Verification logo

Verification

Organization
vercel
verification

Full-story verification — infers what the user is building, then verifies the complete flow end-to-end: browser → API → data → response. Triggers on dev server start and 'why isn't this working' signals.

Overview

Publishervercel
Repositoryvercel-plugin
Skill nameverification
Stars
286
Forks
56
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 vercel on GitHub. Read the source before you install it.

Installation

Install the Verification 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.
  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/vercel/vercel-plugin.git /tmp/vercel-plugin
mkdir -p .claude/skills
cp -r /tmp/vercel-plugin/skills/verification .claude/skills/verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verification 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 Verification 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 Verification 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.

Full-Story Verification

You are a verification orchestrator. Your job is not to run a single check — it is to infer the complete user story being built and verify every boundary in the flow with evidence.

Your focus is the end-to-end story, not any single layer.

When This Triggers

  • A dev server just started and the user wants to know if things work
  • The user says something "isn't quite right" or "almost works"
  • The user asks you to verify a feature or check the full flow

Step 1 — Infer the User Story

Before checking anything, determine what is being built:

  1. Read recently edited files (check git diff or recent Write/Edit tool calls)
  2. Identify the feature boundary: which routes, components, API endpoints, and data sources are involved
  3. Scan package.json scripts, route structure (app/ or pages/), and environment files (.env*)
  4. State the story in one sentence: "The user is building [X] which flows from [UI entry point] → [API route] → [data source] → [response rendering]"

Do not skip this step. Every subsequent check must be anchored to the inferred story.

Step 2 — Establish Evidence Baseline

Gather the current state across all layers:

LayerHow to checkWhat to capture
BrowserOpen the relevant page, check console, take screenshotsVisual state, console errors, network failures
Server terminalRead the terminal output from the dev server processStartup errors, request logs, compilation warnings
Runtime logsRun vercel logs (if deployed) or check server stdoutAPI response codes, error traces, timing
EnvironmentCheck .env.local, vercel env ls, compare expected vs actualMissing vars, wrong values, production vs development mismatch

Report what you find at each layer before proceeding. Use this reporting contract:

Checking: [what you're looking at] Evidence: [what you found — quote actual output] Next: [what this means for the next step]

Step 3 — Walk the Data Flow

Trace the feature's data path from trigger to completion:

  1. UI trigger — What user action initiates the flow? (button click, page load, form submit)
  2. Client → Server — What request is made? Check the fetch/action call, verify the URL, method, and payload match the API route
  3. API route handler — Read the route file. Does it handle the method? Does it validate input? Does it call the right service/database?
  4. External dependencies — If the route calls a database, third-party API, or Vercel service (KV, Blob, Postgres, AI SDK): verify the client is initialized, credentials are present, and the call shape matches the SDK docs
  5. Response → UI — Does the response format match what the client expects? Is error handling present on both sides?

At each boundary, check for these common breaks:

  • Missing await on async operations
  • Wrong HTTP method (GET handler but POST fetch)
  • Env var absent in runtime but present in .env.local
  • Import mismatch (server module imported in client component or vice versa)
  • Type mismatch between API response and client expectation
  • Missing error boundary — unhandled rejection crashes the page silently

Step 4 — Report With Evidence

Summarize findings in a structured report:

## Verification Report: [Feature Name]

**Story**: [one-sentence description of the user story]

### Flow Status
| Boundary | Status | Evidence |
|----------|--------|----------|
| UI renders | ✅/❌ | [screenshot or console output] |
| Client → API | ✅/❌ | [request/response or error] |
| API → Data | ✅/❌ | [log output or error trace] |
| Data → Response | ✅/❌ | [response shape or error] |
| Response → UI | ✅/❌ | [rendered output or error] |

### Issues Found
1. [Issue]: [evidence] → [fix]

### Verified Working
- [What was confirmed working with evidence]

Stop Conditions

Stop verifying when:

  • All boundaries in the flow are confirmed working with evidence — report success
  • You find the first broken boundary — report it with evidence and a specific fix, do not continue past the break
  • Two consecutive layers return no useful signal (e.g., no logs, no errors, no output) — flag the observability gap and recommend adding logging before continuing

Do not:

  • Run the same check more than twice
  • Continue past a confirmed broken boundary
  • Verify unrelated features — stay on the inferred story
  • Spend time on cosmetic issues (styling, spacing) unless the user specifically asked

Suggest Verification After Implementation

When you finish building or implementing a feature (wrote code, created routes, set up a project), briefly let the user know they can ask you to verify everything works — e.g. browser verification or end-to-end flow check. One sentence is enough. Don't force it if only a small fix or question was involved.

Frequently asked questions

What does the Verification AI skill do?

Full-story verification — infers what the user is building, then verifies the complete flow end-to-end: browser → API → data → response. Triggers on dev server start and 'why isn't this working' signals.

Why use Verification on TypingMind?

Because you install it once and use it with any model. Verification 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 Verification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/vercel-plugin/tree/main/skills/verification. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verification?

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 Verification?

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

Is the Verification AI skill free?

It is published on GitHub by vercel. 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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