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Testing Eve Web

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michaelshimeles
testing-eve-web

Run and test the Eve web chat app (apps/eve) end-to-end locally — dev server setup, Vercel env, sending messages, model selector, and Eve session API smoke tests. Use when verifying UI or agent-behavior changes in apps/eve.

Overview

Publishermichaelshimeles
Repositoryeveclaw
Skill nametesting-eve-web
Stars
102
Forks
23
Bundled files
Instructions only
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 michaelshimeles on GitHub. Read the source before you install it.

Installation

Install the Testing Eve Web 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/michaelshimeles/eveclaw/tree/main/.agents/skills/testing-eve-web
  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/michaelshimeles/eveclaw.git /tmp/eveclaw
mkdir -p .claude/skills
cp -r /tmp/eveclaw/.agents/skills/testing-eve-web .claude/skills/testing-eve-web
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing Eve Web 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 Testing Eve Web 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 Testing Eve Web 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.

Testing the Eve web chat (apps/eve)

Setup

  1. Deps: npm install at the repo root.
  2. Env: the app needs Vercel-provided env (Neon DATABASE_URL, Blob, OIDC for sandboxes). Pull it from the correct project (root directory is apps/eve):
    bash
    cd apps/eve
    npx vercel link --yes --project personal-agent --scope ${SENTRY_ORG} --token "$VERCEL_TOKEN"
    npx vercel env pull .env.local --yes --token "$VERCEL_TOKEN"
  3. Dev server (detached, survives shell exit — plain nohup may not):
    bash
    cd apps/eve && setsid nohup npm run dev > /tmp/dev.log 2>&1 < /dev/null &
    App at http://localhost:3000. Check /tmp/dev.log for OIDC/DB errors; if OIDC expired, re-run vercel env pull.
  4. Typecheck: cd apps/eve && npm run typecheck.

Smoke-testing agent behavior via the Eve session API

/eve/v1/** is served on the same origin: withEve (next.config.ts) mounts the agent service into the Next.js dev server. It only 404s while the embedded agent backend is still booting — wait for [eve:dev] server listening in /tmp/dev.log before curling:

bash
until curl -sf localhost:3000/eve/v1/health >/dev/null; do sleep 2; done

Canonical routes (there is NO /eve/v1/message):

bash
# start a session (returns sessionId)
curl -s -X POST localhost:3000/eve/v1/session -H 'content-type: application/json' \
  -d '{"message":"Reply with only: pong","clientContext":{"eveWebModel":"openai/gpt-5.2"}}'
# stream the reply
curl -sN localhost:3000/eve/v1/session/<sessionId>/stream | grep -m1 message.completed

The channel delivers clientContext as a user message Client context:\n<json>; the agent's dynamic model resolver in agent/agent.ts parses that exact shape.

Latency profiling (per-turn overhead vs model TTFT)

  • Time the event stream to split app overhead from model latency: record wall-clock deltas for session.started, turn.started, message.received, step.started, first message.appended, message.completed. The turn.started → message.received gap is pre-model context work (dynamic instructions/skills); expect ≤ ~0.3s. Multi-second gaps usually mean an external call or sandbox open on the turn path.
  • To find slow outbound calls, temporarily monkey-patch globalThis.fetch in an agent-side module to log URL+duration (remove before committing). Watch /tmp/dev.log for vercel.com/api/v2/sandboxes calls — any during a plain chat turn means something (e.g. dynamic skills materialization) is forcing a sandbox open per turn.
  • Follow-up turns: POST localhost:3000/eve/v1/session/<sessionId> with {"message":"...","continuationToken":"<from session create>"}, then re-read the same /stream (events carry sequence).

Model selector testing tips

  • The picker button is in the composer (bottom-left). Catalog comes from /api/models (Vercel AI Gateway); selection persists in localStorage eve-web-model, favorites in eve-web-model-favorites.
  • Adversarial model-switch check: select a non-default model and ask "Which AI model are you? Answer with only the model family name (e.g. Claude, GPT, Gemini)." The default (claude-sonnet-5) answers "Claude"; a working switch to a GPT model answers "GPT".
  • Clear localStorage keys to test the default path.

Pitfalls

  • The Vercel project root directory must be apps/eve; a wrong root breaks ${PUBLIC_SENTRY_ENVIRONMENT} deploys and env pulls.
  • Sandbox tool calls may 403 if OIDC creds are stale — re-pull env and restart the dev server.
  • pkill -f "next dev" can kill your own shell command; prefer targeting the PID from /tmp/dev.log/lsof -i :3000.
  • CSS group-hover reveal-on-hover controls may not be capturable in automated screenshots; prefer always-visible controls or verify via DOM state.
  • If the dev log loops on waiting for sandbox template prewarm to finish, stale template locks from killed processes may be blocking it: stop the server, delete apps/eve/.eve/sandbox-cache/template-locks/<backend>/*.lock, restart. A cold sandbox template build (npm install of agent-browser + chromium in the VM) can take several minutes — chat turns that don't need the sandbox should still work during prewarm.
  • lsof may be unavailable; use ss -ltnp/pgrep -fl node instead to find the dev-server process.

Devin Secrets Needed

  • VERCEL_TOKEN — for vercel link / vercel env pull against the ${SENTRY_ORG} scope.

Frequently asked questions

What does the Testing Eve Web AI skill do?

Run and test the Eve web chat app (apps/eve) end-to-end locally — dev server setup, Vercel env, sending messages, model selector, and Eve session API smoke tests. Use when verifying UI or agent-behavior changes in apps/eve.

Why use Testing Eve Web on TypingMind?

Because you install it once and use it with any model. Testing Eve Web 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 Testing Eve Web in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/michaelshimeles/eveclaw/tree/main/.agents/skills/testing-eve-web. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Testing Eve Web?

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 Testing Eve Web?

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

Is the Testing Eve Web AI skill free?

It is published on GitHub by michaelshimeles. Check the repository for licensing terms. 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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