Most agent tools decide for you. They ship a fixed set of commands, a fixed idea of “plan mode,” a fixed opinion about sub-agents, and you bend your workflow to fit. Pi — the new minimalist agent harness behind OpenClaw — goes the other way. It’s stripped down on purpose, and the whole pitch is that you adapt it to you, not the reverse.
That sounded great. Pi isn’t a sealed product — if you need a command, a tool, a provider, a workflow, or a UI tweak, you just ask Pi to build it, and it customizes itself on the fly. It ships powerful defaults but deliberately skips things like sub-agents and plan mode, on the theory that you’ll add them if you actually want them. Customizations bundle as Pi packages and ship over npm or git.
The catch: I wasn’t sure I was ready to build up all that judgment myself, skill by skill. A blank harness is only as good as what you teach it. Then I remembered agent-skills — the 77k-star pack of engineering process from Addy Osmani, a senior engineering leader at Google — that I’d already been testing inside Claude Code. Someone had ported it to Pi. So the test wrote itself: take the bare harness, drop in those skills, point it at a model, and see how far it gets.
The setup
Three moving parts.
The harness: Pi, out of the box, minimal.
The brain: @chankov/agent-skills, a Pi port of Addy Osmani’s agent-skills. The original bills itself as “production-grade engineering skills for AI coding agents,” and it’s cleared 77k stars on GitHub — Osmani is a senior engineering leader on Google Chrome, so this isn’t hobby advice. The skills are organized by phase: idea → spec → plan → implement → test → review → ship. Each one encodes the process a good engineer actually follows instead of just “write the code.”
install pi.dev/packages/@chankov/agent-skills
The model: I connected Pi to my OpenCode Go subscription and ran everything on kimi-k2.7-code at medium effort.
The test: spec, then build
My prompt was specific: a playable snake game in the terminal, styled to look like The Matrix — green digital rain falling behind the board. Not “write snake” — I let the skills drive. It ran the spec skill first, asked me a few clarifying questions the way Claude Code does, and only then started writing code. That ordering matters. The spec-first workflow is exactly what stops a model from confidently building the wrong thing.
The output was clean Python: a pure game-logic core with no curses imports, a separate renderer for terminal I/O, an input handler, and the Matrix rain effect cascading behind the play area. The interesting part was what happened when I asked it to grade its own work.
Running /review
I ran the installed /review skill on the finished game. What came back wasn’t a rubber stamp — a genuine five-axis review (correctness, readability, architecture, security, performance) that caught the things a good reviewer would: a spot where the renderer was quietly mutating game state, a bloated main() worth splitting, an unhandled edge case nobody asked about, and a correct nothing critical on security instead of invented risk.
That’s the tell. A weak reviewer pads the list. This one separated what’s good from what to fix, ranked it, and knew when to stop. Reading it back, I couldn’t tell it apart from a review I’d get out of Claude Code or any of the other agent harnesses I run day to day.
One note: it just runs
The skills asked me clarifying questions like Claude Code — but they never stopped to ask permission before acting. Pi read files, wrote code, and ran the review on its own. Honestly, I liked it: no clicking “yes” on every step, it just moved.
The clean way to get that speed is to run Pi in a container. Sandbox the workspace, let it go, and autonomy stops being something to worry about — a mistake can’t reach anything outside the box.
Takeaway
A minimalist harness plus a best-practice skills package added up to something that specced deliberately, built cleanly, and produced code and reviews I couldn’t distinguish from what Claude Code or any other harness I already use hands me. The harness stayed out of the way; the skills supplied the judgment.
If you’ve been curious about Pi, this is the fast way in: install @chankov/agent-skills, point it at a model, and ask it to review something you wrote. Then decide who the senior engineer in the room actually is.
