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Open-Source AI SuperAgent: Deer Flow by ByteDance
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
AI Tool: Zhangxuefeng.skill for Cognitive Planning
张雪峰.skill — 张雪峰的认知操作系统。高考志愿/考研/职业规划的实战思维框架。由女娲.skill生成。
NVIDIA SkillSpector: AI Security Scanner for Vulnerabilities
Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks.
PM Skills Marketplace: 100+ Agentic Tools for Project Management
PM Skills Marketplace: 100+ agentic skills, commands, and plugins — from discovery to strategy, execution, launch, and growth.
AI-Powered Job Search with Santifer Career Ops
AI-powered job search system built on Claude Code. 14 skill modes, Go dashboard, PDF generation, batch processing.
Last30Days Skill: AI Research Across Top Platforms
AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
AI Tool Ports PostgreSQL Extensions to MySQL
AI Driven Porting of PostgreSQL Extensions to MySQL: A Seamless Transition In the realm of database management, transitioning from one system to another can be …
Harness AI: Design Domain-Specific Agent Teams Efficiently
A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
Claude Code: 171 Structured Reasoning Skills for AI
Unlocking AI Potential with Claude's Structured Reasoning Skills Artificial Intelligence has revolutionized various industries, paving the way for innovative so…
AI Tool for GitHub: Craig McCaskill's Latest Innovation
AI Tool for GitHub: Craig McCaskill's Latest Innovation Craig McCaskill has introduced a groundbreaking AI driven tool designed to enhance productivity and effi…
AI Tool: Taste-Skill Enhances AI Content Quality
Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
AI Agents Enhanced with 754 Cybersecurity Skills
754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
Harness Performance Optimization with affaan-m/ECC for AI Agents
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Claude Skill for Spec-Driven Development (SDD) Released
Claude Skill for Spec Driven Development (SDD) Released The announcement of the Claude Skill for Spec Driven Development (SDD) marks a significant advancement i…
Multica AI: Open-Source Managed Agents Platform for Teams
The open-source managed agents platform. Turn coding agents into real teammates — assign tasks, track progress, compound skills.
Python API for Google NotebookLM: Full Programmatic Access
Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
AI Coding Agents Enhance .NET and C# Skills on GitHub
Repository for skills to assist AI coding agents with .NET and C#
Sharpskill.dev: Revolutionizing AI Tools on Hacker News
Sharpskill.dev: Transforming AI Tools on Hacker News Sharpskill.dev has emerged as a groundbreaking platform in the AI landscape, especially prominent on Hacker…
SharpSkill: LeetCode Alternative with Real Interview Outcomes
SharpSkill: Enhance Interview Preparedness with Realistic Outcomes Discover SharpSkill: A Modern Approach to Job Interview Readiness In the digital age, job see…
Improve Claude Code with Andrej Karpathy's Insights
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
AI Skills Arms Race: The Future of Automotive with TechCrunch Mobility
AI Skills Arms Race: The Future of Automotive The automotive industry is on the cusp of a transformative shift, driven by the integration of artificial intellig…
AI Coding Agents: Secure Skill Registry for Extending AI Tools
The secure, validated skill registry for professional AI coding agents. Extend Antigravity, Claude Code, Cursor, Copilot and more with absolute confidence.
Sx: Open-Source AI Skills and Commands Manager
Discover Sx: The Ultimate Open Source AI Skills and Commands Manager Introduction In the dynamic landscape of artificial intelligence, managing skills and comma…
Qiaomu: AI Tool Converts Content for NotebookLM
Claude Skill: Multi-source content processor for NotebookLM. Supports WeChat articles, web pages, YouTube, PDF, Markdown, search queries → Podcast/PPT/MindMap/Quiz etc.
Agent Skills: Public Repository for AI Tools on GitHub
Public repository for Agent Skills
AI Tool: K-Dense-AI Scientific Agent Skills for Research & Finance
A set of ready to use Agent Skills for research, science, engineering, analysis, finance and writing.
Agent Skills Evaluation Tool: Improve AI Outputs
Enhance AI Performance with Agent Skills Evaluation Tool In the rapidly evolving landscape of artificial intelligence (AI), ensuring the accuracy and effectiven…
Top Production-Grade AI Coding Skills for Engineers
Production-grade engineering skills for AI coding agents.
AI Tool Skills.sh: Revolutionizing Skill Development
AI Tool Skills.sh: Transforming Skill Development AI tool Skills.sh is an innovative platform designed to revolutionize the way individuals acquire and improve …
Join AI Saturdays: Learn Prompt Engineering for Free
Hey hey Running a small virtual group called AI Saturdays where we pick one practical AI skill per week and actually learn it together. This week: Prompt Engineering. Free, casual, no experience needed. [RSVP Link](https://www.meetup.com/chillnskill/events/314498981)
Claude Agent SDK: Web Browsing Tool for AI
Claude Agent SDK with a web browsing tool
WorkProof: JSON Schema for Skill Evidence Graphs
WorkProof: Harnessing JSON Schema for Skill Evidence Graphs WorkProof leverages JSON Schema to structure and validate skill evidence graphs, offering a robust f…
AI Skill Files: Warm Starts for Claude and Gemini Sessions
One thing that frustrates me about most AI workflows is the cold start problem. Every new session you re-explain your business, your voice, your clients. I started solving this with skill files. A skill file is a markdown document you upload to a Claude Project or paste into a Gemini Gem. It holds your context permanently so you never re-explain anything. The three I use most: brand-voice.md: defines tone, writing rules, and platform-specific formatting client-router.md: when you say a client name, Claude loads their full project context automatically seo-aeo-audit-checklist.md: structured audit that scores any website out of 100 across 7 sections including AI search visibility Anyone else using a similar system? Curious what context you keep persistent across sessions.
MaxHermes by Minimax: AI Agent for Skill Building
AI agent that builds skills from every task you give it
AI Tool: mattpocock/skills for Real Engineers
Skills for Real Engineers. Straight from my .claude directory.
Top Codex Skills for Automating Workflows
A curated list of practical Codex skills for automating workflows across the Codex CLI and API.
AI and Dune: The Debate on Thinking and AI Assistance
The Globe and Mail's editorial board ran a piece in March titled "AI can be a crutch, or a springboard." To illustrate the crutch half, they offered this: someone asked AI to explain a passage from Dune that warns against delegating thinking to machines. Instead of reading the book. That anecdote is doing more work than the studies the editorial cites. But the studies are real. Researchers at MIT published a paper in June 2025 titled "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (Kosmyna et al., arXiv 2506.08872). The study tracked brain activity across three groups: people writing with ChatGPT, people using search engines, and people working unaided. The LLM group showed the weakest neural connectivity. Over four months, "LLM users consistently underperformed at neural, linguistic, and behavioral levels." The most striking finding: LLM users struggled to accurately quote their own work. They couldn't recall what they had just written. The Globe cites this and similar research to make a point about dependency. The implicit argument: hand enough of your thinking to a machine and you stop doing it yourself. That finding is probably accurate for the way most people use these tools. The question is whether that's the only way they can be used. The Globe's own title contains the counter-argument. Crutch or springboard. They wrote both words. They just didn't develop the second one. Ethan Mollick, a professor at Wharton who has been writing about AI use since the tools became widely available, argued in 2023 that the real challenge AI poses to education isn't that students will stop thinking, it's that the old structures assumed thinking was hard enough to enforce. ("The Homework Apocalypse," [oneusefulthing.org](http://oneusefulthing.org), July 2023.) When AI can do the surface-level cognitive work, the only tasks left worth assigning are the ones that require actual judgment. The tool, in that framing, doesn't reduce the demand for thinking. It raises the floor under it. Nate B. Jones, who writes and consults on what it actually takes to work well with AI, has made a sharper version of this argument. His position: using AI effectively requires more cognitive skill, not less. Specifically, it requires the ability to translate ambiguous intent into a precise, edge-case-aware specification that an AI can execute correctly. It requires detecting errors in output that is fluent and confident-sounding but wrong. It requires recognizing when an AI has drifted from your intent, or is confirming a premise it should be challenging. These are not passive skills. They are harder versions of the same thinking the MIT study found LLM users weren't doing. The difference between the group that lost neural connectivity and the group that doesn't isn't the tool. It's what they decided to do with it. Here's my own evidence. In the past year I built a working web application. Python backend. JavaScript frontend. Deployed on two hosting platforms. Payment processing. User authentication. A full data model. I do not know how to code. Every product decision was mine. Every architectural call. Every tradeoff judgment. I defined what the system needed to do, why, and what done looked like. I reviewed every significant change before it was accepted. When something broke, I identified where the breakdown was and directed the fix. The implementation was handled by AI. The thinking was mine. This mode (call it AI-directed building) is the opposite of the Dune reader. The quality of what gets produced is entirely a function of how clearly you can think, how precisely you can specify, and how critically you can evaluate what comes back. There is no shortcut in that. A vague brief to an AI doesn't produce a confused output. It produces a confident, fluent, wrong one. The discipline that prevents that is yours to supply. Non-coders building functional software with AI is common enough now that it isn't a story. What's less visible is the specificity of judgment underneath the ones that actually work. The practices that force more thinking rather than less are not complicated, but they require a decision to use the tool differently. When I've formed a position on something, I give the AI full context and ask it to make the strongest possible case against me. Ask for the hardest opposing argument it can construct. Then I read it. Sometimes it changes nothing. Sometimes it surfaces something I had dismissed without fully examining. The AI doesn't form my view. It stress-tests one I've already formed. When I'm uncertain between options, I don't ask which is better. I ask: here are two approaches, here is my constraint, now what does each cost me, and what does each require me to give up? I make the call. The AI laid out the shape of the decision. The judgment was mine. The uncomfortable part of thinking is still yours in this mode. The tool makes the work more rigorous, not easier. The MIT researchers and the Globe editorial are almost certainly right about the majority of current use. Passive use produces passive outcomes. That's not a controversial claim. The crutch half and the springboard half use the same interface. The difference is whether the person in front of it decided to think. What are you doing with it that forces more thinking rather than less? Are you using it to skip a step, or to take a harder one? Genuinely asking.
Why People Turn to AI for Art: A Deeper Look
Why do people use AI for art? Before anything, this isn’t about debating whether AI art is “real” art. I’ve already shared my personal take on my last post. This is about something simpler and, I think, more human: why people are drawn to it in the first place. I’ll be honest. I used to mock people who used AI for art. I saw it as a shortcut, a lack of effort, even a lack of creativity. It felt easy to dismiss. But as someone who creates in a different medium, writing novels, I started wondering about the motivation behind it. Not the output, but the “why.” After spending time digging into discussions, patterns, and people’s own explanations, I started noticing something deeper. For many, it ties back to how they grew up. A lot of people didn’t have the freedom to explore creativity as kids. Academic pressure, strict expectations, or environments where only “practical” success mattered often pushed curiosity and artistic exploration aside. For some, even trying to pursue something creative was discouraged or punished. That kind of upbringing doesn’t just disappear. It follows people into adulthood. You end up with individuals who feel disconnected from creativity, not because they lack imagination, but because they were never given space to develop it. Trying to learn a creative skill later in life can feel risky, even uncomfortable, especially when it’s tied to the idea that it might not lead to financial stability. Then something like AI tools shows up. Suddenly, there’s a way to express ideas visually without years of training, without the fear of “wasting time,” and without revisiting that pressure. For some, it’s the first time they can take something from their imagination and actually see it exist. That experience can feel new, almost like rediscovering something they never got to have. So when you see a flood of AI-generated art online, it’s not just about technology. For many people, it’s about access. It’s about finally having a low barrier to expressing something internal. That doesn’t mean everyone using AI has the same background or reasons. But reducing it to “laziness” or “lack of creativity” misses a much bigger picture. In some cases, making fun of people for using these tools ends up hitting something more personal than we realize. Curious to hear what others think. What do you see as the main reasons people turn to AI for art?
AI Skills Directory: mattpocock/skills on GitHub
My personal directory of skills, straight from my .claude directory.
Top Codex Skills for Automating Workflows with CLI and API
A curated list of practical Codex skills for automating workflows across the Codex CLI and API.