What makes Zhizhi Math Coach notable?

1.6K downloads. Trust evidence available

How should Zhizhi Math Coach be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Zhizhi Math Coach

Grade math papers, explain mistakes, and track learning Skill: Zhizhi Math Coach Owner: linzi007 Summary: Grade math papers, explain mistakes, and track learning Tags: china:0.3.1, education:0.3.1, latest:0.3.1, math:0.3.1, parenting:0.3.1, worksheet:0.3.1 Version history: v0.3.1 | 2026-10-01T08:59:35.622Z | user Clarify integration with @linzi007/zhizhi-math-worksheet: same-agent handoff of selected questions, scope and language preferences; return PDFs, source mappings

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.6k

Updated

Oct 10, 2026

Version

0.3.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.6K downloadsadoption · observed Oct 10, 2026
Latest release: 0.3.1release · observed Oct 1, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17eh48qh47mdh81192axy3h3986z20y:zhizhi-math-coach

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-linzi007-zhizhi-math-coach/snapshot"

Documentation

CLAWHUB

160,000 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: zhizhi-math-coach
description: "Primary-school math coaching: grade worksheet photos, document full papers, track evidence-based knowledge mastery, explain mistakes and plan follow-up practice. Use for grading, learning records and diagnosis; standalone A4 paper variants and mistake-focused worksheets use zhizhi-math-worksheet when available."
---

# Zhizhi Math Coach

## Language And Regional Scope

Reply in the user's language unless they specify another. Follow explicit worksheet and answer-key language preferences; otherwise preserve the source questions' language for student materials and use the conversation language for parent explanations. Localize generated headings and directions, not just the questions. Do not produce bilingual pages unless requested.

Language does not establish a country's grade levels, school dates, textbook, currency, units, or timezone. Apply the China-specific defaults below only to a confirmed Chinese-school context. For other systems, use the supplied curriculum and school calendar; ask only for missing details that materially affect the task. Never convert grade names across systems by guessing.

The bundled archive initializer, record summaries, and older fixed question templates still contain Chinese labels and China-specific defaults. They are not fully localized. For English worksheets, prefer the independent worksheet skill or model-authored HTML with `language: en`; the latter localizes its generated answer-key headings. For non-China learning archives, create/adapt the relevant profile and calendar from the user's context instead of running the China-default initializer unchanged. Do not advertise complete international curriculum or archive localization.

## Skill Boundary

`zhizhi-math-coach` owns grading, explanations, full-paper archives, knowledge assessment and configured background/sync work. The independent `zhizhi-math-worksheet` skill owns paper design, A4 layout, separate printable answers and PDF preview checks.

For a request that only asks for a paper, use the installed `zhizhi-math-worksheet` instructions when available, before the learning-record loop below. Do not require initializing a learning workspace just to make a variant from a supplied photo. If that skill is unavailable, the local generation references and scripts remain a compatibility path; do not claim to have invoked an unavailable skill.

For grading followed by practice, complete the requested diagnosis, select relevant questions, then pass a question list with complete stems, options and necessary diagram descriptions. Include source IDs; add confirmed answers, observed mistakes, cause hypotheses with confidence, learned scope and requested length when useful. Specify `full_paper` for a whole-paper photo variant, or `question_list` for selected questions from any source. Mistakes and weak points guide upstream selection; diagnosis is optional input to generation. The worksheet skill returns local artifacts and

_meta.json

{
  "ownerId": "kn7cxpw3e2e3tg37c248mvktxh86y35a",
  "slug": "zhizhi-math-coach",
  "version": "0.3.1",
  "publishedAt": 1790845175622
}

references/automation-openclaw.md

# OpenClaw Automation

## Boundary

Scheduled tasks should default to reminders and suggestions. They should not automatically change weak-point status, memory, records, or generate new worksheets unless the parent has explicitly requested that behavior.

OpenClaw cron jobs are not declared by a skill manifest at install time. Use the bundled setup script after the parent explicitly enables scheduled reminders. The script detects whether `openclaw cron` is available; if not, it prints the exact commands instead of failing the learning workflow.

```bash
python3 {baseDir}/scripts/setup_scheduled_tasks.py \
--workspace <personal-learning-workspace> \
--enable-config \
--auto-register \
--timezone Asia/Shanghai
```

The setup writes `.zhizhi-math-coach/config.json`:

- `automation.enabled`: scheduled reminders are allowed.
- `automation.auto_register_when_supported`: register via `openclaw cron` when the CLI exists.
- `automation.timezone`: IANA timezone used by `openclaw cron --tz`; use the parent's local timezone, for example `Asia/Shanghai`.
- `automation.allow_record_writes`: default `false`.
- `automation.allow_auto_worksheet_generation`: default `false`.

## Recommended Schedule

- Opt-in photo archival: every 5 minutes, claim at most one queued photo batch in an isolated multimodal session; see `photo-intake.md`.
- Daily 20:30 local time: due review reminders, pending upload reminders, and stale short-term observations.
- Sunday 20:00 local time: weekly progress review and next-week suggestions.
- End of semester: generate a summary and holiday review pool.
- Winter/summer break: weekly holiday review suggestions.

## Suggested Task Outputs

- due weak points;
- pending worksheets not yet graded;
- short explanation cards that may help parents;
- suggested next worksheet strategy;
- warnings about low-confidence or missing evidence.

## Channels

The learning logic should not depend on a channel. Start with local records and OpenClaw conversation output.

For push delivery, use a channel adapter. Feishu/Lark is the default v1 recommendation when available because it supports chat, files, and operational workflows. DingTalk can be added later through a channel/plugin adapter if the environment supports it.

## Safety

- Do not send sensitive student files to public channels.
- Do not push full answer keys into a child-facing chat.
- Do not infer new mastery status from time alone.
- Do not schedule automatic worksheet generation by default.
- Do not auto-create cron jobs merely because the skill was installed; require an explicit setup trigger or existing automation config.

## Photo Archive Worker

```bash
python3 {baseDir}/scripts/setup_scheduled_tasks.py \
--workspace <personal-learning-workspace> \
--enable-config --photo-worker --auto-register --timezone Asia/Shanghai
```

`--photo-worker` sets `automation.allow_photo_archive: true` and adds a `*/5 * * * *` isolated worker. Its write authorization applies only to explicitly qu

references/complex-problem-generation.md

# Complex Problem Generation

## Purpose

Use this reference for multi-step word problems, condition filtering, compare-after-intermediate problems, and exam review items.

## Generation Rule

AI may design the item, but the worksheet spec must preserve the structure:

- problem type;
- known quantities;
- unknown quantity;
- required conditions;
- distractor or unused conditions;
- intermediate quantity;
- final operation;
- answer sentence;
- `answer_detail` with the full solving path.

## Review Status

Use one of:

- `draft`: not ready to print.
- `model_reviewed`: reviewed by another model or a second pass.
- `human_review_needed`: print only after parent/teacher confirmation.
- `approved`: ready to print.

Complex items should not be printed when `review_status` is `draft` or missing.

## Difficulty Control

If the target is reading or modeling, keep arithmetic easy enough not to hide the diagnosis.

Change one or two dimensions at a time:

- scenario;
- final question wording;
- condition order;
- distractor condition;
- intermediate quantity;
- calculation load.

## Answer Key

For multi-step items, `answer_detail` must show:

- each intermediate value;
- why a condition is used or ignored;
- the final equation or comparison;
- the final answer sentence and unit.

references/curriculum-alignment.md

# Curriculum Alignment

## Purpose

Use curriculum files to keep grading, explanations, and worksheets aligned with the student's real school scope.

This project may reference external textbook indexes or local PDF paths in a personal learning workspace, but the public skill must not include textbook PDFs, screenshots, scans, OCR dumps, or copied problem sets.

## Workspace Files

- `curriculum/profile.md`: student grade, textbook edition, textbook volume, source references, and current unit.
- `curriculum/scope.md`: unit and knowledge-point map.
- `curriculum/progress.md`: learned/not-yet-learned topics, school progress, and exam scope.
- `curriculum/school-calendar.md`: school-year, semester, exam, and holiday windows.

## China Textbook Example

For a first-grade student using 人教版 mathematics, the personal profile can cite:

```text
Textbook source: https://github.com/TapXWorld/ChinaTextbook/tree/master/小学/数学/人教版
Textbook volume: 一年级下册
```

Use that source to identify grade/volume and broad unit scope. Do not copy full textbook pages or exercises into the public repository.

## Scope Rules

- Daily practice should stay within learned content unless the parent explicitly asks for preview.
- Midterm review should cover this semester's learned units and actual mistakes.
- Final review should cover the whole semester, weighted by weak points and relapse history.
- Winter break should repair first-semester weak points before previewing next semester.
- Summer break should review the whole school year before previewing the next grade.

## Output Rules

When using curriculum context, state:

- grade and semester;
- textbook edition and volume;
- current unit or scope;
- whether the item is current review, remedial, transfer check, exam review, or preview.

Generate original diagnostic and practice items. Use textbook terms and scope, not copied textbook text.

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