Skill: disyli/codebuddy-daily-report (version 1.0.0)

--- name: daily-report description: > Generate a daily work report by automatically discovering all git repositories the user worked on, collecting commit logs across all branches, and summarizing CodeBuddy Agent session overviews. Supports any date via --date, --yesterday, or --days-ago flags. Use this skill when the user asks to generate a daily report, work summary, daily log, 日报, 工作日报, 今日工作总结, 昨日工作总结, or any variation of "what did I do today/yesterday". --- # Daily Work Report Generator ## Overview This skill automatically discovers all git repositories the user committed to, collects commit history across ALL branches, gathers CodeBuddy Agent session overviews, and generates a structured daily work report. It works cross-platform (macOS, Linux, Windows) with zero configuration required. Supports generating reports for **any date** (today, yesterday, or a specific date). ## Step-by-Step Workflow ### Step 1: Run the data collection script Execute the bundled Python script to collect all raw data: ```bash # Today (default) python "SKILL_DIR/scripts/collect.py" # Yesterday python "SKILL_DIR/scripts/collect.py" --yesterday # Specific date python "SKILL_DIR/scripts/collect.py" --date 2026-03-20 # N days ago python "SKILL_DIR/scripts/collect.py" --days-ago 3 ``` Replace `SKILL_DIR` with the actual path to this skill's directory (e.g., `~/.codebuddy/skills/daily-report`). **Date selection rules:** - If the user says "今天" / "today" → no extra flags (default) - If the user says "昨天" / "yesterday" → use `--yesterday` - If the user says a specific date → use `--date YYYY-MM-DD` - If the user says "前天" / "the day before yesterday" → use `--days-ago 2` - If the user says "N天前" → use `--days-ago N` The script will: - Auto-detect the operating system - Find common development directories - Discover all git repos with commits on the target date (across ALL branches) - Collect CodeBuddy Agent session overviews modified on the target date - Output structured JSON to stdout If the user has a custom config file at `SKILL_DIR/references/config.yaml`, the script will also read extra search directories and preferences from it. ### Step 2: Parse the JSON output The script outputs JSON with this structure: ```json { "date": "2026-03-25", "system": "Darwin", "git_author": "username", "repos": [ { "path": "/path/to/repo", "name": "repo-name", "commits": [ { "hash": "abc1234", "message": "feat: add login page", "branch": "feature/login", "time": "2026-03-25 14:30:00" } ], "diff_stats": "+150 -30 across 8 files" } ], "agent_sessions": [ { "session_id": "abc123", "overview_content": "...", "modified_time": "2026-03-25 16:00:00" } ], "errors": [] } ``` ### Step 3: Generate the daily report Using the collected data, generate a well-structured daily report in the user's language (default: Chinese if the user speaks Chinese, otherwise English). Follow this structure: #### Report Template ```markdown # 工作日报 - {date} ## 📊 今日概览 - 活跃仓库: {count} 个 - 总提交数: {count} 次 - 代码变更: +{additions} -{deletions} ## 🔧 项目详情 ### {repo-name-1} **分支**: {branch-names} | 时间 | 提交说明 | |------|---------| | HH:MM | commit message | **代码统计**: +{add} -{del}, {files} 个文件 ### {repo-name-2} ... ## 🤖 AI 辅助工作 {Summary of Agent session overviews, grouped by project} ## 📝 今日总结 {A 2-3 sentence high-level summary of the day's work} ``` ### Step 4: Save the report Save the generated report to the user's workspace or a location they specify. Default filename: `daily-report-{YYYY-MM-DD}.md` ## Important Notes - The script requires only Python 3.6+ standard library and git CLI — no pip install needed. - If the script finds no commits, inform the user and suggest checking if git author name is correct. - If the user wants to customize search directories, guide them to edit `references/config.yaml`. - Always present the report in the user's preferred language. - When summarizing Agent sessions, focus on what was accomplished, not implementation details. - Group related commits across repos into coherent work items when possible.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)