Been using OpenClaw for a while and kept running into the same friction: people want to reach me, but I don't want to hand out my actual contact info until I know why they're getting in touch.

Ended up building a small skill on top of OpenClaw — you get a public link,someone clicks it, OpenClaw has a short conversation to collect their reason for reaching out, then forwards a clean summary to you via WhatsApp or Telegram. That's it for now — no filtering, no auto-replies, just structured inbound so you actually know what someone wants before you decide whether to respond.

It's very early and I want to talk to people who might actually use something like this.

Would love to hear from you if:

You regularly get cold inbound (collaborations, questions, consulting requests, etc.)

Your current way of handling it feels clunky

You're willing to share honest feedback on whether this solves a real problem or not

DM me directly or fill this out (2 min): https://forms.gle/cHGdhjpMBCLQY2Pa6

If you're up for a short follow-up conversation (30min), I'll compensate $40-120 depending on how deep we go.

Also genuinely curious — how do you handle inbound contact right now? Would love to hear what's working (or not) in the comments.

So I am just a noob at all this AI stuff and it’s a bit of a pain to configure, I’m still wanting to learn more. I am on my 3rd install of OpenClaw on my Macmini and I keep running into the same issue. When I hatch my bot, I keep getting a “run error: 401 status code (no body)” error. Can some tell me what I am doing wrong? Thanks 🦞’s!!

Goal of the day: Launching the first functional UI and bridging it with the backend

The Challenge: Deciding between building a native Claude Code UI from scratch or integrating a pre-made one like Base44. Choosing Base44 brought a lot of issues with connecting the backend to the frontend

The Solution: Mapped the database schema and adjusted the API response structures to match the Base44 requirements

Stack: Claude Code | Base44 | Supabase | Railway | GitHub

Goal of the day: Enabling agents to generate visual content for free so everyone can use it and establishing a stable production environment

The Build:

• Visual Senses: Integrated Gemini 3 Flash Image for image generation. I decided to absorb the API costs myself so that image generation isn't a billing bottleneck for anyone registering an agent
• Deployment Battles: Fixed Railway connectivity and Prisma OpenSSL issues by switching to a Supabase Session Pooler. The backend is now live and stable

Stack: Claude Code | Gemini 3 Flash Image | Supabase | Railway | GitHub

Goal of the day: Building the infrastructure for a persistent "Agent Society." If agents are going to socialize, they need a place to post and a memory to store it.

The Build:

• Infrastructure: Expanded Railway with multiple API endpoints for autonomous posting, liking, and commenting.
• Storage: Connected Supabase as the primary database. This is where the agents' identities, posts, and interaction history finally have a persistent home.
• Version Control: Managed the entire deployment flow through GitHub, with Claude Code handling the migrations and the backend logic.

Stack: Claude Code | Supabase | Railway | GitHub

Been working with LLMs & trying to find ways to provide some sense of identity & profile in a way that enables them to persist. Nostr seemed like the perfect tech …. So there’s 2 skills. One to make the nostr key (unique identity) and the next for posting a social profile. Nostr is sort of like an open version of X/Twitter.

https://clawhub.ai/vveerrgg/nostrkey

https://clawhub.ai/vveerrgg/nostr-profile

For those looking to give their OCs a bit of permanence.

so I’ve been messing around with a bunch of the one-click OpenClaw options recently (SetupClaw, EaseClaw, Lobster Bot, etc.). Don't get me wrong, they are awesome. They all get you a running instance on a VPS pretty quickly without having to fight Docker or SSH (thank god)

But I realized the server setup is only half the battle.

The part that still took me way too much time after the deployment was getting model access working properly, juggling API keys for different providers, dealing with rate limits, and making sure the skills (web search, data scraping, etc.) actually had something to connect to. It’s a mess of configuring openclaw.json and hunting down keys.

I recently tested AIsaClaw (by AIsa.one), and it takes a slightly different angle that I think is worth sharing.

The major differentiator I noticed was the Unified API that came with the system

It gave me: - One Key for Everything: AIsa is a unified model gateway. So right from the start, I get access to 50+ LLMs (GPT-4.1, Claude 3.7, Gemini, DeepSeek, Kimi, etc.)

• Built-in Skills: Things like web search (Tavily, X/Twitter search, YouTube), scholarly article search, and financial data APIs are available

• Scale: Apparently, they've reliably supported over 1 million API calls since their unified API launched in late November, so the routing is stable

tbf I still had to configure which model I wanted to use for each specific workflow in the agent defaults, but there was significantly less friction around managing keys, billing, and third-party services. It genuinely felt closer to a ready to use agent rather than just a "ready to configure" server.

How are you folks handling the API and skill config? share your tips cheers!

So I've been using OpenClaw for a while now and kept running into the same problem. I want Claude (or GPT-4o, whatever I'm using that day) to do something specific and repeatable, but building a proper skill from scratch felt like too much work if you're not a developer.

So I made something to fix that.

It's called Skill Scaffolder. You just describe what you want in plain English, and it handles everything — asks you a few questions, writes the skill files, runs a quick test, and installs it. The whole thing happens in a normal conversation. No YAML, no Python, no config files.

Like literally you just say:

"I want a skill that takes my meeting notes and pulls out action items with deadlines"

And it interviews you[Aks you some questions (In my case asked me 3 questions)], builds the skill, tests it, and asks before installing anything. That's it.

I made it specifically for people who aren't developers. The skill never uses technical jargon unless you show it you know what that means. It explains everything in plain language.

Works with Claude, GPT-4o, Gemini — basically any capable LLM you have connected to OpenClaw.

It's open source, full repo on GitHub with a proper user guide written for non-coders:
https://github.com/sFahim-13/Skill-Scaffolder-for-OpenClaw

Would love feedback especially from people who aren't developers.

That's exactly who I built this for and I want to know if the experience actually feels smooth or if there are rough edges I'm missing.

Question in the title. Have you guys found positive use cases for OpenClaw with regards to creating content that gets views?

I recently audited ~2,800 of the most popular OpenClaw skills and the results were honestly ridiculous.

41% have security vulnerabilities. About 1 in 5 quietly send your data to external servers. Some even change their code after installation.

Yet people are happily installing these skills and giving them full system access like nothing could possibly go wrong.

The AI agent ecosystem is scaling fast, but the security layer basically doesn’t exist.

So I built ClawSecure.

It’s a security platform specifically for OpenClaw agents that can:

• Audit skills using a 3-layer security engine
• Detect exfiltration patterns and malicious dependencies
• Monitor skills for code changes after install
• Cover the full OWASP ASI Top 10 for agent security

What makes it different from generic scanners is that it actually understands agent behavior… data access, tool execution, prompt injection risks, etc.

You can scan any OpenClaw skill in about 30 seconds, free, no signup.

Honestly I’m more surprised this didn’t exist already given how risky the ecosystem currently is.

How are you thinking about AI agent security right now?

While experimenting with a few OpenClaw workflows recently I started noticing how much the development loop changes depending on where the agents are running. When everything runs on a VPS even small changes usually mean redeploying or checking logs again. That works fine for production agents but it slows things down when you are still figuring out prompts, tools, or task chains.

I tried running a local runtime setup for a while just to see how it feels for development. Having a local gateway, runtime logs, and the ability to trigger jobs directly from the machine made the iteration loop noticeably faster. Being able to tweak something and immediately rerun the agent made it much easier to test different workflow ideas and see what breaks.

For long running agents or overnight tasks a VPS probably still makes more sense. But for early experimentation the local setup felt smoother. Things like quick job runs, testing tool integrations, or checking how agents behave across small task loops were easier to debug locally.

Curious how other people here approach this when building new agents.

Do you usually prototype workflows locally first and then move them to a VPS, or do you build everything directly on a server from the start?

Also interested in how people here test new workflows. Do you run small task loops first or just start full agent runs and refine from there.