Update repo structure with detailed project docs

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# Claw Ideas: Problem Solver Projects
A collection of architecturally challenging project ideas for developers.
## 1. Automated Code-Review-Pipeline (The Gatekeeper)
An agent that monitors git PRs and provides intelligent code reviews using LLMs. Focus on architectural feedback rather than just linting.
These projects are designed for developers looking to grow into "Problem Solver / Engineer" roles by focusing on infrastructure, automation, and intelligent tooling.
## 2. Observability-as-a-Service (Engineering Monitoring)
Aggregate server, container, and application logs with RAG-based analysis to detect and explain anomalous system behavior.
## Projects
## 3. Serverless Micro-Stack Manager
An abstraction layer/CLI to manage complex Docker-Compose stacks or deployments via chat or API. Focus on Developer Experience (DX).
## 4. RAG Knowledge Hub
A "Second Brain" for your infrastructure, indexing your configs, docs, and notes to answer technical questions about your specific setup.
- **[Automated Code-Review-Pipeline](./code-review.md)**
- **[Observability-as-a-Service](./observability.md)**
- **[Serverless Micro-Stack Manager](./micro-stack.md)**
- **[RAG Knowledge Hub](./rag-hub.md)**

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# Automated Code-Review-Pipeline (The Gatekeeper)
## Problem
Manual code review is tedious and spesso inconsistent. Architectural anti-patterns often slip through PRs.
## Instructions
1. **Webhook Integration:** Set up a Gitea webhook that triggers a function on your VPS when a PR is opened.
2. **Analysis Engine:** Use an LLM (e.g., via Gemini API) to analyze the .
3. **Prompt Engineering:** Define a system prompt for the agent to focus on *architectural integrity* and *complexity metrics* rather than just syntax checking.
4. **Feedback Loop:** Have the agent post its review directly back to the PR comments.
## Goal
Automate the "High-Level Review" stage of your development pipeline.

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# Serverless Micro-Stack Manager
## Problem
Managing multiple Docker Compose projects via SSH/CLI is exhausting and error-prone.
## Instructions
1. **Abstraction Layer:** Build a thin Go or Python API that wraps commands.
2. **ChatOps Interface:** Connect this API to your chat agent (via Discord/Telegram) so you can issue commands like "Deploy auth-service" or "Restart proxy".
3. **Version Control:** Add logic to pull the latest image/code, backup data volumes, and restart the service atomically.
4. **Health Check:** Ensure the agent verifies the service is actually "up" (HTTP 200) after the restart before confirming.
## Goal
Build your own platform-as-a-service (PaaS) on your own VPS.

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# Observability-as-a-Service (Engineering Monitoring)
## Problem
Logs are overwhelming. You only notice issues when the system is already down.
## Instructions
1. **Aggregator:** Set up a lightweight log forwarder (like Vector or Promtail) to push logs to a central database (Postgres or Elasticsearch).
2. **Analytics:** Implement a background script that polls for error spikes or latency anomalies.
3. **RAG Context:** Inject your architecture documentation (READMEs, design docs) into the agent's context so it understands *what* service is doing *what*.
4. **Actionable Alerts:** When an anomaly occurs, use the agent to analyze the logs *in context* and provide a summary + suggested fix.
## Goal
Move from "Log Monitoring" to "Intelligent System Health".

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# RAG Knowledge Hub
## Problem
Technical knowledge about personal infrastructure is scattered across config files, chat history, and stale documentation.
## Instructions
1. **Data Collection:** Crawl your , , and project directories for configuration files (, , ).
2. **Embedding:** Use an embedding pipeline to vectorize this knowledge.
3. **Retrieval:** Set up a web interface or terminal bot that allows querying your "Second Brain".
4. **Sync Mechanism:** Create a task that updates the vector database whenever a config file is modified (using or ).
## Goal
Create an infrastructure-aware assistant that knows your setup intimately.