advanced-daily-report

You can generate a daily work report by collecting Git commit activity, email statistics, memory summaries, and todo status into a single Markdown file. This skill aggregates these multi-source signals automatically to produce a comprehensive report without manual synthesis.

AI-assisted work pattern analysis uses LLM to evaluate your productivity indicators and completion rates over the last 7 days. It produces an AI summary, suggests tomorrow’s priorities, and identifies behavioral trends based on your collected work signals.

Yes, you can generate weekly and monthly work reports with trend comparison. The skill computes productivity indicators like code change metrics and completion rates, enabling clear progress tracking across different time periods.

Yes, the daily report skill outputs a Markdown file content obtained through read_file, making it ready to share in platforms like Feishu. The generated report includes your progress, code impact, email load, and tomorrow’s plan.

Yes, required execution is via bash-runner invoking the provided Python entrypoints. This environment setup is necessary to collect work signals, run LLM analysis, and return the final report as Markdown file content.