From PRD-Driven Context Engineering
Seeds a greenfield repository with the PRD-Driven Context Engineering scaffold: PRD.md, SoT/ knowledge files, EPIC templates, domain config, and agent memory starters. Invoked via /prd-ce:init for project initialization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/prd-ce:initThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Plant the consumer-owned scaffold for a fresh PRD-CE project. The methodology engine
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'stemplate_seedsection — the same listinstall.sh(fork path) andghm-template-sync(update path) read. This skill drives the deterministicprd-ce-init.shso 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 hasPRD.mdorSoT/content, the seeder keeps it (non-destructive) rather than adapting to it.
${CLAUDE_PLUGIN_ROOT}/templates/ — the bundled seed sources (mirrors template_seed
paths) the packager ships with the plugin.install-manifest.yaml template_seed + never_touch — authoritative seed/skip lists.scripts/prd-ce-init.sh — the deterministic seeder this skill drives.PRD.md (frontmatter at v0.1, today's date, no stale template_version).readiness.py runs).git, python3, bash are present. Warn (don't block) if the target isn't a git repo.PRD.md or non-empty SoT/ already exist, say so and stop —
the seeder will keep them untouched, so there is nothing for init to do. (Point the user at
the lifecycle skills to keep building, not at re-seeding.)Ask only what changes the outcome (honor the execution mode's budget):
product (default) · library · infrastructure · research.
Quick mode skips this and takes the default.prd-ce-init.sh)Run the deterministic seeder so behavior matches every other path:
bash "${CLAUDE_PLUGIN_ROOT}/scripts/prd-ce-init.sh" --target <DIR> --dry-run # preview
bash "${CLAUDE_PLUGIN_ROOT}/scripts/prd-ce-init.sh" --target <DIR> # seed
--dry-run plan first [standard+], then execute.never_touch honored) and resets a freshly
seeded PRD.md frontmatter to v0.1.profile: key in the seeded .claude/domain-profile.yaml [standard+].python "${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.README.md + PRD.md, then "Let's frame the problem" (v0.1).| Pattern | Fix |
|---|---|
| Hardcoding the seed file list in the skill | Drive prd-ce-init.sh; it reads template_seed |
| Copying the framework (skills/hooks) into the consumer repo | The 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 header | Frontmatter reset to v0.1 happens automatically on a fresh seed |
| Treating a readiness BLOCK on a fresh scaffold as a bug | It's the gate working — report the score |
npx claudepluginhub mattgierhart/prd-driven-context-engineering --plugin prd-ceInstalls the PRD-Driven Context Engineering methodology into a fresh or existing repository via an interactive wizard. Seeds .claude/ hooks, skills, agents, rules, and scripts without overwriting product content.
Scaffolds full projects from PRD + stack templates: directory structure, configs, CLAUDE.md, git repo init, GitHub push. Studies existing projects via SoloGraph, uses Context7 for library versions.
Scaffolds greenfield project architecture and AI agent harness via interview-driven decisions. Outputs markdown spec with code structure exemplar, tests, guardrails, CLAUDE.md setup, and unified plan. Invoke via /scaffold for new projects.