> ## Content Index > Fetch the complete content index at: https://theseguysknow.io/llms.txt > Use this file to discover other available public pages before exploring further. # 5 AI GitHub Repos Worth Using in 2026 - URL: https://theseguysknow.io/best-ai-github-repos-2026/ - Published: 2026-08-06T10:03:30.000Z - Updated: 2026-08-06T10:03:30.000Z - Description: Your feed is full of Claude skills and AI workflows. These are five GitHub repos I think are worth opening, what you can do with them, and what users say after the Reel ends. - Author: Mike Hazard - Tags: AI & Tech, AI Tools & Models If your feed has anything to do with AI, you have probably seen the Reels by now. Ten Claude skills you need. Twenty GitHub repos that will change your life. One hundred and forty-seven agents apparently required to answer an email. Some of it is useful. A lot of it is the same coding-agent setup repackaged with a different thumbnail. So I wanted a more useful list: five big GitHub repos doing five different jobs. One for coding, one for automation, one for running AI locally, one for giving agents useful web data, and one for serious image and video workflows. These are not the five "most used" AI repos on Earth because nobody can prove that. GitHub stars show interest, not real usage, and they are especially bad at comparing an older repo with something that appeared six months ago. I used the numbers as a sanity check, not as the ranking. What matters more here is that the projects are still active in 2026, people are building real things with them, and each one does a different job. This list is current as of August 6, 2026. ## Quick answer | Repo | Best for | What you get | The catch | | ------------------------------------------------------------------------ | --------------------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | [Superpowers](https://github.com/obra/superpowers?ref=theseguysknow.io) | AI coding | A ready-made planning, testing, debugging and review process for coding agents. | Very easy to start; can turn a tiny fix into a whole ceremony. | | [n8n](https://github.com/n8n-io/n8n?ref=theseguysknow.io) | Automation | A visual place to connect APIs, AI models, databases and real business workflows. | You still have to handle auth, errors, bad data and every service that randomly breaks. | | [Ollama](https://github.com/ollama/ollama?ref=theseguysknow.io) | Local AI | Download open models, run them locally and expose them through a local API with little setup. | Convenience first. llama.cpp, MLX or other runtimes can give power users more control or speed. | | [Firecrawl](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io) | Web research and data | Search, scrape, map and crawl the web into Markdown or JSON that agents can use. | Hosted usage can get expensive at scale; self-hosting gives you another stack to maintain. | | [ComfyUI](https://github.com/Comfy-Org/ComfyUI?ref=theseguysknow.io) | AI images and video | Reusable node workflows for images, video, audio and 3D, with control over individual generation steps. | The first complicated graph looks deranged, and custom-node/version problems are real. | For scale, the five repos are all heavily starred on GitHub as of August 6, 2026\. I am deliberately leaving the numbers out of the ranking table. An old repo having more stars tells us very little about what is useful in 2026. ## What users keep using them for This is where the Reddit and community threads became more useful than the star counts. People rarely talk about these tools in the same way the GitHub README does. They talk about the one part that saved them time, the thing that broke, or the reason they went back to another tool. | Repo | Community favourite | What users warn about | | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | | [Superpowers](https://github.com/obra/superpowers?ref=theseguysknow.io) | Verification and review on bigger coding jobs, especially catching work that was in the plan but never implemented. | It can overdo the process on small fixes. | | [n8n](https://github.com/n8n-io/n8n?ref=theseguysknow.io) | Building real workflows around the AI: webhooks, APIs, databases, error handling and even experimental workflows that repair failed workflows. | The AI node is often the easy part. Keeping the whole automation alive is the job. | | [Ollama](https://github.com/ollama/ollama?ref=theseguysknow.io) | Using local models through one simple API and switching models without building the serving layer yourself. | Power users can get more speed and control by going closer to llama.cpp, MLX or another runtime. | | [Firecrawl](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io) | Getting cleaner web data into agents quickly, especially when you map a site first and only crawl the URLs you need. | Hosted limits matter at scale, while self-hosting brings more infrastructure back into the picture. | | [ComfyUI](https://github.com/Comfy-Org/ComfyUI?ref=theseguysknow.io) | Taking somebody else's image or video workflow, changing it, adding nodes and turning it into your own reusable setup. | Old tutorials and missing or renamed custom nodes can waste paid GPU time before you generate anything. | I would not call any of these a universal "Reddit favourite." The communities disagree too much for that. But these are the use cases that kept coming up in the discussions linked below, and they tell you far more than a star counter does. ## 1\. Superpowers: give your coding agent some discipline [Superpowers](https://github.com/obra/superpowers?ref=theseguysknow.io) is nice for one boring reason: it is easy to use. On Claude Code it is in the official plugin marketplace, and the repo has install routes for Codex, Cursor, Gemini CLI, OpenCode and other agent setups. You do not need to invent a giant `CLAUDE.md` and spend Saturday building your own development religion. Tell the agent what you want and Superpowers gives it a process: clarify the idea, write a plan, work in an isolated branch if needed, write tests, implement, review and verify. Its current skill library also includes systematic debugging, parallel agents and two-stage subagent review. The testing skill is strict enough that its own README says code written before the failing test gets deleted. [The repo documents the full workflow here](https://github.com/obra/superpowers?ref=theseguysknow.io#whats-inside). The model itself does not become smarter. What you get is a process around it, which can stop "build me this" from turning into 19 changed files before the agent has even understood the job. It also works across more than Claude Code. The repo currently lists support for Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot CLI, OpenCode and several other agent setups. The Claude Code community is split in a useful way. One user said it ["really changed how I work with Claude"](https://www.reddit.com/r/ClaudeCode/comments/1u2tqud/are%5Fyou%5Fguys%5Fstill%5Fusing%5Fthe%5Fsuperpowers%5Fskill/?ref=theseguysknow.io). In another thread, a user liked the verification subagent because it [caught pieces that were present in the plan but missing from the implementation](https://www.reddit.com/r/ClaudeCode/comments/1r9y2ka/claude%5Fcodes%5Fsuperpowers%5Fplugin%5Factually%5Fdelivers/?ref=theseguysknow.io). Now we have a concrete reason to use it: the plan said something should be built, the implementation missed it, and the review caught it. The complaint is just as clear. Some experienced developers [remove it because it keeps doing more than they asked](https://www.reddit.com/r/ClaudeCode/comments/1upt8mr/why%5Fi%5Fremoved%5Fsuperpowers%5Ffrom%5Fclaude/?ref=theseguysknow.io), while others keep it for bigger features and use normal plan mode for small work. Fair enough. If I am changing one line, I do not need a committee meeting and a six-stage development plan. For a bigger agent job, though, the planning, testing and verification are the reason I would install it. **Repo:** [github.com/obra/superpowers](https://github.com/obra/superpowers?ref=theseguysknow.io) ## 2\. n8n: the bit that makes AI do something useful [n8n](https://github.com/n8n-io/n8n?ref=theseguysknow.io) has been around much longer than the current agent craze, but it fits 2026 almost perfectly. It is a visual workflow platform with custom code support, self-hosting and more than 1,500 integrations according to its current GitHub README. In normal words, you connect things together. A form comes in, n8n checks the data, sends part of it to an AI model, updates your CRM, creates a task, messages somebody on Slack and stores the result. Or you build a support agent that reads a database, answers a customer and hands weird cases to a person. Or a lead-research workflow. Or a daily report. Or one of those insane diagrams with 84 nodes that somebody will definitely post on LinkedIn. This is where a lot of "AI automation" becomes real work. The model is often the easy bit. The pain is triggers, webhooks, authentication, retries, databases and deciding what happens when step 17 fails at 3:14 in the morning. One [r/n8n user put it perfectly](https://www.reddit.com/r/n8n/comments/1srjxfy/is%5Fn8n%5Fthe%5Fbest%5Fplace%5Fto%5Flearn%5Fai%5Fagent/?ref=theseguysknow.io): "the AI node is 10% of the problem." The rest is knowing how to split flows, use sub-workflows and stop one error from killing the whole thing. There are real examples too. One Reddit user says they built a [WhatsApp and voice support agent](https://www.reddit.com/r/n8n/comments/1sc3i30/i%5Fbuilt%5Fa%5Fwhatsapp%5Fvoice%5Fai%5Fagent%5Fin%5Fn8n%5Fthat/?ref=theseguysknow.io) for a device-repair shop and kept it running for more than a year. A June thread on the [n8n community forum](https://community.n8n.io/t/agents-fixing-n8n-workflows/299685?ref=theseguysknow.io) goes a step further: the author built an error handler that sends failed workflows to an agent, has a second model review the proposed fix, takes a snapshot, writes the repair back and reruns the workflow. The author is still keeping it in testing, which is probably wise. It also gets to the part the Reels skip: APIs fail, credentials expire, and somebody still needs to understand the beautiful 80-node monster when it breaks. **Repo:** [github.com/n8n-io/n8n](https://github.com/n8n-io/n8n?ref=theseguysknow.io) ## 3\. Ollama: run AI models on your own machine without making it a weekend project [Ollama](https://github.com/ollama/ollama?ref=theseguysknow.io) is the runner/server layer around open models. It makes downloading, running and serving them on your own machine much less annoying. Pull a model, run it, and Ollama exposes a local REST API that other apps can call. That is the useful part. So you can have a local model behind Open WebUI, a coding tool, a private document workflow, Home Assistant or your own little app without writing the model-serving layer yourself. One [LocalLLaMA user](https://www.reddit.com/r/LocalLLaMA/comments/1icta5y/why%5Fdo%5Fpeople%5Flike%5Follama%5Fmore%5Fthan%5Flm%5Fstudio/?ref=theseguysknow.io) described the appeal nicely: the terminal interface is bare, but as an API endpoint server it makes switching between models painless. Another user in a community setup thread was running [Ollama + Open WebUI mainly for general use and private conversations](https://www.reddit.com/r/LocalLLaMA/comments/1lbd2jy/what%5Fllm%5Fis%5Feveryone%5Fusing%5Fin%5Fjune%5F2025/?ref=theseguysknow.io). Privacy and offline use are good reasons to bother. I think the more useful part is the boring local endpoint. Once that exists, the model on your machine can sit inside a bigger workflow instead of becoming another chat tab. There is a proper nerd fight around it, of course. LocalLLaMA users regularly argue for llama.cpp directly because it gives them more control. In one [Qwen coder benchmark](https://www.reddit.com/r/LocalLLaMA/comments/1q64f26/llamacpp%5Fvs%5Follama%5F70%5Fhigher%5Fcode%5Fgeneration/?ref=theseguysknow.io), the poster measured around 52 tokens/sec with llama.cpp versus 30 with Ollama on the same machine and model. That is one setup, so do not treat those numbers as universal. If squeezing every token per second out of your machine matters to you, Ollama may not be where you end up. If you enjoy tuning llama.cpp, MLX or vLLM, you may outgrow it. If you just want Qwen, Gemma, DeepSeek or another supported model running locally tonight and available to the rest of your tools, that convenience is the whole reason to use Ollama. **Repo:** [github.com/ollama/ollama](https://github.com/ollama/ollama?ref=theseguysknow.io) ## 4\. Firecrawl: give your AI cleaner web data [Firecrawl](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io) is less sexy until you have tried to make an agent read the web properly. It can search, scrape and crawl websites, then turn what it finds into cleaner Markdown or structured JSON. Its current API can also map a site's URLs before you crawl it, interact with pages, batch-scrape large URL lists and run an agent that gathers data from the web from a plain-English request. [The repo lists the current endpoints here](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io#feature-overview). That makes it useful for research agents, searchable knowledge bases, documentation, competitor research, lead enrichment or any workflow where the model needs web information in a cleaner form than a pile of HTML. Scraping gets annoying very quickly. JavaScript pages, rate limits, blocked requests, broken selectors and different page structures can turn "let the agent read this website" into half a development project. The open-source/self-hosted side is where opinions get more mixed. In a recent [Firecrawl versus Crawl4AI thread](https://www.reddit.com/r/AgentsOfAI/comments/1t3pe4e/firecrawl%5Fvs%5Fcrawl4ai%5Fi%5Ftried%5Fboth%5Fand%5Fheres%5Fwhat/?ref=theseguysknow.io), one user summed up the trade well: Crawl4AI gives you more of the system to control, while Firecrawl gets you moving faster with less infrastructure to babysit. Another useful tip from that thread was to map a site first and crawl only the URLs you need instead of burning through a full domain for no reason. That map-first tip is the sort of thing I wanted from the user discussions. Firecrawl makes sense when you want web search, page extraction, URL mapping and agent-friendly output without building the scraping layer yourself. If you need a heavily customized local stack or you are crawling at huge scale, compare Crawl4AI, Scrapling and the rest before committing. The hosted service has usage limits, while serious self-hosting brings Docker, browser infrastructure and more maintenance back into your life. **Repo:** [github.com/firecrawl/firecrawl](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io) ## 5\. ComfyUI: still the serious rabbit hole for AI images and video I used [ComfyUI](https://github.com/Comfy-Org/ComfyUI?ref=theseguysknow.io) in 2025, so when it first came up in a list of "2026 tools" I thought I had lost a year somewhere. I had not. ComfyUI is not new in 2026\. It is here because it is still one of the best places to mess around with generative media when a normal prompt box starts feeling restrictive. The basic idea is a node graph. Instead of giving one app a prompt and accepting whatever happens behind the curtain, you build the generation process visually. Models, prompts, conditioning, samplers, upscaling, image inputs and other steps can be connected into a workflow and reused. The current project has moved well beyond basic Stable Diffusion images too. Its own README now describes image, video, audio and 3D workflows, reusable subgraphs, templates, a local API and App Mode, which can put a simpler front end on top of a horrible-looking graph. It also has one of my favourite weird ComfyUI tricks: a generated PNG can carry the workflow metadata, so a compatible image can be loaded back into ComfyUI and bring the graph with it. That is why people pass around workflow images instead of only screenshots. This level of control is also why ComfyUI can look completely mental the first time you open a complicated workflow. A [2026 Reddit beginner](https://www.reddit.com/r/comfyui/comments/1s8tu7r/how%5Fto%5Flearn%5Fcomfyui%5Fin%5F2026%5Fall%5Ftutorials%5Fseem/?ref=theseguysknow.io) complained that tutorials kept referring to nodes that were renamed, changed or no longer there. Another user managed to [burn 30 minutes of paid RunPod time just looking for ComfyUI Manager](https://www.reddit.com/r/comfyui/comments/1q989x6/where%5Fthe%5Fhell%5Fis%5Fthe%5Fcomfyui%5Fmanager%5Fi%5Fwasted%5F30/?ref=theseguysknow.io). This is the annoying side of ComfyUI: one missing custom node can make you question your life choices before you have generated anything. Start with a small workflow that produces one thing you care about, then add pieces. Once it clicks, this is an awesome place to play around. You can take somebody else's workflow, swap the model, change the sampler, insert ControlNet or an upscaler, add video stages, turn parts into reusable subgraphs, expose it through an API and keep the whole graph for later. This is the part I like about it. You can spend an evening trying something strange instead of waiting for a normal image generator to add a feature six months later. And you do not need a monster PC. [RunPod has current ComfyUI GPU Pod and Serverless guides](https://docs.runpod.io/tutorials/pods/comfyui?ref=theseguysknow.io), and the workflows themselves can be exported as JSON. RunPod is only one option; ComfyUI is not tied to a specific host, so if another GPU rental service has a better price for the hardware you need, use that instead. It is complicated at first, and old tutorials, renamed nodes, missing custom nodes and VRAM limits can waste time fast. I still like it for the same reason it looks intimidating: there is so much you can mess with. If Midjourney-style prompting feels like ordering from a menu, ComfyUI is being allowed into the kitchen and finding 400 ingredients nobody explained to you. **Repo:** [github.com/Comfy-Org/ComfyUI](https://github.com/Comfy-Org/ComfyUI?ref=theseguysknow.io) ## A smaller repo I find more interesting than another Claude skills list: LLM Council This is the one I was trying to remember when thinking about AI "councils." [LLM Council](https://github.com/aiwithremy/claude-skills-llm-council?ref=theseguysknow.io) is tiny next to the five above at about 1.4k stars, so I am not going to pretend it belongs in a popularity ranking. I am including it because the council setup is something I can see myself using. You give it a hard decision. The skill runs the question through five different advisor roles, has them review each other's thinking, then uses a chairman to combine the disagreement and advice into one final recommendation. The repo is based on Andrej Karpathy's LLM Council idea. This is the sort of thing I would use for strategy, positioning or a decision where I specifically want the AI to attack my thinking instead of agreeing with me. The repo itself gives examples such as choosing between offers, comparing positioning angles or deciding whether to hire somebody versus automate a job. A user who had been trying the method wrote that it ["genuinely changed how I use Claude for decisions"](https://www.reddit.com/r/ClaudeCode/comments/1tqcy13/the%5Fllm%5Fcouncil%5Fstop%5Fasking%5Fai%5Fone%5Fquestion%5Fask/?ref=theseguysknow.io). There is one obvious warning: five advisors are not five independent human experts. If every seat uses the same model and the same underlying blind spots, you can get five nicely formatted versions of the same mistake. A separate [Claude Code discussion](https://www.reddit.com/r/ClaudeCode/comments/1unl2si/anthropic%5Fhas%5Fa%5Fnative%5Fadvisor%5Ffor%5Fclaude%5Fcode/?ref=theseguysknow.io) makes a good point that a real multi-model council is stronger when opinions come from different model families or labs. I would still steal the core idea. For decisions, "argue with me from five angles and then tell me where the arguments break" is far more useful than asking one model, "do you think this is a good idea?" **Repo:** [github.com/aiwithremy/claude-skills-llm-council](https://github.com/aiwithremy/claude-skills-llm-council?ref=theseguysknow.io) ## The repo we use in our own field: Marketing Skills [Corey Haines' Marketing Skills](https://github.com/coreyhaines31/marketingskills?ref=theseguysknow.io) is another one I would recommend, even though its 43.2k stars put it well below the main five. This one is a library of skills for AI agents covering SEO, AI SEO, CRO, copywriting, analytics, paid ads, growth, product marketing, PR and a lot more. It works with Claude Code, Codex, Cursor, Windsurf and other agents that support the Agent Skills format. We have used it for TGK, but we did not blindly install everything. We pulled in selected skills that matched work we already needed: SEO Audit, AI SEO, Site Architecture and Public Relations, then adapted them for our setup. That is how I would treat a big skill collection. Pick the parts that have a job instead of stuffing 40 instructions into an agent because a Reel told you to. The growth has been ridiculous: a [Reddit post from three months ago](https://www.reddit.com/r/SideProject/comments/1spqw1p/found%5Fa%5Fgithub%5Frepo%5F20k%5Fstars%5Fthat%5Fturns%5Fai/?ref=theseguysknow.io) described it as a 20K+ star repo, while GitHub shows about 43.2k now. Another marketer said using the repo to iterate on their site's technical SEO was ["shockingly easy"](https://www.reddit.com/r/smallbusiness/comments/1qr7qtn/best%5Fai%5Fmarketing%5Fskills%5Ffor%5Fclaude%5Five%5Fcome/?ref=theseguysknow.io). There is fair criticism too. One recent [skill review](https://www.reddit.com/r/hermesagent/comments/1uj0pss/skill%5Freview%5Fcoreyhaines31marketingskills/?ref=theseguysknow.io) questioned whether giant skill packs are better than a few narrow, deeply tested skills and criticized the repo's evaluation setup. I agree with the first concern more than the second: a big library is useful because you can choose from it. You do not need the entire library active for every job. The repo now even includes its own `marketing-council` skill, which brings the council idea above into marketing decisions. What I like is that it gives an agent a repeatable process for work we already do, and you can take only the pieces you need. That is much easier to recommend than a giant pack where I have no idea whether half the skills are useful. **Repo:** [github.com/coreyhaines31/marketingskills](https://github.com/coreyhaines31/marketingskills?ref=theseguysknow.io) ## Which one would I start with? If you code with an AI agent every day, start with Superpowers and see whether the extra process improves your bigger jobs. If you are trying to automate work between apps, n8n is the obvious rabbit hole. If you want private or local models, start with Ollama. If your agent constantly needs the web, look at Firecrawl. If you care about AI images or video and want far more control than a normal generator gives you, ComfyUI is still worth learning. And if your problem is decision-making rather than production, I would play with the LLM Council before installing the seventeenth viral Claude productivity pack of the week. I am sure the feed will find another 20 "essential" repos next week. These are the ones where I can explain what you get, why people keep using them and what can piss you off before you install anything. --- #### Sources - [Superpowers GitHub repository](https://github.com/obra/superpowers?ref=theseguysknow.io) - [n8n GitHub repository](https://github.com/n8n-io/n8n?ref=theseguysknow.io) - [Ollama GitHub repository](https://github.com/ollama/ollama?ref=theseguysknow.io) - [Firecrawl GitHub repository](https://github.com/firecrawl/firecrawl?ref=theseguysknow.io) - [ComfyUI GitHub repository](https://github.com/Comfy-Org/ComfyUI?ref=theseguysknow.io) - [LLM Council GitHub repository](https://github.com/aiwithremy/claude-skills-llm-council?ref=theseguysknow.io) - [Marketing Skills GitHub repository](https://github.com/coreyhaines31/marketingskills?ref=theseguysknow.io) - [Reddit: Superpowers user discussion](https://www.reddit.com/r/ClaudeCode/comments/1u2tqud/are%5Fyou%5Fguys%5Fstill%5Fusing%5Fthe%5Fsuperpowers%5Fskill/?ref=theseguysknow.io) - [Reddit: Superpowers user workflow and verification discussion](https://www.reddit.com/r/ClaudeCode/comments/1r9y2ka/claude%5Fcodes%5Fsuperpowers%5Fplugin%5Factually%5Fdelivers/?ref=theseguysknow.io) - [Reddit: why one developer removed Superpowers](https://www.reddit.com/r/ClaudeCode/comments/1upt8mr/why%5Fi%5Fremoved%5Fsuperpowers%5Ffrom%5Fclaude/?ref=theseguysknow.io) - [Reddit: n8n and learning agent workflows](https://www.reddit.com/r/n8n/comments/1srjxfy/is%5Fn8n%5Fthe%5Fbest%5Fplace%5Fto%5Flearn%5Fai%5Fagent/?ref=theseguysknow.io) - [n8n Community: an agent that repairs failed n8n workflows](https://community.n8n.io/t/agents-fixing-n8n-workflows/299685?ref=theseguysknow.io) - [Reddit: Ollama vs llama.cpp](https://www.reddit.com/r/LocalLLaMA/comments/1q64f26/llamacpp%5Fvs%5Follama%5F70%5Fhigher%5Fcode%5Fgeneration/?ref=theseguysknow.io) - [Reddit: why people use Ollama](https://www.reddit.com/r/LocalLLaMA/comments/1icta5y/why%5Fdo%5Fpeople%5Flike%5Follama%5Fmore%5Fthan%5Flm%5Fstudio/?ref=theseguysknow.io) - [Reddit: Firecrawl vs Crawl4AI user comparison](https://www.reddit.com/r/AgentsOfAI/comments/1t3pe4e/firecrawl%5Fvs%5Fcrawl4ai%5Fi%5Ftried%5Fboth%5Fand%5Fheres%5Fwhat/?ref=theseguysknow.io) - [Reddit: ComfyUI tutorials and changing nodes](https://www.reddit.com/r/comfyui/comments/1s8tu7r/how%5Fto%5Flearn%5Fcomfyui%5Fin%5F2026%5Fall%5Ftutorials%5Fseem/?ref=theseguysknow.io) - [Reddit: RunPod/ComfyUI Manager problem](https://www.reddit.com/r/comfyui/comments/1q989x6/where%5Fthe%5Fhell%5Fis%5Fthe%5Fcomfyui%5Fmanager%5Fi%5Fwasted%5F30/?ref=theseguysknow.io) - [RunPod: running ComfyUI on a GPU Pod](https://docs.runpod.io/tutorials/pods/comfyui?ref=theseguysknow.io) - [Reddit: LLM Council discussion](https://www.reddit.com/r/ClaudeCode/comments/1tqcy13/the%5Fllm%5Fcouncil%5Fstop%5Fasking%5Fai%5Fone%5Fquestion%5Fask/?ref=theseguysknow.io) - [Reddit: Marketing Skills user experience](https://www.reddit.com/r/smallbusiness/comments/1qr7qtn/best%5Fai%5Fmarketing%5Fskills%5Ffor%5Fclaude%5Five%5Fcome/?ref=theseguysknow.io)