verify
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/plastic-labs/honcho --skill verify- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads verify automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
Prefer driving through the honcho-cli skill; fall back to the SDKs
(sdks/python, sdks/typescript), then raw REST against /v3, in that order
when a method isn't available at the higher level. The honcho-integration
skill covers how to use the SDKs.
When a change updates endpoints or makes schema changes, verify across all three surfaces — CLI, SDKs, and REST — since they can drift independently.
For LLM-path changes, the fastest synchronous surface is dialectic at the
minimal reasoning level: send a couple of messages, then hit
/v3/.../peers/{peer_id}/chat and observe the response.
Configuration
Configuration is central to both driving the app and running tests: it's how
API keys reach the server and how a config-related change gets exercised at
all. Settings come from environment variables or files, with precedence
env > .env > config.toml > defaults. To verify a configuration change,
set the relevant option through one of these layers, restart the affected
process, and observe the behavioral difference at the surface — the same
mechanism lets you point provider base URLs, model choices, and timeouts at
credentials and proxies you actually have.
Two concrete levers: deep-nested settings (e.g.
[dialectic.levels.minimal.model_config.overrides.provider_params]) are
miserable as env vars — drop a partial config.toml in the repo root instead.
And for load-time config behavior, uv run python -c "import src.config; ..."
is faster than booting the server.
Test suites
Three test types matter here. All run in CI, but they're also runnable locally with whatever API keys and configuration you have — Honcho's config surface is large, so options like per-agent timeouts and provider base URLs can be pointed at your own keys/proxies to exercise a change:
# Unit tests (pytest; spins up its own infra via fixtures)
uv run pytest tests/
# Unified tests — step-based end-to-end flows defined in JSON
# (config hierarchy, multi-turn interactions, LLM-as-judge assertions)
uv run python -m tests.unified.run
uv run python -m tests.unified.run --test-dir tests/unified/test_cases
# Live LLM tests — real provider calls, for testing specific backends.
# Needs provider API keys AND model vars — without LIVE_LLM_*_MODELS the
# tests silently deselect that provider. See tests/live_llm/README.md.
export LLM_ANTHROPIC_API_KEY=...
export LIVE_LLM_ANTHROPIC_45_PLUS_MODELS=claude-sonnet-4-5
uv run pytest tests/live_llm -n 0 --live-llm --no-header -q
FAQ
What does the verify skill do?
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.
How do I install verify?
Run npx skills add https://github.com/plastic-labs/honcho --skill verify in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/verify/ (personal) or .claude/skills/verify/ (project).
Which AI agents can use verify?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is verify safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains verify?
It is published by plastic-labs in plastic-labs/honcho. It has 146 recorded installs.
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