compound-engineering-pi0.3.0
Pi-first Compound Engineering extension with subagents and MCPorter compatibility.
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Compound Engineering for Pi
Compound Engineering adapted for Pi. 84 skills, 9 legacy workflow prompts for compatibility, and a native compatibility layer, all installable in one command.
Install
Use this package as the Pi install/distribution layer:
pi install npm:compound-engineering-pi -l
Reload your session, then you're ready:
/reload
Quick start
Legacy prompt aliases still work:
pi -p "/workflows-plan Build a todo app"
pi -p "/workflows-work"
pi -p "/workflows-review"
pi -p "/workflows-compound"
And the newer upstream workflow skills are bundled too:
pi -p "/skill:ce:plan Build a todo app"
pi -p "/skill:ce:work"
pi -p "/skill:ce:review"
pi -p "/skill:ce:compound"
These follow the core loop: Plan, Work, Review, Compound.
What's included
Legacy workflow prompts
| Prompt | What it does |
|---|---|
/workflows-plan |
Create a structured project plan |
/workflows-work |
Execute work from a plan |
/workflows-review |
Multi-agent code review |
/workflows-compound |
Document learnings for future sessions |
/workflows-brainstorm |
Explore ideas before planning |
/deepen-plan |
Add depth to an existing plan |
/test-browser |
Run browser tests on your pages |
/feature-video |
Record a video walkthrough of a feature |
/resolve_todo_parallel |
Resolve all pending todos in parallel |
84 skills
This package now bundles the newer upstream Compound Engineering skill set for Pi, including the ce:* workflow skills, reviewer personas, planning/document-review personas, browser/video workflows, and the older specialized research/review skills that still power the legacy prompts.
Run pi skills after installing to see the full list.
Pi compatibility extension
Four tools that bridge common agent workflows into Pi:
ask_user_question- prompt the user for inputsubagent- orchestrate tasks (single,parallel,chain)mcporter_list- list available MCP serversmcporter_call- call MCP server tools
Subagent output behavior:
- single runs return the full subagent output
- chain runs return the final step output plus a step summary
- parallel runs stay compact by default, but support
includeOutputs: true - if you install a richer
pi-subagentspackage, this package automatically defers to it
Subagent model selection (compatibility tool):
- pass
model: "haiku"or aprovider/modelId(optionally with a:thinkingsuffix) for a single task - set a top-level
modelas the default fortasks[]orchain[]; each entry can override it - omitted models keep the child Pi process's configured default, not necessarily the parent session's active model
- an empty or whitespace-only entry model clears the top-level default
Example subagent tool arguments for a cheaper scout and a stronger reviewer:
{
"model": "haiku",
"tasks": [
{ "agent": "repo-research-analyst", "task": "Find the relevant code" },
{ "agent": "kieran-typescript-reviewer", "task": "Review correctness", "model": "sonnet" }
]
}
Use models available and authenticated in your Pi installation. See the Pi guide for all three modes.
Conversion and sync
For converter usage, prefer the upstream package:
bunx @every-env/compound-plugin install compound-engineering --to pi
This repo’s bundled package should mainly be used via pi install npm:compound-engineering-pi.
The vendored plugins/compound-engineering snapshot and bundled Pi skills can be refreshed from the main plugin repo with:
bun run sync:upstream
By default this syncs from ../compound-engineering-plugin. Override with COMPOUND_PLUGIN_SOURCE=/path/to/compound-engineering-plugin if needed.
Optional: MCP support via MCPorter
For MCP interoperability, install MCPorter:
npm i -g mcporter
Maintainer workflow
This repo now follows an upstream-first model:
- make converter/content changes in
compound-engineering-pluginfirst - refresh this package with
bun run sync:upstream - keep this repo focused on Pi packaging, Pi docs, and Pi-specific compatibility behavior
See MAINTENANCE.md for the exact boundary and workflow.
Credits
Built on the Compound Engineering plugin by Every and Kieran Klaassen. Read the original writeup.
License
MIT