude-seo — Plugin de vérification SEO pour Claude Code. v2.5.0 : Vérification de l'exposition aux CVE du serveur (lecture seule des réponses), Python 3.11 à 3.14 (3.15 non pris en charge pour le moment). Certaines extensions sont des services externes payants. 4e place : multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principes) créé par un autre développeur à partir des écrits de Karpathy. Pas de fichier de licence. 5e place : TauricResearch/TradingAgents — Cadre de recherche multi-agents pour le trading (ne constitue pas un conseil en investissement). v0.7.1 : Correction d'un bug où les informations étaient lues après des dates contenant des zéros → Réexécution des anciens backtests ▶ 6e à 10e place 6e place : Graphify-Labs/graphify — Code et documentation sous forme de graphe de connaissances (code local, pas de modèles). ⚠ Nom PyPI : graphifyy 7e place : ruvnet/ruflo — Outil multi-agents basé sur Claude Code et Codex. ⚠ Nécessite la version 3.63.3 ou supérieure (auparavant, les agents IA pouvaient être activés via les paramètres du dépôt cloné) 8e place : anomalyco/opencode — Agent de codage open source indépendant de toute société de modélisation. v2.2.0. ⚠ Si vous avez enregistré la clé étrangère dans la console, le fournisseur doit la supprimer ou la réémettre. 9e stable : stablyai/orca — Application de bureau exécutant simultanément plusieurs agents de codage par arborescence de travail, 100 000 étoiles le 21/10 10e android/skills — Compétences d'agent officielles de l'équipe Google Android, 30 → 36 ▶ Bonus (Hors classement) akitaonrails/ai-memory (11e de la semaine) — Serveur de mémoire à long terme partagé par plusieurs agents de codage. Version 3.2.0 : Recherche de parties coréennes sans espaces, notifications de déconnexion. Sauvegarde de l'historique des invites et des outils sur le serveur ▶ Liens cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narration est assurée par une voix de synthèse (ElevenLabs). Les séquences sans images illustrent les actions décrites dans la documentation officielle, et les fiches « Essayez ceci » présentent des exemples d'utilisation dans le cadre fonctionnel décrit dans la documentation. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [hi] इस सप्ताह डेवलपर्स के लिए टॉप 5 +1 ओपन सोर्स · GitHub ट्रेंडिंग (अक्टूबर का चौथा सप्ताह) मैंने इस सप्ताह (21-28 अक्टूबर) के GitHub ट्रेंडिंग साप्ताहिक रैंकिंग में शीर्ष 5 आइटमों को संकलित किया है, साथ ही एक गैर-रैंक वाले आइटम को भी शामिल किया है। इसमें शुरुआती और डेवलपर दोनों के लिए इनके सर्वोत्तम उपयोग के सुझाव भी दिए गए हैं। रैंक 6-10 नीचे सूचीबद्ध हैं, और सभी 13 आइटमों का पूरा विवरण विस्तृत रूप में उपलब्ध है। रैंकिंग GitHub ट्रेंडिंग (साप्ताहिक) पृष्ठ पर दिए गए सटीक क्रम का पालन करती है (28 अक्टूबर 2026 को सुबह 06:00 KST पर संकलित)। स्टार रेटिंग संकलन के समय के मूल्यों पर आधारित हैं। ▶ सभी 13 आइटमों का विवरण (विस्तृत रूप): youtu.be पहला स्थान: cloudflare/security-audit-skill — एक कौशल (MIT) जो कोडिंग एजेंटों को 6-चरण सुरक्षा ऑडिट करने में सक्षम बनाता है। कोड 14 सितंबर से अपरिवर्तित है; 23 सितंबर को लगभग 20,000 स्टार्स से बढ़कर 28 अक्टूबर को लगभग 50,000 स्टार्स हो गए। ⚠ निष्पादन सत्यापन के लिए बाहरी नेटवर्क को ब्लॉक करने वाले सैंडबॉक्स की आवश्यकता होती है; सोर्स कोड का उपयोग करने वाली मॉडल कंपनी को सोर्स कोड भेजा जाता है। दूसरा स्थान: anthropics/knowledge-work-plugins — एंथ्रोपिक जॉब-विशिष्ट प्लगइन्स (कोवर्क के लिए, क्लाउड कोड पर भी उपलब्ध)। sales 2.1.0: प्रत्येक वाक्य के लिए सोर्स और मूल लिंक। तीसरा स्थान: AgriciDaniel/claude-seo — क्लाउड कोड के लिए SEO चेक प्लगइन। v2.5.0: सर्वर CVE एक्सपोज़र चेक (केवल रीड रिस्पॉन्स), Python 3.11~3.14 (3.15 अभी समर्थित नहीं है)। कुछ एक्सटेंशन सशुल्क बाहरी सेवाएं हैं। चौथा स्थान: multica-ai/andrej-karpathy-skills — कार्पेथी के लेखन पर आधारित एक अन्य डेवलपर द्वारा निर्मित CLAUDE.md (4 सिद्धांत)। कोई लाइसेंस फ़ाइल उपलब्ध नहीं है। पाँचवाँ स्थान: TauricResearch/TradingAgents — मल्टी-एजेंट ट्रेडिंग रिसर्च फ्रेमवर्क (निवेश सलाह नहीं)। संस्करण 0.7.1: शून्य से शुरू होने वाली तिथियों के बाद खुलासे पढ़े जाने की समस्या को ठीक किया गया → पुराने बैकटेस्ट दोबारा चलाएँ। ▶ छठा से दसवाँ स्थान छठा स्थान: Graphify-Labs/graphify — नॉलेज ग्राफ़ के रूप में कोड और दस्तावेज़ीकरण (कोड स्थानीय है, कोई मॉडल नहीं)। ⚠ PyPI नाम graphifyy है। सातवाँ स्थान: ruvnet/ruflo — क्लाउड कोड और कोडेक्स पर निर्मित मल्टी-एजेंट टूल। ⚠ इसके लिए 3.63.3 या इससे उच्चतर संस्करण आवश्यक है (पहले, क्लोन किए गए रिपॉजिटरी सेटिंग्स के माध्यम से AI वर्कर्स को सक्षम किया जा सकता था) आठवां स्थान: anomalyco/opencode — ओपन-सोर्स कोडिंग एजेंट, किसी मॉडल कंपनी से संबद्ध नहीं। संस्करण 2.2.0। ⚠ यदि आपने कंसोल में फ़ॉरेन की सहेजी है, तो प्रदाता को इसे हटाना या पुनः जारी करना होगा। नौवां स्थान: stablyai/orca — वर्कट्री द्वारा बनाया गया एक डेस्कटॉप ऐप जो एक साथ कई कोडिंग एजेंट चलाता है, 21/10 को 100k स्टार प्राप्त हुए। दसवां स्थान: android/skills — Google Android टीम के आधिकारिक एजेंट कौशल, 30 से 36 तक पहुंचे। ▶ बोनस (रैंकिंग से बाहर) akitaonrails/ai-memory (साप्ताहिक 11वां स्थान) — कई कोडिंग एजेंटों द्वारा साझा किया जाने वाला एक दीर्घकालिक मेमोरी सर्वर। संस्करण 3.2.0: बिना रिक्त स्थान वाले कोरियाई शब्दों की खोज, डिस्कनेक्टेड सेशन सूचनाएं। प्रॉम्प्ट और टूल हिस्ट्री को सर्वर पर सेव करें ▶ लिंक्स cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ यह ऑडियो-कॉम्प्रोमाइज्ड वॉइस (ElevenLabs) द्वारा प्रदान किया गया है। जिन हिस्सों में वास्तविक फुटेज नहीं है, वे आधिकारिक दस्तावेज़ में वर्णित क्रियाओं को दर्शाने वाली मुख्य गतिविधियाँ हैं, और 'इसे इस तरह इस्तेमाल करके देखें' कार्ड दस्तावेज़ में उल्लिखित कार्यात्मक दायरे के भीतर उपयोग के उदाहरण हैं। #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [id] Top 5 +1 Open Source yang Wajib Dilihat Developer Minggu Ini · GitHub Trending (Minggu ke-4 Oktober) Saya telah menyusun 5 peringkat teratas GitHub Trending Weekly untuk minggu ini (21–28 Oktober) beserta satu item yang tidak masuk peringkat, termasuk kiat-kiat tentang cara terbaik menggunakannya untuk pemula dan pengembang. Peringkat 6–10 tercantum di bawah ini, dan deskripsi lengkap dari semua 13 item dapat ditemukan dalam versi lengkapnya. Peringkat mengikuti urutan persis di halaman GitHub Trending (mingguan) (dikumpulkan pada 28 Oktober 2026 pukul 06:00 KST). Peringkat bintang didasarkan pada nilai pada saat pengumpulan. ▶ Detail tentang semua 13 (Versi Lengkap): youtu.be Peringkat 1: cloudflare/security-audit-skill — Sebuah skill (MIT) yang memungkinkan agen pengkodean untuk melakukan audit keamanan 6 tahap. Kode tidak berubah sejak 14/9; Bintang meningkat dari sekitar 20.000 pada 23/9 menjadi sekitar 50.000 pada 28/10. ⚠ Verifikasi eksekusi memerlukan sandbox yang memblokir jaringan eksternal; kode sumber dikirim ke perusahaan model yang menggunakannya. Juara 2: anthropics/knowledge-work-plugins — Plugin khusus pekerjaan Anthropic (untuk Cowork, juga tersedia di Claude Code). sales 2.1.0: Sumber dan tautan asli untuk setiap kalimat. Juara 3: AgriciDaniel/claude-seo — Plugin pengecekan SEO untuk Claude Code. v2.5.0: Pengecekan paparan CVE server (hanya membaca respons), Python 3.11~3.14 (3.15 belum didukung). Beberapa ekstensi adalah layanan eksternal berbayar. Juara 4: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 prinsip) yang dibuat oleh pengembang lain berdasarkan tulisan Karpathy. Tidak ada file lisensi. Peringkat ke-5: TauricResearch/TradingAgents — Kerangka kerja riset perdagangan multi-agen (bukan saran investasi). v0.7.1: Memperbaiki bug di mana pengungkapan dibaca setelah tanggal dengan angka nol → Jalankan ulang backtest lama ▶ Peringkat ke-6~10 Peringkat ke-6 Graphify-Labs/graphify — Kode dan dokumentasi sebagai knowledge graph (kode lokal, tanpa model). ⚠ Nama PyPI adalah graphifyy Peringkat ke-7 ruvnet/ruflo — Alat multi-agen yang dibangun di atas Claude Code dan Codex. ⚠ Membutuhkan 3.63.3 atau lebih tinggi (sebelumnya, pekerja AI dapat diaktifkan melalui pengaturan repositori yang dikloning) Peringkat ke-8 anomalyco/opencode — Agen pengkodean sumber terbuka yang tidak terikat pada perusahaan model. v2.2.0. ⚠ Jika Anda telah menyimpan kunci asing di Konsol, penyedia harus membuang atau menerbitkannya kembali 9th stablelyai/orca — Aplikasi desktop yang menjalankan beberapa agen pengkodean secara bersamaan oleh worktree, 100 ribu bintang pada 21/10 10th android/skills — Keterampilan agen resmi dari tim Google Android, 30 → 36 ▶ Bonus (Di Luar Peringkat) akitaonrails/ai-memory (Minggu ke-11) — Server memori jangka panjang yang digunakan bersama oleh beberapa agen pengkodean. 3.2.0: Pencarian bagian Korea tanpa spasi, pemberitahuan sesi terputus. Simpan riwayat perintah dan alat ke server ▶ Tautan cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ Narasi disediakan oleh suara AI (ElevenLabs). Bagian tanpa cuplikan aktual adalah gerakan prinsip yang menggambarkan tindakan yang dijelaskan dalam dokumentasi resmi, dan kartu 'Cobalah menggunakannya seperti ini' adalah contoh penggunaan dalam lingkup fungsional yang diuraikan dalam dokumentasi. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [it] I 5+1 migliori progetti open source che ogni sviluppatore dovrebbe vedere questa settimana · GitH... Ho compilato la classifica settimanale dei 5 elementi più popolari su GitHub (dal 21 al 28 ottobre), includendo anche un elemento non classificato e suggerimenti su come utilizzarli al meglio, sia per i principianti che per gli sviluppatori. Le posizioni dalla 6 alla 10 sono elencate di seguito, mentre le descrizioni complete di tutti e 13 gli elementi sono disponibili nella versione estesa. La classifica segue l'ordine esatto della pagina GitHub Trending (settimanale) (dati raccolti il ​​28/10/2026 alle 06:00 KST). Le valutazioni a stelle si basano sui valori al momento della raccolta. ▶ Dettagli su tutti e 13 gli elementi (versione estesa): youtu.be 1° posto: cloudflare/security-audit-skill — Una skill (MIT) che consente agli agenti di programmazione di condurre audit di sicurezza in 6 fasi. Codice invariato dal 14/09; Le stelle sono aumentate da circa 20.000 il 23/09 a circa 50.000 il 28/10. ⚠ La verifica dell'esecuzione richiede una sandbox che blocca le reti esterne; il codice sorgente viene inviato all'azienda modello che la utilizza. 2° posto: anthropics/knowledge-work-plugins — Plugin specifici per il lavoro di Anthropic (per Cowork, disponibili anche su Claude Code). sales 2.1.0: Fonte e link originale per ogni frase. 3° posto: AgriciDaniel/claude-seo — Plugin di controllo SEO per Claude Code. v2.5.0: Verifica dell'esposizione CVE del server (legge solo le risposte), Python 3.11~3.14 (3.15 non ancora supportato). Alcune estensioni sono servizi esterni a pagamento. 4° posto: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principi) creato da un altro sviluppatore basandosi sugli scritti di Karpathy. Nessun file di licenza. 5° posto: TauricResearch/TradingAgents — Framework di ricerca multi-agente per il trading (non fornisce consulenza sugli investimenti). v0.7.1: Corretto un bug per cui le informazioni venivano lette anche dopo date con zeri → Eseguire nuovamente i vecchi backtest ▶ Dal 6° al 10° posto 6° posto: Graphify-Labs/graphify — Codice e documentazione sotto forma di grafo della conoscenza (il codice è locale, non ci sono modelli). ⚠ Il nome su PyPI è graphifyy 7° posto: ruvnet/ruflo — Strumento multi-agente basato sul Codice di Claude e sul Codex. ⚠ Richiede la versione 3.63.3 o superiore (in precedenza, i worker AI potevano essere abilitati tramite le impostazioni del repository clonato) 8° posto anomalyco/opencode — Agente di codifica open source non legato a un'azienda modello. v2.2.0. ⚠ Se hai salvato la chiave esterna nella Console, il fornitore deve eliminarla o riemetterla. 9° posto stablyai/orca — Un'app desktop che esegue più agenti di codifica simultaneamente tramite worktree, 100.000 stelle il 21/10 10° posto android/skills — Skill ufficiali per agenti del team Google Android, da 30 a 36 ▶ Bonus (Fuori classifica) akitaonrails/ai-memory (11° posto settimanale) — Un server di memoria a lungo termine condiviso da più agenti di codifica. 3.2.0: Ricerca di parti coreane senza spazi, notifiche di sessione disconnessa. Salvataggio della cronologia dei prompt e degli strumenti sul server ▶ Link cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narrazione è fornita da una voce AI (ElevenLabs). Le parti senza filmati reali mostrano i movimenti principali che illustrano le azioni descritte nella documentazione ufficiale, mentre le schede "Prova a usarlo in questo modo" sono esempi di utilizzo nell'ambito funzionale delineato nella documentazione. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [pt-BR] Top 5 +1 projetos open source que todo desenvolvedor deve ver esta semana · GitHub Trending (4ª s... Compilei o Top 5 dos itens mais populares do GitHub desta semana (21 a 28 de outubro), juntamente com um item não classificado, incluindo dicas sobre como melhor utilizá-los, tanto para iniciantes quanto para desenvolvedores. Os itens classificados de 6 a 10 estão listados abaixo, e as descrições completas de todos os 13 itens podem ser encontradas no formato longo. A classificação segue a ordem exata da página de itens populares (semanais) do GitHub (coletada em 28/10/2026 às 06:00 KST). As classificações por estrelas são baseadas nos valores no momento da coleta. ▶ Detalhes sobre todos os 13 itens (Formulário longo): youtu.be 1º lugar: cloudflare/security-audit-skill — Uma skill (MIT) que permite que agentes de codificação realizem auditorias de segurança em 6 etapas. Código inalterado desde 14/09; O número de estrelas aumentou de aproximadamente 20.000 em 23/09 para aproximadamente 50.000 em 28/10. ⚠ A verificação de execução requer um ambiente isolado (sandbox) que bloqueie redes externas; o código-fonte é enviado para a empresa modelo que o utiliza. 2º Lugar: anthropics/knowledge-work-plugins — Plugins antrópicos específicos para o trabalho (para Cowork, também disponíveis no Claude Code). sales 2.1.0: Fonte e link original para cada frase. 3º Lugar: AgriciDaniel/claude-seo — Plugin de verificação de SEO para Claude Code. v2.5.0: Verificação de exposição a CVEs no servidor (somente leitura das respostas), Python 3.11~3.14 (3.15 ainda não suportado). Algumas extensões são serviços externos pagos. 4º Lugar: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 princípios) criado por outro desenvolvedor com base nos escritos de Karpathy. Sem arquivo de licença. 5º Lugar: TauricResearch/TradingAgents — Framework de pesquisa de negociação multiagente (não é recomendação de investimento). v0.7.1: Corrigido um bug que fazia com que as divulgações fossem lidas após datas com zeros → Reexecutados os backtests antigos ▶ 6º ao 10º Lugar 6º Lugar: Graphify-Labs/graphify — Código e documentação como um grafo de conhecimento (código local, sem modelos). ⚠ Nome no PyPI: graphifyy 7º Lugar: ruvnet/ruflo — Ferramenta multiagente construída sobre Claude Code e Codex. ⚠ Requer a versão 3.63.3 ou superior (anteriormente, os workers de IA podiam ser habilitados por meio das configurações do repositório clonado) 8º Lugar: anomalyco/opencode — Agente de codificação de código aberto não vinculado a uma empresa modelo. v2.2.0. ⚠ Se você salvou a chave estrangeira no Console, o provedor deve descartá-la ou reemitir. 9º stablyai/orca — Um aplicativo para desktop que executa vários agentes de codificação simultaneamente, da worktree, 100 mil estrelas em 21/10. 10º android/skills — Skills oficiais de agentes da equipe do Google Android, 30 → 36. ▶ Bônus (Fora do Ranking) akitaonrails/ai-memory (11º da semana) — Um servidor de memória de longo prazo compartilhado por vários agentes de codificação. 3.2.0: Busca por partes em coreano sem espaços, notificações de sessão desconectada. Salvar histórico de prompts e ferramentas no servidor ▶ Links cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ A narração é feita por uma voz de IA (ElevenLabs). As partes sem imagens são animações que ilustram as ações descritas na documentação oficial, e os cards "Experimente usar assim" são exemplos de uso dentro do escopo funcional descrito na documentação. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack

이번 주 개발자라면 봐야 할 오픈소스 Top 10 +@ 총정리 · GitHub 트렌딩 (10월 4주) 이번 주(10/21~10/28) GitHub 트렌딩 주간 순위 Top 10과 순위 밖 3개(이번 주 페이지에 올라온 13개 전부)를 정리했어요. 레포마다 무엇을 하는지, 최근 바뀐 것, 초보자·개발자별로 어떻게 써 보면 좋은지까지 담았어요. 순위는 GitHub Trending(weekly) 페이지 순서 그대로예요(2026-10-28 06:00 KST 수집). 별 수는 수집 시점 값이에요. ▶ 챕터 0:00 오프닝 0:21 10위 android/skills 0:59 9위 Orca 1:36 8위 OpenCode 2:18 7위 ruflo 3:00 6위 graphify 3:38 5위 TradingAgents 4:24 4위 andrej-karpathy-skills 5:01 3위 claude-seo 5:43 2위 knowledge-work-plugins 6:25 번외 ① ai-memory 7:03 번외 ② T3 Code 7:33 번외 ③ FastAPI 8:11 1위 security-audit-skill 9:16 이번 주 확인할 것 ▶ Top 10 10위 android/skills — Google Android 팀 공식 에이전트 스킬. 일주일 새 30개에서 36개(v1.0.16~1.0.18): ANR 점검·수정, 온디바이스 LLM(LiteRT-LM), ML Kit 에이전트 API, Compose Ink, Glance 위젯 메모리 등 9위 stablyai/orca — 여러 코딩 에이전트를 git worktree별로 동시에 돌리는 데스크톱 앱(내 구독 사용). 9/30 이후 채팅 화면(실험)·권한 모드(Manual/Auto/Yolo)·앱 안 브라우저 클릭 표시·휴대폰 앱 개선, 10/21 별 10만. v1.4.221(10/5) Copilot 설정 파일 권한 보안 수정 8위 anomalyco/opencode — 모델 회사에 묶이지 않는 오픈소스 코딩 에이전트(75+ 공급자·로컬 모델). v2.2.0: 데스크톱 앱 개편, 데스크톱 플러그인(실험). ⚠ Console 보안 사고(10/17): Console에 외부 키를 저장했다면 공급자 쪽에서 폐기·재발급(OpenCode 발표 기준 Console 사용자의 1.4%) 7위 ruvnet/ruflo — Claude Code·Codex 위 다중 에이전트 도구(옛 claude-flow). 이번 주 릴리스 28번. ⚠ 3.63.3(10/26) 이전에는 클론한 저장소의 설정 파일로 AI 워커가 켜질 수 있었음 → 3.63.3 이상으로. Anthropic Managed Agents 도구 4개(직접 켜야 함, 예산 상한 필수, 실서비스 미검증) 6위 Graphify-Labs/graphify — /graphify로 코드·문서·SQL·PDF를 지식 그래프로. 코드는 로컬 tree-sitter 분석(모델 없음), 문서·이미지는 쓰는 모델 사용. 0.9.97: 그래프 설명 속 비밀값 가림 강화. ⚠ PyPI 이름은 graphifyy(y 두 개) 5위 TauricResearch/TradingAgents — 증권사 조직을 본뜬 멀티 에이전트 매매 연구 프레임워크(투자 조언 아님). 9/16 이후 백테스트·분석가 병렬·한국 등 벤치마크·역할별 모델 공급자·ChatGPT 구독 실행. v0.7.1: 0 없는 날짜가 이후 공시를 읽던 버그 수정 → 예전 백테스트는 다시 돌려 보기 4위 multica-ai/andrej-karpathy-skills — Karpathy의 글을 바탕으로 다른 개발자가 만든 CLAUDE.md 하나(원칙 4개). 마지막 커밋 4/20, LICENSE 파일 없음 3위 AgriciDaniel/claude-seo — Claude Code용 SEO 점검 플러그인(스킬 26·서브에이전트 19). v2.5.0: 자체 보안 감사 반영, 서버 CVE 노출 점검(응답만 읽음), Python 3.11 이상. 일부 확장은 유료 외부 서비스 2위 anthropics/knowledge-work-plugins — Anthropic 직무별 플러그인(Cowork용, Claude Code에서도). 저장소 안 22개·목록 123개. sales 2.1.0: 문장마다 출처와 원본 링크 ▶ 번외(순위 밖, 이번 주 페이지 11~13위) akitaonrails/ai-memory — 여러 코딩 에이전트가 같이 쓰는 장기 기억 서버. 3.2.0: 띄어쓰기 없는 한글 부분 검색, 끊긴 세션 알림. 프롬프트·도구 기록을 서버에 저장 pingdotgg/t3code — 내 PC의 Codex·Claude Code·Cursor·OpenCode 등을 웹·휴대폰에서 조작(내 구독 사용). 이번 주 v0.1.1~0.1.3, 한 번만 써 보려면 npx t3@latest fastapi/fastapi — ⚠ 0.160.0: 파라미터 모델 extra 설정이 같은 위치 다른 필드에도 적용 / 0.162.0: 폼에서 빠진 bool을 False로 치지 않음 → 체크박스는 기본값 False 선언 후 테스트 ▶ 1위 cloudflare/security-audit-skill — 코딩 에이전트에게 6단계 보안 감사를 시키는 스킬(MIT). 블로그의 후보 20,799 → 조치 7,245는 Cloudflare 사내 시스템 수치(이 스킬 성능 아님). 코드는 9/14 이후 변경 없음, 별은 9/23 약 2만 → 10/28 약 5만, 10/21~24 일간 트렌딩 전체 1위. ⚠ 실행 검증엔 외부 네트워크를 막은 샌드박스 필요, 한 번 실행은 여러 번 돌린 결과의 절반쯤(README), 소스는 쓰는 모델 회사로 전송 ▶ 끄는 방법·설치 - Android CLI 사용 통계: 명령마다 --no-metrics - Orca 익명 사용 데이터: onorca.dev - TradingAgents 업데이트 확인: TRADINGAGENTS_NO_UPDATE_CHECK=1 - graphify: uv tool install graphifyy → graphify install - security-audit-skill: npx skills add cloudflare/security-audit-skill --skill security-audit ▶ 관련 영상 - 지난주 트렌딩 롱폼(10월 3주): youtu.be - OpenCode 무료 모델 직접 실측: youtu.be ▶ 링크 cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents Graphify-Labs/graphify ruvnet/ruflo anomalyco/opencode stablyai/orca android/skills akitaonrails/ai-memory pingdotgg/t3code fastapi/fastapi ※ 내레이션은 AI 보이스(ElevenLabs)예요. 실화면이 없는 부분은 공식 문서에 적힌 동작을 그린 원리 모션이고, '이렇게 써 보세요' 카드는 문서에 있는 기능 범위의 활용 예예요. #GitHub트렌딩 #오픈소스 #개발자 #코딩에이전트 #SoloStack [en] This Week's Must-See Open Source: GitHub Trending Top 10 + Extras (Oct, Week 4) A full walkthrough of this week's GitHub Trending (weekly) Top 10 in page order (collected 2026-10-28 06:00 KST), plus the three repos ranked 11-13 — what each does, what changed recently, and how beginners and working developers can put it to use. 10 android/skills · 9 Orca · 8 OpenCode · 7 ruflo · 6 graphify · 5 TradingAgents · 4 andrej-karpathy-skills · 3 claude-seo · 2 knowledge-work-plugins · 1 Cloudflare security-audit-skill Extras: ai-memory, T3 Code, FastAPI (0.160/0.162 change parameter-model extras and omitted bool form fields). Check this week: upgrade ruflo to 3.63.3+, revoke and reissue provider keys stored in OpenCode Console, test FastAPI form handling before upgrading. Narration is an AI voice (ElevenLabs).

[개발자들의 PICK] 이번 주 깃허브 트렌딩 결산, 인기 오픈소스 TOP 10 이번 주 깃허브에서 가장 많은 별을 받은 오픈 소스 TOP 10을 10위부터 소개합니다. 소개한 저장소 링크는 고정 댓글에 있습니다. 00:00 이번 주 TOP 10 00:05 10위 안드로이드 스킬 00:51 9위 오르카 01:38 8위 오픈코드 02:15 7위 루플로 02:47 6위 그래피파이 03:32 5위 트레이딩 에이전츠 04:17 4위 카파시 스킬 04:56 3위 클로드 SEO 05:35 2위 지식 노동 플러그인 06:14 1위 클라우드플레어 시큐리티 오딧 스킬 06:55 마무리 📌 이번 주! 깃허브 트렌딩 TOP 10 저장소 10위 안드로이드 스킬 — android/skills 9위 오르카 — stablyai/orca 8위 오픈코드 — anomalyco/opencode 7위 루플로 — ruvnet/ruflo 6위 그래피파이 — Graphify-Labs/graphify 5위 트레이딩 에이전츠 — TauricResearch/TradingAgents 4위 카파시 스킬 — multica-ai/andrej-karpathy-skills 3위 클로드 SEO — AgriciDaniel/claude-seo 2위 지식 노동 플러그인 — anthropics/knowledge-work-plugins 1위 클라우드플레어 시큐리티 오딧 스킬 — cloudflare/security-audit-skill [en-US] [Developer's PICK] This Week's GitHub Trending Roundup, Top 10 Popular Open Source Projects Here are the Top 10 Open Sources with the Most Stars on GitHub this week, starting from 10th place. Links to the featured repositories are in the pinned comment. 00:00 This Week's Top 10 00:05 10th Android Skills 00:51 9th Orca 01:38 8th OpenCode 02:15 7th Luplo 02:47 6th Graphify 03:32 5th Trading Agents 04:17 4th Kapasi Skills 04:56 3rd Claude SEO 05:35 2nd Knowledge Work Plugin 06:14 1st Cloudflare Security Audit Skills 06:55 Wrap-up 📌 This Week! GitHub Trending Top 10 Repositories 10th Android Skills — android/skills 9th Orca — stablyai/orca 8th Open Code — anomalyco/opencode 7th Ruflo — ruvnet/ruflo 6th Graphify — Graphify-Labs/graphify 5th Trading Agents — TauricResearch/TradingAgents 4th Karpathy Skills — multica-ai/andrej-karpathy-skills 3rd Claude SEO — AgriciDaniel/claude-seo 2nd Knowledge Work Plugins — anthropics/knowledge-work-plugins 1st Cloudflare Security Audit Skills — cloudflare/security-audit-skill [id] [Pilihan Pengembang] Rekap Trending GitHub Minggu Ini, 10 Open Source Terpopuler Berikut adalah 10 Open Source Teratas dengan Bintang Terbanyak di GitHub minggu ini, dimulai dari peringkat ke-10. Tautan ke repositori unggulan ada di komentar yang disematkan. 00:00 10 Teratas Minggu Ini 00:05 Peringkat ke-10 Android Skills 00:51 Peringkat ke-9 Orca 01:38 Peringkat ke-8 OpenCode 02:15 Peringkat ke-7 Luplo 02:47 Peringkat ke-6 Graphify 03:32 Peringkat ke-5 Trading Agents 04:17 Peringkat ke-4 Kapasi Skills 04:56 Peringkat ke-3 Claude SEO 05:35 Peringkat ke-2 Knowledge Work Plugin 06:14 Peringkat ke-1 Cloudflare Security Audit Skills 06:55 Ringkasan 📌 Minggu Ini! 10 Repositori Terpopuler di GitHub 10 Android Skills — android/skills 9 Orca — stablyai/orca 8 Open Code — anomalyco/opencode 7 Ruflo — ruvnet/ruflo 6 Graphify — Graphify-Labs/graphify 5 Trading Agents — TauricResearch/TradingAgents 4 Karpathy Skills — multica-ai/andrej-karpathy-skills 3 Claude SEO — AgriciDaniel/claude-seo 2 Knowledge Work Plugins — anthropics/knowledge-work-plugins 1 Cloudflare Security Audit Skills — cloudflare/security-audit-skill

Claude SEO's 19-Agent Architecture: How 26 Sub-Skills Coordinate Technical Audits Without a Central Orchestrator Claude SEO runs 19 specialist agents across 26 sub-skills to audit websites for technical SEO, schema markup, Core Web Vitals, E-E-A-T signals, and AI search readiness. The architecture has no central orchestrator. Instead, agents coordinate through a flat skill composition pattern where each sub-skill wraps one or more agents and exposes a uniform interface to Claude Code's skill system. This design choice surfaces interesting trade-offs. You get simpler failure modes (no single point of coordination failure), easier agent reuse (skills compose without routing logic), and clearer boundaries (each agent owns a domain). You lose explicit dependency graphs, centralized state management, and the ability to optimize cross-agent workflows. Architecture: Skills as Agent Wrappers The system defines 26 sub-skills that wrap 19 specialist agents: - Technical SEO agents: crawl analysis, robots.txt validation, sitemap parsing, canonical tag checks, redirect chains - Performance agents: Core Web Vitals measurement, Lighthouse integration, render-blocking resource detection - Schema agents: structured data validation, JSON-LD parsing, rich snippet eligibility - E-E-A-T agents: author bio extraction, citation analysis, expertise signal detection - AI search agents: GEO/AEO readiness (Google AI Overviews, ChatGPT search), llms.txt generation, Lighthouse Agentic Browsing support, WebMCP integration - Domain-specific agents: local SEO (Google Business Profile), e-commerce (product schema), international SEO (hreflang), backlink analysis Each skill exposes a standard interface: class SEOSkill: def analyze(self, url: str, context: AuditContext) -> SkillResult: """Run analysis, return structured findings.""" pass def get_dependencies(self) -> list[str]: """Declare which other skills must run first.""" pass def merge_context(self, result: SkillResult, context: AuditContext): """Write findings back to shared context.""" pass Claude Code invokes skills in dependency order. A skill that needs crawl data declares a dependency on the crawl skill. The skill system resolves the graph and runs crawl first, then passes the enriched context to dependent skills. Coordination Without a Router Traditional multi-agent systems use a central orchestrator to route tasks, manage state, and handle failures. Claude SEO skips this layer. Coordination happens through three mechanisms: 1. Dependency declarations Skills declare dependencies explicitly. The schema validation skill depends on the crawl skill (needs HTML) and the render skill (needs JavaScript-rendered DOM). The skill system builds a directed acyclic graph and executes in topological order. 2. Shared context object All skills read from and write to a single AuditContext object. The crawl skill writes page HTML, status codes, and redirect chains. The schema skill reads HTML and writes structured data findings. The E-E-A-T skill reads both and writes authority signals. This is a shared-memory model. No message passing, no event bus, no coordination primitives. Skills trust the context object to be consistent. 3. Skill result merging Each skill returns a SkillResult with findings, warnings, and recommendations. The skill system merges results into the final audit report. No skill sees another skill's output during execution (only through the shared context). State Handoff and Failure Modes The shared context model creates predictable failure modes: Missing dependencies: If skill A depends on skill B but B fails, A receives incomplete context. The skill system catches this at graph-build time (missing dependency) or runtime (null context fields). The audit continues with warnings. Context pollution: If skill A writes malformed data to context, skill B may crash or produce garbage output. No schema validation on context writes. The skill system logs exceptions but doesn't roll back context state. Concurrent access: Skills run sequentially (no parallelism). This avoids race conditions but makes audits slower. A full audit takes 10-15 minutes for a medium site. Partial failures: If the crawl skill fetches 80% of pages before hitting rate limits, dependent skills see incomplete data. No retry logic, no partial result handling. The audit completes with gaps. Skill Composition Pattern The 26 sub-skills layer on top of 19 agents through composition: | Skill | Agent(s) | Composition Type | |-------|----------|------------------| | Technical SEO | Crawl + Robots + Sitemap | Sequential pipeline | | Core Web Vitals | Lighthouse + Render | Parallel then merge | | Schema validation | JSON-LD parser + Validator | Sequential with fallback | | E-E-A-T analysis | Author + Citation + Expertise | Parallel aggregation | | AI search readiness | llms.txt + WebMCP + Agentic Browsing | Independent checks | This pattern lets you reuse agents across skills. The crawl agent appears in technical SEO, schema validation, and E-E-A-T skills. The Lighthouse agent appears in Core Web Vitals and performance skills. Composition types: - Sequential pipeline: Skill A feeds output to skill B. Example: crawl → robots.txt validation. - Parallel then merge: Skills A and B run independently, results merge. Example: Lighthouse + render-blocking detection. - Sequential with fallback: Try skill A, fall back to skill B on failure. Example: JSON-LD parser → microdata parser. - Parallel aggregation: Skills A, B, C run independently, results aggregate into a score. Example: E-E-A-T signals. - Independent checks: Skills run without dependencies, results stand alone. Example: llms.txt presence, WebMCP support. Extension System: 9 Optional Live Data Integrations The base system runs offline analysis (crawl, parse, validate). Nine optional extensions add live SEO data: - Google Search Console API (rankings, impressions, clicks) - Google Analytics API (traffic, bounce rate, conversions) - Google PageSpeed Insights API (field data, lab data) - Ahrefs API (backlinks, domain rating) - SEMrush API (keyword difficulty, SERP features) - Moz API (domain authority, spam score) - Screaming Frog integration (deep crawl data) - Lighthouse CI (historical performance) - Custom data sources (CSV, JSON, database) Extensions follow the same skill interface. They declare dependencies (e.g., Search Console skill depends on crawl skill for URL list) and write to shared context. This creates a second failure mode: API rate limits. If the Search Console extension hits quota limits mid-audit, dependent skills see partial data. No backoff, no queue, no retry. The audit completes with warnings. Report Generation: PDF and Excel via Google APIs The final audit report renders as PDF or Excel using Google Docs and Sheets APIs. The skill system: 1. Merges all skill results into a structured JSON object 2. Applies a template (Google Doc or Sheet) 3. Populates template fields via API batch updates 4. Exports to PDF or Excel 5. Returns a download link This is a synchronous blocking operation. If the Google API is slow or rate-limited, the entire audit stalls. No async rendering, no background jobs. When Flat Hierarchies Outperform Orchestrators Claude SEO's flat agent hierarchy works because: Task boundaries are clear: Each agent owns a well-defined SEO domain. No ambiguity about which agent handles schema validation vs. crawl analysis. Dependencies are static: The dependency graph doesn't change per audit. Crawl always runs before schema validation. No dynamic routing needed. Failures are local: If the backlink agent fails, the rest of the audit completes. No cascading failures (except for hard dependencies like crawl). State is append-only: Skills write findings to context but don't modify existing data. No coordination needed to prevent conflicts. Execution is sequential: No parallelism means no race conditions, no distributed state, no consensus protocols. This pattern breaks down when: - Task boundaries blur (which agent handles JavaScript-rendered schema?) - Dependencies become dynamic (run backlink analysis only if domain authority is low) - Failures cascade (crawl failure blocks 80% of skills) - State requires coordination (two skills need to update the same context field) - Execution needs parallelism (audit 1000 pages in 5 minutes) Code Example: Skill Interface Implementation from dataclasses import dataclass from typing import Optional @dataclass class AuditContext: url: str html: Optional[str] = None status_code: Optional[int] = None redirects: list[str] = None structured_data: Optional[dict] = None core_web_vitals: Optional[dict] = None class CrawlSkill: def get_dependencies(self) -> list[str]: return [] # No dependencies def analyze(self, url: str, context: AuditContext) -> dict: # Fetch page, follow redirects, extract HTML response = self._fetch_with_redirects(url) context.html = response.text context.status_code = response.status_code context.redirects = response.history return { "status": "success", "findings": { "crawlable": response.status_code == 200, "redirect_chain_length": len(response.history), "html_size_kb": len(response.text) / 1024 } } class SchemaSkill: def get_dependencies(self) -> list[str]: return ["crawl"] # Needs HTML from crawl skill def analyze(self, url: str, context: AuditContext) -> dict: if not context.html: return {"status": "error", "message": "No HTML available"} # Extract and validate JSON-LD structured_data = self._extract_jsonld(context.html) validation_errors = self._validate_schema(structured_data) context.structured_data = structured_data return { "status": "success", "findings": { "schema_types": list(structured_data.keys()), "validation_errors": validation_errors, "rich_snippet_eligible": len(validation_errors) == 0 } } Technical Verdict Use Claude SEO's flat agent pattern when: - You have 10-30 specialist agents with clear domain boundaries - Dependencies form a static DAG (no dynamic routing) - Failures can be isolated (no cascading dependencies) - Sequential execution is acceptable (10-15 minute audits) - You want simple debugging (no orchestrator state to inspect) Avoid this pattern when: - Task boundaries are ambiguous (agents compete for the same work) - Dependencies change per request (dynamic workflow routing) - You need parallelism (audit 1000 pages in under 5 minutes) - Failures must trigger compensating actions (rollback, retry, circuit breaker) - You need observability into coordination logic (trace which agent made which decision) The flat hierarchy trades orchestrator complexity for simpler agent interfaces and clearer failure modes. It works when your domain naturally decomposes into independent specialists. It breaks when coordination becomes the hard problem. Source Links - Claude SEO GitHub Repository - Claude Code Plugin Documentation

🚀 Trending on GitHub claude-seo by AgriciDaniel ⭐ 22,685 (2,546 this week) 🧠 A free, open-source SEO skill for Claude Code: 26 sub-skills that audit your site for Google rankings and for AI search citations from ChatGPT, Perplexity and Gemini too. AgriciDaniel/claude-seo #GenAI #OpenSource #AI

AgriciDaniel/claude-seo: Universal SEO skill for Claude Code. 26 sub-skills + 19 sub-agents covering technical SEO, E-E-A-T, schema, GEO/AEO, agent readiness (Lighthouse Agentic Browsing, WebMCP, llms.txt), backlinks, local SEO, e-commerce, international SEO, Google APIs, and PDF/Excel reporting....