ClariLayer

An Anthropic engineer shared the exact system they use as a second brain. Three folders. One file. One evening to build. Most people use Claude the same way every day. Open a new tab. Rebuild context. Get an answer. Close the tab. Tomorrow it remembers nothing. You are still the one holding all the context. You are still the one resetting. This architecture solves that problem. The system is built around three folders and one file. raw/ holds everything unstructured. Articles, transcripts, PDFs, voice memos, screenshots. Drop it in and never touch it again. Immutable ground truth. wiki/ is where Claude converts everything in raw into structured, linked, cross-referenced knowledge. Clean. Organized. This is the folder Claude actually thinks from. The human reads it. The model writes it. output/ is where finished work lands. Reports, posts, documents, presentations. Everything Claude builds using the wiki as its source. At the center is CLAUDE.md. Not a prompt, but a persistent layer of identity, preferences, goals, and project context. Claude reads it before every session. You never explain yourself again. Five automations run the system. Ingest captures and extracts new sources into the wiki. Write retrieves context and drafts outputs. Manage links decisions to context. Review summarizes and updates. Maintain prunes and improves connections. Every session adds to the system. Every source makes the wiki smarter. The returns compound over time. One month in, context stops disappearing. Three months in, the vault surfaces ideas you forgot you had. Six months in, the gap between compounding and resetting becomes impossible to ignore. Build once. Maintain daily. Let it compound. Bookmark this.

Someone published the architecture for an AI agent that never stops learning. 8 agents. One loop. Runs without you. Every AI agent stops improving the moment you stop improving it. New workflows and engineering patterns appear on GitHub every day. None of them reach your agent unless a human finds them, understands them, and installs them manually. This system automates everything up to the final human review. Here’s how the pipeline divides the work across eight specialized agents. Scout monitors GitHub Trending and runs targeted searches for new AI workflows continuously. Filter removes everything irrelevant before any model touches it - deterministic rules, no LLM cost wasted on obvious noise. Reader extracts documentation and understands the workflow before looking at a single line of source code. Extractor pulls out reusable patterns and structures them into a standardized format. Skill Score validates against objective criteria - confidence above 0.85, minimum three workflow steps, general purpose, reusable. Skill Generator converts the validated workflow into a complete Agent Skill package with examples, commands, and tests. Reviewer asks one question: Would an experienced engineer install this without editing it? Publisher creates the pull request and assigns a human reviewer. Automation proposes. Humans approve. The library compounds over time. More skills lead to richer context. Richer context leads to better discoveries. Better discoveries lead to stronger skills. The loop never stops. Today, finding new capabilities is still a human job. This pipeline makes it continuous. The complete architecture is in the image. Open source.