openstudent-claw

This repository contains the BSI Student automation logic and is designed to run as a managed skill within the OpenClaw framework.

Quick Start (Docker)

The project uses Docker to provide a consistent environment with OpenClaw and Bun pre-installed.

1. Configure Environment

Create or update your Turso database credentials in a .env file (or pass them directly to compose):

TURSO_URL=libsql://your-db.turso.io
TURSO_TOKEN=your-token

2. Start the Gateway

Launch the OpenClaw gateway using Docker Compose. The repository is mounted as a volume, and the bsi_students skill is automatically mirrored into the OpenClaw workspace at startup.

docker compose up -d

Gateway Mode Fix: If startup logs show gateway.mode=local (current: unset) and the gateway fails to start, run:

docker exec -it openstudent-claw-openclaw openclaw config set gateway.mode local

Note: If you modify the startup script in docker/openclaw-entrypoint.sh, you must rebuild the image to apply changes:

docker compose up -d --build --force-recreate openclaw

3. Authenticate with OpenAI Codex

This setup uses OpenAI Codex for model operations. Authenticate using the OAuth flow:

docker exec -it openstudent-claw-openclaw openclaw models auth login --provider openai-codex

Follow the interactive prompts to complete the browser-based authentication.

If openclaw models status still shows a different default model (like Anthropic), set Codex as the default:

docker exec -it openstudent-claw-openclaw openclaw models set openai-codex/gpt-5.4

4. Verify Setup

Check the status of your models and skills:

# Check model connectivity
docker exec -it openstudent-claw-openclaw openclaw models status

# List available skills (verify 'bsi_students' is present)
docker exec -it openstudent-claw-openclaw openclaw skills list

Repository Structure

• src/lib/: Core logic for BSI integration, parsing, and database access.
• src/scripts/students/: CLI entry points for student-specific tasks (login, schedule, etc.).
• SKILL.md: Definition for the bsi_students OpenClaw skill.
• .agents/skills/: Additional specialized skills available to the agent.
• docker/: Container configuration and entrypoint logic.

Skill Integration

At container startup, the entrypoint script mirrors the repository into the OpenClaw workspace at /home/node/.openclaw/workspace/skills/bsi_students. It automatically runs bun install within that mirrored directory to ensure all dependencies are ready for use by the agent.

Telegram Bot Setup

To use the Telegram channel, add it via the OpenClaw CLI. This stores the configuration in the persistent OpenClaw volume:

docker compose run --rm openclaw openclaw channels add --channel telegram --token "<TELEGRAM_BOT_TOKEN>"

2. Pair with the Bot

After adding the channel, message your bot on Telegram. OpenClaw will generate a pairing code and wait for approval. To approve the connection, run:

docker exec -it openstudent-claw-openclaw openclaw pairing approve telegram <PAIRING_CODE>

Example: docker exec -it openstudent-claw-openclaw openclaw pairing approve telegram xxxx

3. Verify Telegram Setup

After adding and pairing the channel, verify that it's correctly configured and active:

# List all channels to see the Telegram entry
docker exec -it openstudent-claw-openclaw openclaw channels list

# Check the status of the Telegram channel
docker exec -it openstudent-claw-openclaw openclaw channels status --channel telegram --probe

# View logs to ensure there are no connection errors
docker exec -it openstudent-claw-openclaw openclaw channels logs --channel telegram