I started working on a CLI to keep OpenClaw organized: https://clawtique.ai

The analogy is that of a boutique. OpenClaw goes to the boutique and is "dressed up" properly so that all the various components are organized and easy to maintain. Clawtique is organized around the concept of a "dress", basically a bundle of everything OpenClaw needs to achieve a goal (skills, plugins, memory segments, crons, ...). The CLI enables users to easily dress and undress OpenClaw so that you can try out a dress and easily remove it without leaving any dangling dependencies.

Some dresses i created are the sleeping-coach (bundles the OuraClaw plugin https://github.com/rickybloomfield/OuraClaw with skills and crons that notify you on how you slept) and the fabric-sync (bundles the fabric plugin https://github.com/onfabric/openclaw-fabric with skills and crons to maintain an accurate USER.md of you based on your interactions on the various big tech platforms).

The follow up is to have OpenClaw use the Clawtique CLI itself so that it can easily dress and undress with whatever it needs to accomplish the goal without everything becoming an unmanageable mess.

Here is the repo: https://github.com/onfabric/clawtique

Curious to know what you guys think

Comments URL: https://news.ycombinator.com/item?id=47476677

Points: 7

# Comments: 3

I would like a single number that I would use to optimize the pipeline with but I find it hard to figure out what that number should be measuring.

I've been searching, posting, chatting on various platforms for more than a decade now. context-use allows me to turn all those years of data into context that I can bring to my favorite agents.

Agents today are largely blind to the user. Even when they add memory, they usually start from zero instead of using the rich context already locked up in consumer platforms. The result is agents with big gaps in their understanding of the user, generic experiences, lots of repetitive input, and still poor engagement.

We’re trying to solve this by focusing on *portable, user-controlled personal context*. **

With Fabric, an agent doesn’t just recall what happened inside that one app. It can start from a richer baseline that evolves as the user interacts across their existing apps, using memories created from tens of thousands of real interactions, such as:

- Travel posts and restaurant stories from Instagram - Videos watched on YouTube and TikTok - Years of Google search, shopping, and navigation behavior

What we’ve built so far

- Official connectors for Instagram (global), Google (EEA, UK) and YouTube (EEA, UK) for beta users. First company globally to build DMA style integrations with all Big Tech platforms. - An MCP server so users can bring their context into MCP clients - Under the hood, Fabric: - Ingests raw interactions that look very different (e.g. Google searches vs Instagram stories) - Normalises them into an ActivityStreams-style schema - Builds a personal knowledge graph on top - Exposes higher-level “memories” that agents can retrieve via our MCP server - A user portal where users can connect data sources and manage their context

We have a few strong opinions:

- The profile belongs to the person, not to any one app. Fabric is designed as a neutral “personal context vault” that multiple AI apps can plug into (with user approval), rather than a proprietary profile owned by a single product. - User control is first-class. Users can: - Choose which data sources to connect - See which apps/agents have access - Revoke access and delete their context from Fabric - We’re explicitly building on top of GDPR data portability and EU/UK “smart data” initiatives, rather than shadow integrations.

We would love to hear what you think.

And if this is interesting, you can sign up to our waitlist or join our beta at onfabric.io.

Comments URL: https://news.ycombinator.com/item?id=46062078

Points: 1

# Comments: 0