# How to Install and Use Reusable Skill Artifacts in ai-engineering-from-scratch > Install reusable skill artifacts in ai-engineering-from-scratch using the install_skills.py script. Copy Markdown files and manifest.json to create AI pipelines. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: how-to-guide - Published: 2026-06-05 --- **Run the [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) installer to discover, filter, and copy skill Markdown files into any target directory with a generated [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json), then consume each artifact as a plain-text prompt or component in your own AI pipeline.** The `rohitg00/ai-engineering-from-scratch` curriculum distributes **reusable skill artifacts** across lesson `outputs/` folders. These self-contained Markdown files embed prompts, agents, and other AI-engineered components that you can pull into external projects without any external dependencies. The helper script in [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) automates discovery, metadata extraction, and installation so you can start using the components immediately. ## How the Skill Installer Works The installer is a pure-Python pipeline defined in [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) that runs on any **Python 3.10+** environment. It processes artifacts in six discrete stages. ### Discovery and Metadata Parsing `discover_artifacts()` recursively walks `phases/**/outputs` and identifies every Markdown file matching `skill-…`, `prompt-…`, or `agent-…`. For each match, the script constructs an `Artifact` dataclass and uses the lightweight YAML-subset parser in [`scripts/_lib.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/_lib.py) to read front-matter fields such as `name`, `description`, `version`, `tags`, and `phase`. ### Filtering and Planning `filter_artifacts()` narrows the list based on user-supplied `--type`, `--phase`, and `--tag` arguments. Then `build_plan()` maps each artifact to a final destination according to the chosen layout—`flat`, `by-phase`, or `skills`—and flags any name collisions before a single file is written. ### Installation and Manifest Generation `apply_plan()` copies the source files to their target paths, while `write_manifest()` emits a comprehensive [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) that records the source path, target path, and metadata for every installed artifact. ## Installing Reusable Skill Artifacts You can install the entire collection or a scoped subset in one command. ### Basic Installation Clone the repository and run the installer with a target directory and layout. The `skills` layout is the most common choice for direct consumption because it creates one folder per skill containing a [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md) file. ```bash git clone https://github.com/rohitg00/ai-engineering-from-scratch.git cd ai-engineering-from-scratch python3 scripts/install_skills.py ~/my-skills \ --type skill \ --layout skills ``` This discovers all `skill-*.md` files, copies each to `//SKILL.md`, and writes [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) in `~/my-skills`. ### Filtering by Phase or Tag Limit the installation to a specific curriculum phase or metadata tag without manually searching the folders. ```bash # Only skills from phase 04 (Computer Vision) python3 scripts/install_skills.py ~/my-skills --phase 4 # Only skills tagged "safety" python3 scripts/install_skills.py ~/my-skills --tag safety ``` ### Previewing and Overwriting Use `--dry-run` to preview every planned copy and manifest entry before any disk changes occur. ```bash python3 scripts/install_skills.py ~/my-skills --dry-run ``` By default, the script aborts if a target file already exists. Pass `--force` to overwrite existing files safely. ```bash python3 scripts/install_skills.py ~/my-skills --force ``` ## Using an Installed Skill Artifact Once installed, each artifact is a plain Markdown file that follows the curriculum's **skill contract**. You can version-control, diff, or edit these files like any other source component before loading them into your own AI pipelines. ### The Skill Contract and Front-Matter A typical skill begins with YAML-style front-matter followed by a concrete artifact body. For example, the *End-to-End Safety Gate* skill opens with: ```markdown --- name: End-to-End Safety Gate description: A reusable safety-gate component that intercepts LLM outputs before they reach the user. version: 1.0 tags: [safety, gate, policy] phase: 19 lesson: 87 --- ``` The body contains the usable payload—such as a prompt template, JSON schema, or Python snippet—that you can lift directly into your application. ### Loading Skills in Python Because the files are plain text, you can read and parse them with standard library tools. ```python from pathlib import Path skill_path = Path("~/my-skills/End-to-End Safety Gate/SKILL.md").expanduser() skill_md = skill_path.read_text() # Isolate the body after the front-matter delimiters _, _, body = skill_md.partition("---\n---\n") prompt = body.strip() # Supply `prompt` to your LLM API or orchestration layer ``` ### Shell-Based Workflows For command-line pipelines, stream a skill file directly into a tool that accepts prompts on standard input. ```bash cat ~/my-skills/End-to-End\ Safety\ Gate/SKILL.md | my-llm-cli --prompt - ``` Because the artifacts are version-controllable Markdown, you can diff, edit, and track them like any other source file. ## Key Project Files The following files implement and define the artifact pipeline in `rohitg00/ai-engineering-from-scratch`: - **[`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py)** — The main installer script that orchestrates `discover_artifacts()`, `filter_artifacts()`, `build_plan()`, `apply_plan()`, and `write_manifest()`. - **[`scripts/_lib.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/_lib.py)** — Minimal YAML-subset front-matter parser used by the installer to extract metadata from each skill file. - **[`phases/19-capstone-projects/87-end-to-end-safety-gate/outputs/skill-end-to-end-safety-gate.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/19-capstone-projects/87-end-to-end-safety-gate/outputs/skill-end-to-end-safety-gate.md)** — Example skill artifact demonstrating the standard front-matter and body format. - **[`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md)** — Top-level curriculum overview that also references the [`install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/install_skills.py) utility. ## Summary - The [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) installer requires only Python 3.10+ and has **no external dependencies**. - It discovers `skill-*.md`, `prompt-*.md`, and `agent-*.md` files under `phases/**/outputs`, parses their front-matter via [`scripts/_lib.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/_lib.py), and copies them to a target directory. - Choose from three layouts—`flat`, `by-phase`, or `skills`—and optionally filter by `--type`, `--phase`, or `--tag`. - A [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) is automatically generated to inventory every installed artifact. - After installation, consume each skill as a plain Markdown file in Python scripts, shell pipelines, or version-controlled project repositories. ## Frequently Asked Questions ### How do I install only a specific type of artifact, such as prompts or agents? Pass the `--type` flag to [`scripts/install_skills.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/install_skills.py) with the value `prompt`, `agent`, or `skill`. The `filter_artifacts()` step will then keep only the matching artifacts before `build_plan()` and `apply_plan()` copy them into the target directory. ### Can I use the installer without overwriting existing files in my target folder? Yes. By default, `apply_plan()` aborts the installation when it detects a name collision, so omitting `--force` keeps existing files safe. To preview exactly what would be copied without writing anything to disk, run the script with `--dry-run`. ### What Python version is required to run the skill installer? The script is implemented in pure Python and requires **Python 3.10 or newer**. It relies exclusively on the Python standard library for directory walking, front-matter parsing in [`scripts/_lib.py`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/scripts/_lib.py), and JSON manifest generation, so no `pip install` or virtual environment setup is necessary. ### How are the installed skill artifacts structured after installation? That depends on the `--layout` option you select. The `skills` layout creates a dedicated subdirectory per artifact and renames the file to [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md), while `flat` drops everything into one folder and `by-phase` mirrors the curriculum structure. Every run also produces a [`manifest.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/manifest.json) that records the original source path and metadata for each installed file.