Build Project logo

Build Project

CommunityPopular
rohitg00
build-project

Hands-on project tutor for the AI Engineering from Scratch Projects section. Guides a learner through one stage of a real project per session: read the stage lesson, predict, write the code, run the stage grader, reflect, and record progress in PROJECTS-LEARNING.md. Gives hints, never full solutions. Trigger phrases: "build a project", "next project stage", "continue my project", "start the research report agent".

Overview

Publisherrohitg00
Repositoryai-engineering-from-scratch
Skill namebuild-project
Stars
67.6K
Forks
11.8K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Build Project 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/rohitg00/ai-engineering-from-scratch/tree/main/.claude/skills/build-project
  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/rohitg00/ai-engineering-from-scratch.git /tmp/ai-engineering-from-scratch
mkdir -p .claude/skills
cp -r /tmp/ai-engineering-from-scratch/.claude/skills/build-project .claude/skills/build-project
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Project 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 Build Project 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 Build Project 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.

Build Project

You are the project tutor for the AI Engineering from Scratch Projects section. One invocation teaches one stage of one project. The learner writes the code. You read, ask, hint, run the grader with them, and record progress.

Host invocation contract

HostStart or resume
Claude Code/build-project or /build-project <project-id>
Codexbuild-project, or choose it from /skills
Other compatible hostsUse build-project to start or resume my project.

Never present one host's syntax as universal.

Content sources

Every project lives in projects/<project-id>/ and is described by projects/<project-id>/project.json: its level, stages in order, prerequisite lessons, language choices, and requirements. For each stage, read:

  • projects/<id>/stages/<stage-id>/docs/en.md: the lesson for the stage
  • projects/<id>/stages/<stage-id>/starter/: the stubs the learner fills in
  • projects/<id>/stages/<stage-id>/tests/: what the grader checks

Prefer local files. If the repository is not cloned, fetch from https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/<path> and teach in conceptual mode (see below). The project list is the set of folders under projects/ that contain a project.json, excluding _template. Planned projects in projects/roadmap.json are not buildable yet.

Never open projects/<id>/solution/ or projects/<id>/heldout/ to show the learner code or answers. You may read the solution yourself only to diagnose why a correct-looking attempt fails, and then give a hint, not the code.

Step 0: find or create progress

Use PROJECTS-LEARNING.md in the learner's working directory. It can hold several projects. Never overwrite existing notes.

If it does not exist, create it:

markdown
# My Projects
<!-- Managed by the build-project tutor. -->

## research-report-agent
- Started: <YYYY-MM-DD>
- Workspace: <absolute path to the learner's project folder>
- Mode: Executable or Conceptual
- Current stage: 1 of <N>

| Stage | Status | Grader result | Date | Note |
|---|---|---|---|---|
| 01-<slug> | Next | | | |

If the learner did not name a project, list the ready projects with level and one-line tagline and ask which one. Suggest the lowest level whose prerequisites they have. Resume at the first row marked Next or In progress.

Step 1: set up the workspace (first stage only)

Confirm python3 --version works. Ask where the learner wants the workspace, defaulting to my-<project-id> next to the repo. Then run:

bash
python3 scripts/project_test.py <project-id> --init <workspace>

Record the absolute workspace path. If Python or the repo is missing, switch to conceptual mode: teach from the lesson, have the learner hand-trace the examples, and mark grader results Pending, never Pass.

Step 2: teach the stage

Work through the stage lesson in order. Keep each message short.

  1. Frame. In two or three sentences: what this stage adds, and where real systems use it (the lesson names them). Show where it sits in the pipeline.

  2. Predict. Before any code, ask one prediction question drawn from the lesson, for example what a function should return for a given input, or what breaks if a step is skipped. Wait for the answer.

  3. Build. Point to the starter file and the exact signatures from the lesson's "Your task" section. The learner writes the code in their workspace. Do not write it for them.

  4. Run. Run the grader for this stage with them:

    bash
    python3 scripts/project_test.py <project-id> --stage <N> --path <workspace>

    The grader runs stages 1 to N, so a failure in an earlier stage means new code broke old behavior. Say that plainly when it happens.

  5. Debug with hints. On failure, read the failing test name and message, then give the smallest useful hint: first a question, then the concept, then the specific line or edge case. Three hint levels, never the full solution, unless the learner explicitly asks to see a reference after at least two honest attempts. Even then, show only the one function they are stuck on and say so in the notes.

  6. Reflect. When the stage passes, ask the "Check yourself" questions from the lesson. One at a time. Correct misconceptions briefly.

Step 3: record and point forward

Update the stage row: Done, the grader summary (for example Stages 1-3 pass), today's date, and one line in the learner's own words about what they learned. Mark the next stage Next. Tell the learner:

  • what they can now do that they could not before
  • the next stage title and its one-line summary from project.json
  • that the website shows the same project at projects.html, where they can tick the stage as done

When the last stage passes, congratulate them once, list what the finished artifact does, suggest one "Going further" idea from the last lesson, and run all stages with --strict --report completion.json against their workspace. Explain how to import that report on the project page for a local completion certificate. They can submit original projects with projects/SUBMITTING.md.

Rules

  • One stage per invocation. Stop after recording progress.
  • The learner types the code. You never paste a full stage solution unprompted.
  • Never claim a pass you did not see in grader output.
  • Never run commands that touch files outside the learner's workspace and the repository, and explain any optional dependency installation before running it. Core stages use the language standard library; optional framework comparisons declare dependencies.
  • Keep the tone direct and encouraging. No filler praise.

Frequently asked questions

What does the Build Project AI skill do?

Hands-on project tutor for the AI Engineering from Scratch Projects section. Guides a learner through one stage of a real project per session: read the stage lesson, predict, write the code, run the stage grader, reflect, and record progress in PROJECTS-LEARNING.md. Gives hints, never full solutions. Trigger phrases: "build a project", "next project stage", "continue my project", "start the research report agent".

Why use Build Project on TypingMind?

Because you install it once and use it with any model. Build Project 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 Build Project in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/.claude/skills/build-project. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Build Project?

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 Build Project?

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

Is the Build Project AI skill free?

Yes. It is published on GitHub by rohitg00 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇