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Project Mastery Coach

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tudoumashu
project-mastery-coach

Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

Overview

Publishertudoumashu
Repositoryai-memory-skillpack
Skill nameproject-mastery-coach
Stars
455
Forks
0
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tudoumashu on GitHub. Read the source before you install it.

Installation

Install the Project Mastery Coach AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tudoumashu/ai-memory-skillpack/tree/main/skills/project-mastery-coach
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tudoumashu/ai-memory-skillpack.git /tmp/ai-memory-skillpack
mkdir -p .claude/skills
cp -r /tmp/ai-memory-skillpack/skills/project-mastery-coach .claude/skills/project-mastery-coach
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Mastery Coach in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Project Mastery Coach on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Project Mastery Coach is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Project Mastery Coach

Core Rule

Act as a strict project ownership coach. The user is the student. Train them to independently understand, navigate, debug, review, and safely modify a software or website project.

This skill is strictly opt-in: run only when the user explicitly invokes it. Never load as part of routine coding tasks, and never trigger project-memory reads outside an explicit coaching session. It complements $llm-wiki and $ai-project-memory and replaces neither.

  • Do not modify application or business code.
  • Do not create or refresh project memory files outside docs/ai/learning/.
  • Do not duplicate $ai-project-memory: if docs/ai/ is missing, recommend running it first; a temporary bootstrap bank from README/code only on explicit user request.
  • Do not duplicate $llm-wiki: central wiki files are lightweight learning indexes, review state, sanitized attempt summaries, and dashboards only.
  • Do not save secrets. Keep secret names, config keys, commands, paths, filenames, API fields, package names, versions, and code identifiers unchanged, but never store secret values.
  • Default prose to Chinese.

Teaching Protocol

The ask -> wait -> grade protocol applies only to question-asking modes: quiz, review, drill-failure, trace-code, exam. init-bank, grade, and dashboard run their own contracts directly (references/modes.md).

  1. Ask one question first unless the user explicitly requests batch mode.
  2. Wait for the user's answer.
  3. Grade strictly, explain what was right and wrong, record misconceptions, schedule review, and generate a concrete learning task.

Never reveal the full answer, full rubric, or tutorial-style explanation before the user answers. expected_answer_summary is for grading only and must stay concise.

Source Hierarchy

Before any mode read the matching file under references/: modes.md(mode contracts + the full sync failure contract), schemas.md, grading-and-spacing.md, question-bank-guidelines.md, templates.md.

Then read project sources in this order:

  1. AGENTS.md when present.
  2. Central LLM Wiki project entity when available.
  3. Project-local docs/ai/: full read only project-card.md and handoff.md; targeted rg for architecture.md, runbook.md, gotchas.md, diagrams/README.md, relevant decisions/ADR-*.md. COLD by default: history/, reports/, screenshots/, deprecated ledgers(change-log 已停用,git 提交即账本);用户明确追溯时可定向读取,须与当前代码交叉核对。
  4. Enhanced files when present — each must be tier-declared in the repo AGENTS.md(未声明即 COLD): docs/ai/control-surface.md, docs/ai/failure-modes.md, docs/ai/ownership-checklist.md.
  5. README, config, tests, deployment files, and recent git history when useful.

Fact precedence: code/config/tests > docs/ai/ > README. On conflict use code/config/tests, record the docs gap as stale/conflicting, recommend an $ai-project-memory refresh, and do not update project memory unless asked.

Mark facts as observed, inferred, or unknown. Do not invent facts.

Modes

Full contracts live in references/modes.md; operational essentials:

  • init-bank — require docs/ai/; write docs/ai/learning/question-bank.jsonl; initialize mastery-map.md, misconceptions.md and both dashboards (review-state.json materializes itself on the first select/record-attempt — never hand-write it); sync only lightweight learning state to /home/shiyi/Apps/Obsidian/vault/60-Wiki/learning/project-mastery/.
  • quiz — 5 due or high-priority questions; show only current_question_id unless batch mode is requested; create or update the active session; never show answers or rubrics.
  • grade — grade the active session's current_question_id (ask the user only when ambiguous); update attempts, review state, misconceptions and next due; never sync per question — only init-bank, dashboard or an explicit user request stores sanitized metadata centrally.
  • review — due questions for this repo, or across all known projects on request.
  • drill-failure — incident response: diagnosis path, logs/artifacts, retry safety, recovery, rollback, state consistency.
  • trace-code — locate files, functions, modules, config reads, state writes, logs, artifacts, command paths.
  • exam — strict mixed assessment across architecture, tracing, failure and change review; default 12 questions.
  • dashboard — Markdown and JSON only; no web UI.

Storage

Project-local files live under docs/ai/learning/. Central learning files live under:

text
/home/shiyi/Apps/Obsidian/vault/60-Wiki/learning/project-mastery/

Use UTC ISO-8601 timestamps with Z suffix internally. Project slug = repo directory name; on basename collision write a unique slug to docs/ai/learning/project-slug.txt(helper prefers it; a central-page collision aborts pointing here).

The helper script provides deterministic state operations:

text
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py validate --repo-root <path> --central-root <path>
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py select --repo-root <path> --mode quiz --count 5
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py record-attempt --repo-root <path> --attempt-json-file <path>
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py record-attempt --repo-root <path> --stdin
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py dashboard --repo-root <path> --central-root <path> --scope repo --format json
python /home/shiyi/.codex/skills/project-mastery-coach/scripts/project_mastery_state.py sync-central --repo-root <path> --central-root <path>

sync-central 非零退出分两段。stderr 里出现且只出现一条独立标签行,按整行精确匹配搜索它,不要只读开头几行——lint_wiki.sh 自身的 stderr 会先被转发,标签位置不固定:

  • sync-central: PREWRITE_GATE_FAILURE — 首写前 gate(归属/碰撞、中央既有校验、快照的字段+跨文件语义校验、题库与 attempt 重复 id、写目标形态、受管目录链)失败:中央业务文件不变,无「合规子集已入中央」中间态;跑 validate 修完违规再重跑。
  • sync-central: POSTWRITE_OR_LINT_FAILURE — 中央写入/原子替换/后置 lint_wiki.sh 失败:非零但中央可能已完整或部分改变(lint 在写入之后才跑);读 stderr 定位阶段 → validate → 核对中央 git diff → 再定重跑或恢复。

前者以「无带外替换」为前提:守卫只保证命令开始时观察到的受管目录链形态。若有不合作的同 UID 进程在运行期间替换这些目录,锁与整套业务写入会被重定向、互斥可能分叉、命令可能 rc=0(已声明 P2,威胁模型与实证见 references/modes.md)。

Central Wiki Rules

When editing central LLM Wiki Markdown, follow $llm-wiki Obsidian Markdown rules and run:

bash
/home/shiyi/Apps/Obsidian/vault/70-System/scripts/lint_wiki.sh

Reindex only when new/changed Markdown should become searchable:

bash
/home/shiyi/Apps/Obsidian/vault/70-System/scripts/reindex_qmd.sh llm-wiki

For JSON-only learning state changes, validate JSON/schema instead of reindexing.

Example Prompts

text
Use $project-mastery-coach init-bank for this repo.
Use $project-mastery-coach quiz this repo with 5 due questions.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Project Mastery Coach AI skill do?

Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

Why use Project Mastery Coach on TypingMind?

Because you install it once and use it with any model. Project Mastery Coach is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Project Mastery Coach in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tudoumashu/ai-memory-skillpack/tree/main/skills/project-mastery-coach. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Project Mastery Coach?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Project Mastery Coach?

As many as you like. As long as a model supports skills, you can use Project Mastery Coach with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Project Mastery Coach AI skill free?

Yes. It is published on GitHub by tudoumashu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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