Skills

Verify

Build, launch, and drive a local Honcho stack to verify a change at its runtime surface...

Install

npx skills add plastic-labs/honcho --skill verify -g -a claude-code -y
Repository
plastic-labs/honcho7.9k
Category
Engineering
Updated
3d ago
License
AGPL-3.0
Context cost
Lightabout 1,110 tokens
Runs scripts
No, instructions only

What it does

Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app.

Verifying changes in Honcho

Prerequisites

Docker Compose is the preferred way to run the stack, docker-compose.yml at the repo root brings up Postgres (pgvector), Redis, the API server, and the deriver worker together. If Docker Compose isn't available, the stack can also run directly on the host (see Launch below); you'll need Postgres with pgvector and Redis reachable, plus a .env with connection strings and an LLM provider key for any flow that hits a model (deriver, dialectic, dreamer).

Working in a worktree? It carries neither .env nor node_modules. Copy .env from the main checkout (that's where the provider keys live), and run bun install in sdks/typescript if you'll run the full test suite, otherwise the pre-push gate fails on a phantom Cannot find package 'zod'.

Launch

First check whether the stack is already running via Docker Compose:

docker compose ps          # look for api / deriver / database / redis
docker compose up -d       # start it if not

A running stack is not the same as your branch's code running, check the CREATED column; the images may be weeks old. For verifying a diff, the cheap path is to reuse the stack's Postgres/Redis containers but run the branch's API as a host process on a spare port:

uv run uvicorn src.main:app --port 8901   # branch code, stack's DB/Redis
uv run python -m src.deriver              # if the diff touches the worker

This avoids both an image rebuild and the port/project-name conflicts a second compose stack in a worktree would cause. Without Docker at all, run the two processes the same way against host Postgres + Redis (API default port 8000 via uv run fastapi dev src/main.py).

When reading server logs, note that with the main .env the telemetry emitter spams connection warnings at an unreachable endpoint, grep -v telemetry.emitter before looking for the real error.

Drive it

Read the whole SKILL.md on GitHub

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