u/Final_Elevator_1128

After three weeks of tutorials, four YouTube videos, two Reddit rabbit holes, and one very patient friend who works in DevOps, I have successfully installed OpenClaw.

It is running.

I think.

The terminal says something is happening. There are logs. The logs look confident. I have chosen to trust the logs.

Now I just need to figure out:

- Why it forgot who I am between sessions

- Why it tried to email my entire contact list at 2am

- Why the memory I spent four hours configuring apparently doesn't exist anymore

- Why every update breaks exactly the thing I fixed last week

- Whether the VPS I'm paying for is actually doing anything or just vibing

- What a Docker container is and why I have seventeen of them

- Why the agent that was supposed to save me time has consumed fourteen hours of my weekend

Anyway.

I'm sure this is fine and I'm definitely not questioning every life decision that led me here.

For the people who actually have this working — genuinely, how? Like actually how.

What does your setup look like? What did you sacrifice to get here? What do you wish someone had told you before you started? Is there a version of this that doesn't require a computer science degree to maintain?

Asking for a friend. The friend is me. I am not okay.

But also genuinely curious — for those who've been running OpenClaw for a while, is the juice worth the squeeze? What's your honest take after real usage?

Hermes already has a solid execution layer — 70+ skills across a bunch of categories. What feels missing is a strong knowledge layer. Memory is handled natively, but structured external knowledge (research bases, domain-specific info, niche datasets) is still pretty thin across community skills. One standout I found is llm-wiki-compiler by AtomicMem. It’s a clean example of what a proper knowledge skill could look like:

• citations per paragraph
• built-in quality checks
• semantic search
• Obsidian integration
• multi-provider support
• agents can read/write via MCP

What’s interesting is it’s designed to be forked and adapted, not rebuilt from scratch.Feels like a lot of domains are still open here:

• legal
• medical
• finance
• niche communities
• client-specific systems
• dev docs

Given how the skills hub is growing, early knowledge-layer builds in these areas seem underexplored.

I've been studying the llm-wiki-compiler codebase by AtomicMem as a reference since it seems like the most complete example of a production-ready knowledge skill for Hermes. v0.2.0 shipped some genuinely impressive infrastructure — paragraph citations, automated linting, Obsidian integration, semantic search, MCP server, multi-provider support.

What I'm trying to understand before forking it:

1. Is the MCP server path the cleanest way to integrate a custom knowledge skill into Hermes or does packaging it as a native skill work better in practice?

2. How much of the llm-wiki-compiler architecture is specific to wiki-style knowledge versus reusable for other knowledge formats like structured databases or document collections?

3. Has anyone built domain-specific versions of this pattern — legal, medical, finance, coding docs — and run into limitations that aren't obvious from the codebase?

4. Is the semantic search implementation in v0.2.0 good enough for production niche domain work or does it need significant tuning for specialized terminology?

5. AtomicMem positions this as forkable infrastructure for new projects building on Hermes. Has anyone actually done that and what was the experience?

Genuinely trying to avoid reinventing something that already exists. Any experience from people who've gone through this would be really useful.

For context - Hermes memory handles personal context well (what you do, your preferences, session history) but my project needs something separate for external knowledge. Domain-specific sources, research docs, things the agent needs to know that aren't tied to a specific conversation.

I've been studying how some community skills handle this and the pattern that keeps coming up:

- Ingest external sources on command

- Compile them into structured queryable format

- Expose via MCP so the agent can call it natively

- Let answers compound into new pages over time

A few specific questions for anyone who's gone through this:

**1. Skill vs MCP server vs background infra**

Which integration path actually works best in practice? I'm leaning toward MCP server because it keeps things modular but curious if others found a different approach cleaner.

**2. Semantic search vs keyword retrieval**

Is embedding-based search worth the overhead for a Hermes skill or is keyword search good enough for most use cases?

**3. Quality control**

As the knowledge base grows, how are people keeping it clean? Broken links, orphaned pages, inconsistencies — does anyone have an automated approach for this?

**4. Source attribution**

Has anyone built citation tracking into their knowledge layer? The "did the AI make this up" problem feels important to solve at the infrastructure level rather than prompting around it.

**5. Multi-provider support**

Is it worth building provider-agnostic from day one or is it premature optimization at the early stage?

I know llm-wiki-compiler just shipped v0.2.0 and seems to have tackled most of these — paragraph-level source citations, automated linting, semantic search, MCP server, Obsidian integration, multi-provider support. Might just study that codebase as a reference.

But genuinely curious how others have approached this. What worked, what didn't, what you'd do differently