This is a Claude Code Skill, a refined security knowledge base based on 88,636 real vulnerability cases collected by WooYun from 2010 to 2016. After installing this Skill, Claude can think about vulnerability issues like a senior security expert. #AiSecurity https://github.com/tanweai/wooyun-legacy

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This is a Claude Code Skill, a refined security knowledge base based on 88,636 real vulnerability cases collected by WooYun from 2010 to 2016. After installing this Skill, Claude can think about vulnerability issues like a senior security expert. #AiSecurity github.com/tanweai/wooyun-legacy

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After a couple hours of work, I finally finished developing my first ever skill. :D Claude’s frontend skill tells the AI to "pick an extreme aesthetic" and "be creative." The problem tho is LLMs are just based on probability. Without strict rules, they statistically default to the most likely patterns, that's where AI slop comes from. To get clean and production-grade UI, you need to override these biases with some engineering constraints. I open-sourced Taste-Skill to fix this. :) Check it out! https://github.com/Leonxlnx/taste-skill (still early lots of improvements are on the way)@LexnLin@coreyhainesco@affaan@bnj@bbssppllvvI've tried all ( 74 😵‍💫 ) AI Coding Agents & IDEs [Rork, CodeRabbit, Anima, Zed, Factory, Cursor, Windsurf, Copilot, Lovable, Bolt, v0, Replit, MarsX, Canva, Devin, Github Spark, Vercel, Lindy, Warp, Figma, Cline, Vibe Coder & more] The most complete list ever made (with demos & notes):@johnrushx@xmayeth+1@zkgoudanLow-key websites I quietly rely on 1) http://roadmap.sh Gives you a brutally clear learning path for roles like frontend, backend, DevOps, etc No fluff, just “learn this → then this → then this”. 2) http://playcode.io An online playground to quickly test HTML, CSS, JS without setting up anything locally Perfect for quick experiments and debugging ideas 3) http://usehooks.com A collection of reusable React hooks with real use cases Saves time and helps you avoid rewriting the same logic again and again 4) http://devhints.io Concise cheat sheets for languages, frameworks, and tools. Ideal when you forget syntax and don’t want to read a 20-minute blog 5) http://jsoncrack.com Turns messy JSON into a clean visual tree Makes understanding large APIs and configs way easier than staring at raw text 6) http://realtimecolors.com Lets you generate and preview color palettes instantly Useful when you want decent UI colors without guessing or copying blindly 7) http://regex101.com Build, test, and debug regex step by step with explanations Honestly, the fastest way to stop hating regex 8) http://bundlephobia.com Shows how big an npm package really is before you install it Helps you avoid bloating your app with “tiny” libraries 9) http://caniuse.com Tells you which CSS/JS features actually work across browsers Essential before using shiny new features in production 10) http://toolbox.googleapps.com Google’s own diagnostics tools for DNS, email, headers, and network issues Surprisingly useful for debugging real-world problems 👉 Which one of these do you already use and which one did you not know existed?@shekhu04
@emilkowalskiA single place to browse, compare, and install Clawdbot skills by intent. Built for fast discovery and practical use. GitHub Repository: https://github.com/VoltAgent/awesome-clawdbot-skills@GithubProjects@Fried_rice@argofowl2@coderabbitai@everythe fact that http://pi.dev agent is so good, with virtually no sophisticated harness whatsoever, is a testament to the fact token vendor (codex/claude) agents are overrated. highly. today's moat of codex/claude tools is GIVING TOKENS FOR FREE LEFT AND RIGHT to make you dependant@krzyzanowskim@realmcore_@bertwitt12+13I took the @karpathy autoresearch loop and pointed it at markets. 25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function. Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived. The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top. The agents even figured out their own portfolio manager was the weakest link before we did! Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%. The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital. https://github.com/chrisworsey55/atlas-gic Part hedge fund, part research experiment :)@Chris_Worsey