diff --git a/README.md b/README.md
index 2b776c7..2ea6d54 100644
--- a/README.md
+++ b/README.md
@@ -4,16 +4,11 @@ A documentation and pattern library that gives LLM agents the context they need
 
 The goal is to use [opencode](https://opencode.ai) (or any LLM-powered coding tool) to iteratively create [marimo](https://marimo.io) notebook workflows that query and analyze company data.
 
-## Getting Started
+## Setup
 
-### Prerequisites
-
-- [opencode](https://opencode.ai) installed
-- Access to the InternalAI platform (ContactDB + DataIndex running locally, accessible via http://localhost:42000)
-
-### Configuring opencode with LiteLLM
-
-To use models through LiteLLM, add the following to `~/.config/opencode/config.json`:
+1. Install [opencode](https://opencode.ai)
+2. Make sure InternalAI is running locally (ContactDB + DataIndex accessible via http://localhost:42000)
+3. Configure LiteLLM — add to `~/.config/opencode/config.json`:
 
 ```json
 {
@@ -38,78 +33,33 @@ To use models through LiteLLM, add the following to `~/.config/opencode/config.j
 
 Replace `xxxxx` with your actual LiteLLM API key.
 
-### Running opencode
+## Quickstart
 
-From the project root:
+1. Run `opencode` from the project root
+2. Ask it to create a workflow, e.g.: *"Create a workflow that shows all meetings about Greyhaven in January"*
+3. The agent reads `AGENTS.md`, proposes a plan, and generates a notebook like `workflows/001_greyhaven_meetings_january.py`
+4. Run it: `uvx marimo edit workflows/001_greyhaven_meetings_january.py`
+5. Iterate — review the output in marimo, go back to opencode and ask for refinements
 
-```bash
-opencode
-```
+## How AGENTS.md is Structured
 
-opencode will pick up `AGENTS.md` automatically and use it as the entry point to understand the project, the available APIs, and how to write workflows.
+`AGENTS.md` is the entry point that opencode reads automatically. It routes the agent to the right documentation:
 
-## How AGENTS.md Works
+| Topic | File |
+|-------|------|
+| Company context, tools, connectors | `docs/company-context.md` |
+| People, contacts, relationships | `docs/contactdb-api.md` |
+| Querying emails, meetings, chats, docs | `docs/dataindex-api.md` |
+| Connector-to-entity-type mappings | `docs/connectors-and-sources.md` |
+| Notebook templates and patterns | `docs/notebook-patterns.md` |
 
-`AGENTS.md` is the routing guide for LLM agents. It is structured as follows:
-
-1. **Purpose statement** — Explains that the agent's job is to build marimo notebooks that analyze company data.
-
-2. **Documentation routing table** — Directs the agent to the right file depending on the topic:
-
-   | Topic | File |
-   |-------|------|
-   | Company context, tools, connectors overview | `docs/company-context.md` |
-   | People, contacts, relationships | `docs/contactdb-api.md` |
-   | Querying emails, meetings, chats, docs | `docs/dataindex-api.md` |
-   | Connector-to-entity-type mappings | `docs/connectors-and-sources.md` |
-   | Notebook creation patterns and templates | `docs/notebook-patterns.md` |
-
-3. **API base URLs** — ContactDB and DataIndex endpoints (both via Caddy proxy and direct).
-
-4. **Common query translation table** — Maps natural-language questions (e.g. "Who am I?", "Recent meetings") to the corresponding API calls.
-
-5. **Workflow rules** — When to create a notebook vs. answer inline, naming conventions, and the requirement to propose a plan before implementing.
-
-## Workflow
-
-### How it works
-
-1. **Ask a question in opencode** — Describe what you want to analyze (e.g. "Show me all meetings about Greyhaven in January").
-
-2. **Agent reads AGENTS.md** — opencode picks up the routing guide and navigates to the relevant docs to understand the APIs.
-
-3. **Agent proposes a plan** — Before writing code, the agent outlines: Goal, Data Sources, Algorithm, and Output Format.
-
-4. **Agent creates a marimo notebook** — A `.py` file is written to `workflows/` following the naming convention `<NNN>_<topic>_<scope>.py`.
-
-5. **Iterate** — Run the notebook with `marimo edit workflows/<name>.py`, review the output, and ask the agent to refine.
-
-### Workflow output format
-
-Workflows are [marimo notebooks](https://marimo.io) — plain Python files with `@app.cell` decorators. They typically follow this structure:
-
-- **params cell** — User-editable parameters (search terms, date ranges, contact names)
-- **config cell** — API base URLs
-- **setup cell** — Shared imports (`httpx`, `polars`, `marimo`)
-- **data cells** — Fetch and transform data from ContactDB / DataIndex
-- **output cells** — Tables, charts, or markdown summaries
-
-### Naming convention
-
-```
-workflows/<NNN>_<topic>_<scope>.py
-```
-
-Examples:
-- `001_greyhaven_meetings_january.py`
-- `002_email_activity_q1.py`
+It also includes API base URLs, a translation table mapping natural-language questions to API calls, and rules for when/how to create workflow notebooks.
 
 ## Project Structure
 
 ```
 internalai-agent/
 ├── AGENTS.md                        # LLM agent routing guide (entry point)
-├── README.md
 ├── docs/
 │   ├── company-context.md           # Monadical org, tools, key concepts
 │   ├── contactdb-api.md             # ContactDB REST API reference