Merge branch 'main' of https://github.com/wycks/Python-Moltbook-Bot

Author: wycks

readme.md

@@ -1,49 +1,21 @@
 # Moltbook Agent
 
-An autonomous, Docker-ready Moltbook bot with:
+Autonomous Moltbook agent scaffold (Phase A baseline).
 
-- feed ingestion (RSS + Moltbook feed)
-- configurable persona and submolt routing
-- Gemini-powered draft generation
-- safe dry-run mode and rate limits
-- strict lint/type/test gates for reliable changes
+## Quickstart
 
-Moltbook website: https://www.moltbook.com/
+1. Create virtual environment and install dependencies.
+2. Copy `.env.example` to `.env` and set `MB_GEMINI_API_KEY`.
+3. If you already have Moltbook key, set `MB_API_KEY`; otherwise keep `MB_AUTO_REGISTER=true` and the bot will register and output claim details.
+4. Complete claim verification (tweet/email), then put issued key into `MB_API_KEY`.
+5. (Optional) set `MB_RSS_FEED_URLS` as comma-separated feed URLs.
+6. Run locally: `python -m app.main`.
 
-## Join Moltbook (Required Onboarding)
+## Gemini settings
 
-Read and follow the official instructions in [skill.md](skill.md) and on https://moltbook.com/skill.md.
-
-High-level flow:
-
-1. Send [skill.md](skill.md) to your agent.
-2. Agent signs up and returns a claim link.
-3. You tweet to verify ownership.
-
-After claim is complete, set `MB_API_KEY` in your local `.env`.
-
-## Core Configuration Files
-
-- [configs/agent.yaml](configs/agent.yaml)
-  - Runtime and operational controls (heartbeat, limits, logging defaults).
-- [configs/submolts.yaml](configs/submolts.yaml)
-  - Per-submolt behavior and personality (persona, tone, topics, abilities, thresholds, dry-run per submolt).
-- [configs/gemini.yaml](configs/gemini.yaml)
-  - Gemini generation settings (model, temperature, top-p/top-k, function-calling, URL lookup behavior).
-
-## Quick Start
-
-1. Create and activate a Python environment.
-2. Install dependencies.
-3. Copy `.env.example` to `.env`.
-4. Set at minimum:
-   - `MB_GEMINI_API_KEY`
-   - `MB_API_KEY` (after claim), or keep `MB_AUTO_REGISTER=true` for onboarding flow.
-5. Optional: set `MB_RSS_FEED_URLS` to one or more comma-separated feed URLs.
-6. Run one dry-run cycle:
-   - `MB_RUNTIME__AUTONOMOUS_MODE=false`
-   - `MB_RUNTIME__RUN_ONCE=true`
-   - `python -m app.main`
+- Settings file: `configs/gemini.yaml`
+- Override path with `MB_GEMINI_CONFIG_PATH`
+- Tunables include model, temperature, top-p/top-k, function-calling mode, allowed functions, and URL lookups.
 
 ## Running with Docker