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3.15.3 Automation Scenario Examples

This section provides three typical automation scenarios demonstrating CLI usage in CI, scheduled tasks, and log analysis. Each example can be directly copied into production environments.

Running Code Reviews in CI​

Use @dmoss/agent to review PR diffs within a GitHub Actions workflow:

# .github/workflows/review.yml
name: AI Code Review

on:
  pull_request:
    branches: [main]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - uses: actions/setup-node@v4
        with:
          node-version: '22'

      - name: Install @dmoss/agent
        run: npm install -g @dmoss/agent

      - name: Review diff
        env:
          DMOSS_API_KEY: ${{ secrets.DMOSS_API_KEY }}
          DMOSS_MODEL: claude-sonnet-4-20250514
          DMOSS_BASE_URL: https://api.anthropic.com
        run: |
          git diff origin/main HEAD | dmoss-agent --pipe \
            "review the diff, focus on bugs and missing tests"

The Agent analyzes the diff, points out potential bugs, and suggests adding tests. The output appears in the Action logs.

Scheduled On-Device Health Checks​

Use rdkstudio in a crontab to periodically check the health of production devices:

# /etc/cron.d/rdk-checkin
0 * * * * rdkstudio exec "df -h /; free -h; uptime" --device prod-x5-01 \
  | tee -a /var/log/rdk-checkin.log

This runs hourly, appending disk usage, memory usage, and uptime information to the log file. Regularly reviewing this log helps identify abnormal trends.

Going further, use @dmoss/agent to let AI automatically analyze inspection results:

# Trigger a full diagnostic once daily at midnight
0 0 * * * bash /opt/rdk/daily-health.sh
#!/bin/bash
# /opt/rdk/daily-health.sh
set -e

DIAGNOSIS=$(rdkstudio exec "cat /proc/meminfo; df -h; dmesg | tail -100" --device prod-x5-01)

echo "$DIAGNOSIS" | dmoss-agent --pipe \
  "Analyze the following device status output and provide a health score and recommendations" \
  > /var/log/rdk-daily-report.log

AI-Powered Log Summarization​

Hand systemd logs to AI for summarizing anomalies:

# Execute manually or via scheduled job
journalctl -u myservice --since="1 hour ago" \
  | dmoss-agent --pipe "summarize errors and root causes"

The Agent reads the logs, extracts error events, categorizes them by time and severity, and suggests possible root causes—ideal for quickly pinpointing issues amid large volumes of logs.

Extension: Let AI proactively notify you when critical issues are detected:

LOG=$(journalctl -u myservice --since="5m ago" --no-pager)

RESULT=$(echo "$LOG" | dmoss-agent --pipe --json \
  "Analyze whether the logs contain severe errors. Return JSON: {severity: none|low|medium|high, summary: '...'}")

SEVERITY=$(echo "$RESULT" | jq -r '.severity')

if [ "$SEVERITY" = "high" ]; then
  # Trigger alert channel
  curl -X POST https://your-alert-endpoint \
    -d "$(echo "$RESULT" | jq -r '.summary')"
fi

Deployment in Docker Images​

Package @dmoss/agent into a Docker image for containerized Agent services:

FROM node:22-alpine

RUN npm install -g @dmoss/agent

ENV DMOSS_WORKSPACE=/app
WORKDIR /app

ENTRYPOINT ["dmoss-agent"]

Build and run:

docker build -t my-dmoss-agent .

docker run -it --rm \
  -e DMOSS_API_KEY=sk-xxxx \
  -e DMOSS_MODEL=qwen3.6-plus \
  my-dmoss-agent "Help me analyze the structure of the current working directory"

This approach is well-suited for deploying short-lived Agent tasks in Kubernetes or providing a consistent Agent runtime environment in CI/CD pipelines.

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