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Init

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mattgierhart
init

Seed a fresh (greenfield) repository with the PRD-Driven Context Engineering scaffold — PRD.md, SoT/ knowledge files, EPIC templates, domain-profile config, and agent MEMORY starters. Invoked as /prd-ce:init when the plugin is installed. The framework itself (skills, agents, hooks, scripts) ships LIVE in the plugin; this skill only plants the consumer-owned files the plugin cannot carry as behavior. Triggers on "/prd-ce:init", "set up PRD lifecycle here", "scaffold a new PRD-CE project", "initialize the methodology". Outputs a seeded scaffold + a verification report.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameinit
Stars
179
Forks
11
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 mattgierhart on GitHub. Read the source before you install it.

Installation

Install the Init 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.
  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/mattgierhart/PRD-driven-context-engineering.git /tmp/PRD-driven-context-engineering
mkdir -p .claude/skills
cp -r /tmp/PRD-driven-context-engineering/plugins/prd-ce/skills/init .claude/skills/init
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Init 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 Init 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 Init 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.

/prd-ce:init — Greenfield Scaffold Seeder

Plant the consumer-owned scaffold for a fresh PRD-CE project. The methodology engine (lifecycle skills, the agent squad, governance hooks, readiness.py) is delivered live by the installed prd-ce plugin — it never gets copied into your repo. This skill seeds only what is yours to own and edit: PRD.md, the SoT/ graph, epics/ templates, your .claude/domain-profile.yaml, and per-agent MEMORY.md starters.

One manifest, no drift. The seed list comes from install-manifest.yaml's template_seed section — the same list install.sh (fork path) and ghm-template-sync (update path) read. This skill drives the deterministic prd-ce-init.sh so behavior is identical no matter who invokes it.

Scope (v1): greenfield only. This seeds an empty structure into a fresh repo. Mid-build and live-codebase on-ramps (entry-mode branching, graph extraction) are backlogged — see temp/plugin-conversion-plan.md. If the target already has PRD.md or SoT/ content, the seeder keeps it (non-destructive) rather than adapting to it.

Consumes

  • ${CLAUDE_PLUGIN_ROOT}/templates/ — the bundled seed sources (mirrors template_seed paths) the packager ships with the plugin.
  • install-manifest.yaml template_seed + never_touch — authoritative seed/skip lists.
  • scripts/prd-ce-init.sh — the deterministic seeder this skill drives.

Produces

  • A seeded scaffold in the target repo (no new SoT IDs — this skill places templates, it does not author specs).
  • A freshly reset PRD.md (frontmatter at v0.1, today's date, no stale template_version).
  • A verification report (hooks emit valid JSON; readiness.py runs).

Workflow

Phase 1 — Preflight

  1. Confirm git, python3, bash are present. Warn (don't block) if the target isn't a git repo.
  2. Confirm greenfield: if PRD.md or non-empty SoT/ already exist, say so and stop — the seeder will keep them untouched, so there is nothing for init to do. (Point the user at the lifecycle skills to keep building, not at re-seeding.)

Phase 2 — Wizard questions

Ask only what changes the outcome (honor the execution mode's budget):

  • Target directory (default: current repo).
  • Domain profile: product (default) · library · infrastructure · research. Quick mode skips this and takes the default.

Phase 3 — Seed (drive prd-ce-init.sh)

Run the deterministic seeder so behavior matches every other path:

bash
bash "${CLAUDE_PLUGIN_ROOT}/scripts/prd-ce-init.sh" --target <DIR> --dry-run   # preview
bash "${CLAUDE_PLUGIN_ROOT}/scripts/prd-ce-init.sh" --target <DIR>             # seed
  • Show the --dry-run plan first [standard+], then execute.
  • The seeder skips any file that already exists (never_touch honored) and resets a freshly seeded PRD.md frontmatter to v0.1.
  • After seeding, if the chosen domain profile differs from the default, update the profile: key in the seeded .claude/domain-profile.yaml [standard+].

Phase 4 — Verify (trust-but-verify)

  1. Run each plugin hook against the target; assert valid JSON on stdout.
  2. Run python "${CLAUDE_PLUGIN_ROOT}/scripts/readiness.py" run — a BLOCK on an empty scaffold is the gate working (no content yet), not a failure. Report the score.
  3. Print next steps: customize README.md + PRD.md, then "Let's frame the problem" (v0.1).

Anti-patterns

PatternFix
Hardcoding the seed file list in the skillDrive prd-ce-init.sh; it reads template_seed
Copying the framework (skills/hooks) into the consumer repoThe plugin provides those live — seed only consumer-owned files
Overwriting an existing PRD.md/SoT/Greenfield-only; the seeder skips what exists
Leaving PRD.md at the example's version headerFrontmatter reset to v0.1 happens automatically on a fresh seed
Treating a readiness BLOCK on a fresh scaffold as a bugIt's the gate working — report the score

Frequently asked questions

What does the Init AI skill do?

Seed a fresh (greenfield) repository with the PRD-Driven Context Engineering scaffold — PRD.md, SoT/ knowledge files, EPIC templates, domain-profile config, and agent MEMORY starters. Invoked as /prd-ce:init when the plugin is installed. The framework itself (skills, agents, hooks, scripts) ships LIVE in the plugin; this skill only plants the consumer-owned files the plugin cannot carry as behavior. Triggers on "/prd-ce:init", "set up PRD lifecycle here", "scaffold a new PRD-CE project", "initialize the methodology". Outputs a seeded scaffold + a verification report.

Why use Init on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattgierhart/PRD-driven-context-engineering/tree/main/plugins/prd-ce/skills/init. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Init?

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 Init?

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

Is the Init AI skill free?

Yes. It is published on GitHub by mattgierhart 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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