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Student Companion Agent

by haidong · GitHub ↗ · v0.1.0 · MIT-0
linuxdarwinwin32 ✓ Security Clean
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Install in OpenClaw
/install student-companion-agent
Description
Parent-facing student companion agent for OpenClaw and Hermes. Use when parents want to collect exam scores, homework, curriculum progress, images, voice not...
README (SKILL.md)

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 \x3Cfile> --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.
Usage Guidance
This looks reasonable for a parent-facing student tracker if you are comfortable running the bundled Python helper and storing child learning records locally. Use a protected data path, avoid unnecessary personal identifiers or passwords, and review reports before sharing them with teachers, tutors, or others.
Capability Analysis
Type: OpenClaw Skill Name: student-companion-agent Version: 0.1.0 The student-companion-agent is a legitimate educational tool designed for parents to track student progress. The core logic in 'scripts/student_companion.py' implements standard CRUD operations for managing student records (scores, homework, and progress) stored in a local JSON database (~/.local/share/student-companion-agent/). The script uses standard libraries, contains no network-reaching code, and lacks any signs of obfuscation or malicious intent. The 'SKILL.md' instructions are well-defined, focusing on educational analysis while explicitly advising the agent to minimize privacy risks and avoid sensitive data like passwords or IDs.
Capability Assessment
Purpose & Capability
The skill’s purpose—recording student scores, homework, progress, evidence, and follow-ups—is coherent with the provided README, SKILL.md, examples, and helper CLI. Because it handles child/student educational data, users should treat the stored records as sensitive.
Instruction Scope
The instructions include appropriate boundaries: confirm uncertain OCR/STT results before recording, avoid medical or psychological diagnosis, and do not request unnecessary identifiers or school account passwords.
Install Mechanism
There is no install spec or dependency installation; use is via local Python commands and optional symlinks. Running the helper is expected for the skill, but it is still local code execution from the installed skill package.
Credentials
The helper writes a local JSON database by default and can write report files to user-specified paths. This is proportionate to the tracking/reporting purpose and disclosed in the artifacts.
Persistence & Privilege
The skill persistently stores student profiles, records, evidence summaries, and follow-up actions over time. No credentials, elevated privileges, background services, or network exfiltration are shown in the provided artifacts.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install student-companion-agent
  3. After installation, invoke the skill by name or use /student-companion-agent
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release: parent-facing student companion agent for OpenClaw and Hermes with score/homework/progress collection, multimodal evidence, weak-point analysis, teaching suggestions, and follow-up tracking.
Metadata
Slug student-companion-agent
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Student Companion Agent?

Parent-facing student companion agent for OpenClaw and Hermes. Use when parents want to collect exam scores, homework, curriculum progress, images, voice not... It is an AI Agent Skill for Claude Code / OpenClaw, with 60 downloads so far.

How do I install Student Companion Agent?

Run "/install student-companion-agent" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Student Companion Agent free?

Yes, Student Companion Agent is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Student Companion Agent support?

Student Companion Agent is cross-platform and runs anywhere OpenClaw / Claude Code is available (linux, darwin, win32).

Who created Student Companion Agent?

It is built and maintained by haidong (@harrylabsj); the current version is v0.1.0.

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