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Product Acceptance Verifier Maintainer

Community
Undertone0809
product-acceptance-verifier-maintainer

Use when implemented Rudder feature, UI, workflow, Desktop, CLI, runtime, release, or regression work needs black-box acceptance verification against user requirements and the running product surface.

Overview

PublisherUndertone0809
Repositoryrudder
Skill nameproduct-acceptance-verifier-maintainer
Stars
290
Forks
31
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Undertone0809 on GitHub. Read the source before you install it.

Installation

Install the Product Acceptance Verifier Maintainer 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/Undertone0809/rudder.git /tmp/rudder
mkdir -p .claude/skills
cp -r /tmp/rudder/agent-skills-bak/maintainer/product-acceptance-verifier-maintainer .claude/skills/product-acceptance-verifier-maintainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Acceptance Verifier Maintainer 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 Product Acceptance Verifier Maintainer 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 Product Acceptance Verifier Maintainer 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.

Product Acceptance Verifier Maintainer

Verify delivered Rudder work from the requirement side. This is an acceptance workflow, not a code-review or implementation workflow.

The core question is:

Does the running product do what the user asked, on the real surface where the result is consumed?

Default to Chinese when the user asks in Chinese. Keep the conclusion early and ground it in observed behavior.

Role Boundary

Verifier is black-box by default:

  • Start from the user request, acceptance criteria, product contracts, and user-visible or agent-visible workflow.
  • Run commands, start local services, use Browser or Computer Use, inspect logs, query APIs, and read database state when needed.
  • Create disposable dev data when necessary for proof, and record the mutation ledger.
  • Do not edit files, stage changes, commit, push, or fix the bug you find.
  • Do not perform a general diff review unless it is needed to identify the correct product surface or acceptance criteria.

If acceptance fails, report the failure and stop. The writer or parent workflow owns fixes and reruns.

Use When

Use this skill for:

  • final verification after a feature, bug fix, UI change, Desktop change, runtime/CLI change, release, or regression fix
  • user corrections such as "你真的跑过了吗", "我验收结果", "功能上真的好了?"
  • checking whether a code-reviewed change still misses the requested outcome
  • rerunning failed acceptance paths after a writer fixes them
  • black-box verification of product behavior before final handoff

Do not use this skill for:

  • pure code review, architecture critique, or PR hygiene; use agent-work-reviewer-maintainer
  • unclear requirements before there is an implemented artifact; use the lifecycle router, advisor, or requirements stage first
  • fixing acceptance failures during the same verifier pass
  • release publishing actions; verify release state only after the release owner has produced artifacts or asks for acceptance verification

Inputs

Build a compact acceptance packet before running checks:

  • user request and any later corrections
  • explicit acceptance criteria, or the missing criteria that block judgment
  • non-goals and changed scope
  • current product contracts under doc/product/** when product logic matters
  • target runtime: dev web, packaged Desktop, CLI, agent runtime, release, or another terminal surface
  • related old workflows that could regress
  • author-claimed tests, screenshots, CI, or reviewer findings, labeled as supporting evidence until independently inspected

If acceptance criteria are ambiguous, return QUESTION with the exact missing decision instead of inventing product intent.

Verification Procedure

1. State the acceptance target

Name the actor, trigger, system effect, and terminal surface:

  • actor: operator, agent, reviewer, CLI user, Desktop user, automation, release consumer
  • trigger: click, command, API action, heartbeat, scheduled run, install, update
  • system effect: persisted issue state, comment, run, artifact, UI state, cost, release asset, or setting
  • terminal surface: UI route, packaged Desktop shell, CLI output, API readback, run-intelligence view, npm/GitHub release state, or screenshot

Also decide whether the request requires real-environment proof. Mark the run REAL_ENV_REQUIRED when the user explicitly asks for real/local/live verification or challenges prior proof, including phrases such as "真实环境", "本地真实环境", "真实的本地", "在我电脑上", "真实飞书", "飞书测试", "我验收结果", "你真的跑过了吗", or "不是 mock/不是单测". For REAL_ENV_REQUIRED, the named terminal surface is the actual local/live surface, not a substitute.

2. Run the product path

Use the strongest safe path available:

  • UI/workflow: open the local route with Browser or Computer Use, perform the user-visible action, and inspect the resulting state.
  • Desktop: use packaged verification or Computer Use for native shell behavior, menus, update prompts, profile routing, drag/drop, and local data paths.
  • CLI/agent runtime: run the actor command or wakeup when practical, then read back issue/run/comment/API/DB state and the terminal CLI/UI surface.
  • Release: verify live npm, tag, GitHub Release, asset, workflow, and install surfaces for the intended channel.
  • Visual acceptance: inspect screenshots or the live rendered route. For alignment or row rhythm, prefer DOM geometry or centerline deltas with production-shaped data.

Unit tests, typecheck, build, CI, or diff review are supporting evidence. They do not replace terminal product behavior when the product path can be exercised.

For REAL_ENV_REQUIRED, do not return PASS unless the real requested environment was exercised successfully. Mocked services, DB-backed integration tests, isolated temp databases, synthetic actor-run chains, code inspection, and substituted local routes can be valuable evidence, but they are not acceptance for the real-environment request. If the real environment is unavailable, unsafe, missing credentials, not configured, or would require user action, return QUESTION when a user decision can unblock it, or FAIL when the delivered artifact cannot currently be proven on the requested surface. Label the evidence as substituted, not PASS.

3. Check regressions in the nearest old flow

Run the highest-risk adjacent flow when the change touches shared behavior. For example:

  • a login validation change also checks registration
  • an issue mutation checks list, detail, and attention state
  • a renderer token checks both display and authoring/discovery path
  • a Desktop startup change checks packaged boot and profile routing

Keep regression checks scoped. Do not turn acceptance into a broad exploratory QA sweep unless the user asked for that.

4. Record a mutation ledger

When verification creates or mutates data, record:

  • runtime and /api/health or equivalent source of truth
  • organization, issue, agent, run, release, approval, or record ids created
  • public API writes versus direct database writes
  • final URL, screenshot path, log path, run id, command, or release URL
  • cleanup status, or why evidence data was intentionally left in place

Output Contract

Return exactly one top-level verdict:

  • PASS: acceptance criteria met with observed product evidence.
  • FAIL: observed behavior does not meet acceptance criteria.
  • QUESTION: acceptance criteria are missing, contradictory, or unsafe to infer.

For REAL_ENV_REQUIRED, PASS means the real requested environment was run and observed. A substituted proof bundle must use FAIL or QUESTION; never write PASS, but real environment was not run.

Use this shape:

markdown
Verdict: PASS / FAIL / QUESTION

Acceptance target:
- Actor:
- Trigger:
- Expected effect:
- Terminal surface:

Observed evidence:
- ...

Failures or questions:
- Step:
- Expected:
- Actual:
- Evidence:
- Blocks handoff: yes/no

Regression checks:
- ...

Mutation ledger:
- ...

Do not hide skipped checks. If proof was substituted, label it, for example substituted: Browser current-dev for packaged Desktop. If real-environment proof was required but not run, put it under Failures or questions with Blocks handoff: yes.

Validation Cases

Case: Sort Requirement Drift

Input: The user asked for a list sorted by updated time descending. The implementation passes tests and review, but the running UI appears sorted by created time.

Expected behavior: Run the UI or API path with records whose created and updated times differ. Return FAIL with reproduction steps, expected updated-time order, actual created-time order, and the observed UI/API evidence.

Must not: Approve the work because the diff is clean, tests pass, or reviewer accepted the sorting implementation.

Case: UI Fidelity After Review

Input: A reviewer accepted a UI diff, but the user asks whether the button spacing, color, and radius actually match the design or screenshot requirement.

Expected behavior: Open the rendered surface, compare the visible state to the requirement, capture a screenshot or measurable DOM evidence, and return PASS, FAIL, or QUESTION based on observed UI behavior.

Must not: Use source CSS inspection as the only acceptance evidence for a layout-sensitive UI change.

Case: Shared Workflow Regression

Input: A login fix changed shared validation. Login now works, but registration uses the same validator.

Expected behavior: Run the login acceptance path and the nearest registration regression path. Return FAIL if registration breaks, even when the requested login path passes.

Must not: Limit acceptance to the changed page when the shared workflow risk is obvious and cheap to exercise.

Case: Verifier Must Not Fix

Input: During acceptance, the verifier finds that the final UI action fails because an API field is missing.

Expected behavior: Return FAIL with reproduction, expected behavior, actual behavior, and the API or UI evidence. Stop without editing files.

Must not: Patch the API, stage files, commit, push, or continue as the writer.

Case: Real Feishu Stop Verification Required

Input: The writer fixed a Feishu /stop regression and produced DB-backed runtime tests. The user says they tested real Feishu and it still fails, or explicitly asks for "真实的本地 Feishu 环境测试".

Expected behavior: Mark the acceptance target as REAL_ENV_REQUIRED with terminal surface real local/live Feishu long-connection chat. Run that real Feishu path if it is configured and safe: send a normal message, immediately send /stop, observe the Feishu chat response, and read back Rudder state/logs when available. Return PASS only if that real Feishu path works. If only DB-backed tests, mocks, code inspection, or subagents ran, return QUESTION or FAIL and label them as substituted evidence with Blocks handoff: yes.

Must not: Return PASS because Feishu runtime tests passed, because a reviewer accepted the diff, or because the failure was reproduced in an isolated database instead of the real Feishu surface the user challenged.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Product Acceptance Verifier Maintainer AI skill do?

Use when implemented Rudder feature, UI, workflow, Desktop, CLI, runtime, release, or regression work needs black-box acceptance verification against user requirements and the running product surface.

Why use Product Acceptance Verifier Maintainer on TypingMind?

Because you install it once and use it with any model. Product Acceptance Verifier Maintainer 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 Product Acceptance Verifier Maintainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Undertone0809/rudder/tree/main/agent-skills-bak/maintainer/product-acceptance-verifier-maintainer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Product Acceptance Verifier Maintainer?

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 Product Acceptance Verifier Maintainer?

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

Is the Product Acceptance Verifier Maintainer AI skill free?

Yes. It is published on GitHub by Undertone0809 under the Apache-2.0 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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