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Cli Testing

OrganizationPopular
vellum-ai
cli-testing

Manually test a running Vellum assistant end-to-end purely from the CLI — no desktop app or web UI. Hatch an instance, send messages, watch the reply, and tear it down. Use when verifying assistant behavior, reproducing a bug, or smoke-testing a change without the macOS/web clients.

Overview

Publishervellum-ai
Repositoryvellum-assistant
Skill namecli-testing
Stars
1.4K
Forks
208
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vellum-ai on GitHub. Read the source before you install it.

Installation

Install the Cli Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vellum-ai/vellum-assistant/tree/main/.claude/skills/cli-testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git /tmp/vellum-assistant
mkdir -p .claude/skills
cp -r /tmp/vellum-assistant/.claude/skills/cli-testing .claude/skills/cli-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cli Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cli Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cli Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CLI Testing — Exercise the Assistant End-to-End

Drive a real assistant from the terminal only. The vellum CLI (cli/, package @vellumai/cli) manages instance lifecycle; vellum message / vellum events exercise a running instance. See cli/AGENTS.md and the root README.md § CLI for command reference.

0. Prerequisites

bash
export PATH="$HOME/.bun/bin:$PATH"   # bun + the linked `vellum` binary
vellum ps                            # sanity check the CLI resolves

If vellum is missing, run ./setup.sh from the repo root once (installs deps, links the vellum command). Docker must be running for the default flow below.

1. Provide an LLM provider key (from the environment)

Local-mode and Docker-mode instances need one LLM provider key. The CLI reads it straight from the host environment — just export it before hatching/setup:

bash
export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY / GEMINI_API_KEY /
                                      # FIREWORKS_API_KEY / OPENROUTER_API_KEY /
                                      # MINIMAX_API_KEY

In Devin sessions ANTHROPIC_API_KEY is typically already present in the environment — check with echo "${ANTHROPIC_API_KEY:0:7}" before asking for one. The CLI maps providers to env vars in cli/src/shared/provider-env-vars.ts.

2. Hatch — default to a Docker hatch built from source

Always default to --remote docker. It runs the assistant, gateway, and credential-executor in isolated containers that mirror production and keep the test off your host process table. Reserve --remote local (§5) for the rare case where Docker is unavailable.

Build from source — that's the point of testing. A bare vellum hatch --remote docker pulls the published platform images even when the CLI itself runs from your checkout, so it would test released code, not your changes. Source-build is opt-in via a flag (resolveDockerHatchMode in cli/src/lib/docker.ts):

  • --source <path> — build images once from the source tree at <path>, no watcher. Default for testing: picks up your current changes and is robust for a scripted one-shot run.
  • --watch — build from source and start a file-watcher that rebuilds the affected image on change (watches each service's src/, package.json, and Dockerfile). Use while iterating. The watcher is a long-lived foreground process, so prefer --source for unattended/scripted runs.
bash
vellum hatch --remote docker --source . --name clitest   # build from cwd
# → "Mode: build-from-source" then "Images (local build): vellum-assistant:local-clitest …"

If --source/--watch is passed but no full source tree is found (e.g. the CLI is running from a packaged app bundle), the CLI falls back to pulling the published images and says so — watch for that line if you expect a build. Building all three images takes ~1–2 min the first time.

Hatch attached — do not pass -d. An attached hatch leases the guardian token and configures the provider credential from your environment inline, then returns once the containers are healthy — no follow-up vellum setup needed. Detached mode (-d) defers the guardian-token lease, so a later vellum setup cannot authenticate against the gateway and fails with an invalid_signature 401. Confirm readiness with vellum ps (🟢 healthy) before messaging.

3. Verify functionality

vellum message is async (returns a message id, not the reply — --json only adds {accepted, messageId}). vellum events streams the reply but is long-running, so background it, send, wait, then read.

Assert on a token the assistant must generate, never one you put in the prompt. vellum events echoes your prompt as **You:** <text> (cli/src/commands/events.ts), so grepping for a word that appears in the prompt passes even when the assistant never replied. Ask a question whose answer is absent from the prompt:

bash
( vellum events > /tmp/vel_events.log 2>&1 & )   # stream in background
sleep 2
vellum message "What is 6 multiplied by 7? Reply with only the number."
sleep 25                                          # let the assistant respond
pkill -f "vellum events"
grep -w 42 /tmp/vel_events.log                    # "42" is NOT in the prompt,
                                                  # so a match proves a real reply

The assistant's streamed reply is written as plain text (no **You:** prefix), so a match on a generated answer confirms the round-trip worked. If you must use a fixed sentinel string, strip the echoed prompt first (grep -v '^\*\*You:\*\*' /tmp/vel_events.log | grep <sentinel>).

Common verification commands

CommandPurpose
vellum psList instances + health (🟢 healthy), id, runtime URL, cloud
vellum message "<text>"Send a message (async; prints message id)
vellum eventsStream live events/replies (long-running — background it)
vellum logs -n 100Last 100 log lines; add -f to follow, -s assistant/-s gateway to filter
vellum clientInteractive terminal chat session (manual exploration)
vellum message --json "<text>"Send-ack as JSON ({accepted, messageId}) — the reply still arrives via vellum events, not here

4. Tear down

bash
vellum retire clitest --yes          # stops containers and removes the instance

retire is destructive (removes per-instance Docker volumes); always clean up test instances when done.

5. Fallback: local mode (no Docker)

Only when Docker is unavailable. Runs the daemon + gateway as plain host processes; configures the provider key automatically from the env at hatch time:

bash
vellum hatch --name clitest          # defaults to --remote local
# verify via the `vellum events` + generated-answer pattern in §3, then:
vellum retire clitest --yes

Frequently asked questions

What does the Cli Testing AI skill do?

Manually test a running Vellum assistant end-to-end purely from the CLI — no desktop app or web UI. Hatch an instance, send messages, watch the reply, and tear it down. Use when verifying assistant behavior, reproducing a bug, or smoke-testing a change without the macOS/web clients.

Why use Cli Testing on TypingMind?

Because you install it once and use it with any model. Cli Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cli Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vellum-ai/vellum-assistant/tree/main/.claude/skills/cli-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cli Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cli Testing?

As many as you like. As long as a model supports skills, you can use Cli Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cli Testing AI skill free?

Yes. It is published on GitHub by vellum-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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