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🦀 ClawHub

Student Companion Agent

by @harrylabsj

Parent-facing student companion agent for OpenClaw and Hermes. Use when parents want to collect exam scores, homework, curriculum progress, images, voice not...

Versionv0.1.0
#audio#legal#real-estate#education
TERMINAL
clawhub install student-companion-agent

📖 About This Skill

name: student-companion-agent slug: student-companion-agent version: 0.1.0 author: harrylabsj license: MIT description: "Parent-facing student companion agent for OpenClaw and Hermes. Use when parents want to collect exam scores, homework, curriculum progress, images, voice notes, or files; analyze weak knowledge points across multiple subjects; generate teaching suggestions; and track follow-up actions over time. 适用场景：家长记录孩子成绩、作业、教学进度、试卷图片、语音反馈或文件，分析多科薄弱知识点，生成教学建议并持续跟踪。" platforms: [macos, linux, windows] metadata: hermes: category: education tags: [education, parent, student, homework, exams, tracking, multimodal, 学习, 家长, 成绩, 作业] related_skills: [learning-planner] clawdbot: emoji: "🎓" requires: bins: [python3] os: [linux, darwin, win32]

Student Companion Agent

家长使用的学生陪伴 agent。目标不是替代老师，而是把零散的成绩、作业、课堂进度、语音、图片和文件变成可跟踪的学习画像。

When To Use

Use this skill when a parent asks to:

  • 记录考试成绩、测验、小题得分、作业完成情况、错题、课堂或补习进度。
  • 上传试卷图片、作业照片、老师语音反馈、成绩单文件、Excel/CSV/JSON/Markdown/TXT。
  • 识别多科目薄弱知识点，并希望获得家庭辅导建议。
  • 持续跟踪补救行动、复盘结果和下一次检查日期。

    • Operating Principles

  • 面向家长：先给一句结论，再给可执行动作；避免教育术语堆叠。
  • 证据优先：所有判断都要能追溯到成绩、作业、进度或附件提取内容。
  • 多科统一：按 subject + knowledge_point 聚合，避免只按总分判断。
  • 小步跟踪：每次建议都转成 1-3 个 follow-up 动作，带截止日期或检查点。
  • 风险边界：不要做医学、心理诊断；发现明显焦虑、厌学、霸凌、睡眠严重不足等信号时，建议家长联系老师或专业人士。
  • 隐私最小化：不要要求身份证号、详细住址、学校账号密码等不必要信息。

    • Quick Start

    The deterministic helper lives at scripts/student_companion.py.

    python3 scripts/student_companion.py init --student 小明 --grade 五年级 --goal "数学稳定 90+"
python3 scripts/student_companion.py record score --student 小明 --subject 数学 --title "期中考试" --score 78 --max-score 100 --knowledge "分数应用题,单位换算"
python3 scripts/student_companion.py record homework --student 小明 --subject 数学 --title "周三作业" --status needs_review --knowledge "分数应用题" --notes "应用题第 4、6 题错"
python3 scripts/student_companion.py analyze --student 小明
python3 scripts/student_companion.py report --student 小明 --output reports/xiaoming-weekly.md

    By default data is stored in ~/.local/share/student-companion-agent/student-data.json. Use --data /path/to/student-data.json when you need a project-local or test database.

    Parent Workflow

    1. 收集输入 - Ask for the student's name/grade if unknown. - Accept text, voice, image, spreadsheet, PDF, CSV, JSON, Markdown, or TXT. - For image or voice input, extract the visible/spoken facts first, then record both the extracted text and source path with record evidence. - For structured files, prefer import over manually rewriting rows.

    2. 标准化记录 - Scores: subject, title, date, score, max score, knowledge points. - Homework: subject, title, date, status, knowledge points, notes. - Teaching progress: subject, unit, status, knowledge points, notes. - Evidence: source type, source path, extracted text, related subject.

    3. 分析薄弱点 - Run analyze after new data is recorded. - Focus on high-severity knowledge points with repeated evidence, not one isolated low score. - Explain whether the weakness comes from scores, homework errors, unfinished progress, or teacher notes.

    4. 给教学建议 - Separate parent actions from teacher/tutor suggestions. - Keep suggestions specific: what to practice, how long, what evidence proves improvement. - Prefer short routines parents can run at home: 10-20 minutes, 2-4 times per week.

    5. 跟踪 - Convert advice into follow-up actions with followup add. - At the next check-in, compare new records against the old weak points. - Mark completed actions with followup complete only after evidence is collected.

    Multimodal Handling

    OpenClaw/Hermes may receive attachments through chat gateways, browser tools, or local files. Use this rule:

    | Input | Agent action | CLI action | | --- | --- | --- | | Voice note | Transcribe or summarize spoken facts. Include uncertainty. | record evidence --source-type audio --extracted-text ... | | Test paper image | Extract subject, score, question errors, and visible knowledge points. | record evidence --source-type image ..., then record score if score is reliable | | Homework photo | Extract completion status, wrong questions, teacher marks. | record homework plus optional record evidence | | CSV/JSON | Use structured import. | import --student ... | | PDF/DOC/XLSX | Extract or convert relevant tables/text first. | record evidence or generated CSV/JSON import |

    Do not pretend OCR/STT is exact. If text is unclear, say what is uncertain and ask for confirmation before recording a score.

    CLI Reference

    # profile
python3 scripts/student_companion.py init --student NAME [--grade GRADE] [--school SCHOOL] [--goal GOAL]
python3 scripts/student_companion.py --data ./student-data.json init --student NAME
python3 scripts/student_companion.py status --student NAME
    record facts
    
python3 scripts/student_companion.py record score --student NAME --subject SUBJECT --title TITLE --score N --max-score N --knowledge "A,B"
python3 scripts/student_companion.py record homework --student NAME --subject SUBJECT --title TITLE --status completed|needs_review|missing|late --knowledge "A,B"
python3 scripts/student_companion.py record progress --student NAME --subject SUBJECT --unit UNIT --status not_started|learning|blocked|reviewing|mastered --knowledge "A,B"
python3 scripts/student_companion.py record evidence --student NAME --source-type image|audio|file|text --source-path PATH --extracted-text TEXT
    bulk import
    
python3 scripts/student_companion.py import examples/sample_records.csv --student NAME
python3 scripts/student_companion.py import examples/sample_records.json --student NAME
    analysis and reporting
    
python3 scripts/student_companion.py analyze --student NAME [--days 30] [--format markdown|json]
python3 scripts/student_companion.py report --student NAME --output weekly.md
    tracking
    
python3 scripts/student_companion.py followup add --student NAME --subject SUBJECT --knowledge "知识点" --action "本周练 10 道分数应用题" --due 2026-05-09
python3 scripts/student_companion.py followup list --student NAME
python3 scripts/student_companion.py followup complete --student NAME --id 1

    Output Expectations

    A useful answer to the parent should include:

  • 当前判断：1-2 句说明最需要关注的科目/知识点。
  • 证据：引用最近成绩、作业或进度记录。
  • 家庭动作：1-3 条可执行建议。
  • 跟踪点：下次看什么数据，什么时候检查。
  • 需要补充：只问最关键的缺口，例如“这张试卷满分是多少？”

    • Verification

    Before claiming the agent package is ready:

  • python3 scripts/student_companion.py --help
  • python3 -m unittest discover -s tests
  • bash scripts/verify.sh
  • Confirm SKILL.md frontmatter has name and description.
  • Confirm clawhub.json, package.json, and README.md point to the same package name and version.

    • ⚡ When to Use

    TriggerAction
    - 记录考试成绩、测验、小题得分、作业完成情况、错题、课堂或补习进度。
    - 上传试卷图片、作业照片、老师语音反馈、成绩单文件、Excel/CSV/JSON/Markdown/TXT。
    - 识别多科目薄弱知识点，并希望获得家庭辅导建议。
    - 持续跟踪补救行动、复盘结果和下一次检查日期。

    💡 Examples

    The deterministic helper lives at scripts/student_companion.py.

    python3 scripts/student_companion.py init --student 小明 --grade 五年级 --goal "数学稳定 90+"
python3 scripts/student_companion.py record score --student 小明 --subject 数学 --title "期中考试" --score 78 --max-score 100 --knowledge "分数应用题,单位换算"
python3 scripts/student_companion.py record homework --student 小明 --subject 数学 --title "周三作业" --status needs_review --knowledge "分数应用题" --notes "应用题第 4、6 题错"
python3 scripts/student_companion.py analyze --student 小明
python3 scripts/student_companion.py report --student 小明 --output reports/xiaoming-weekly.md

    By default data is stored in ~/.local/share/student-companion-agent/student-data.json. Use --data /path/to/student-data.json when you need a project-local or test database.