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Manual Qa

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gronxb
manual-qa

Execute a caller's natural-language HotUpdater QA scenario on `examples/v0.85.0` with profile-scoped infrastructure prepared by `hot-updater-agent` and app interactions driven through `agent-device`. Use for AI-led manual OTA QA rather than the full E2E runner.

Overview

Publishergronxb
Repositoryhot-updater
Skill namemanual-qa
Stars
1.8K
Forks
179
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Manual Qa 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/gronxb/hot-updater/tree/main/.agents/skills/manual-qa
  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/gronxb/hot-updater.git /tmp/hot-updater
mkdir -p .claude/skills
cp -r /tmp/hot-updater/.agents/skills/manual-qa .claude/skills/manual-qa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manual Qa 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 Manual Qa 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 Manual Qa 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.

Hot Updater Manual QA

The only required caller input is a natural-language scenario. Accept the user's intent directly; never require or reinterpret it as a file name from e2e/shared/scenarios.

Always load and follow $hot-updater-agent and $agent-device, then read references/runtime-targets.md.

Defaults

  • Profile: standalone-kysely.
  • Platforms: iOS, then Android, strictly sequential.
  • Environment target: examples/v0.85.0/.env.hotupdater.
  • Git target: the exact current HEAD SHA.
  • Lease: two hours.

Honor explicit profile, platform, ref, environment target, or lease overrides. Never ask for them when the defaults are sufficient.

Start The Prepared Environment

For each platform, run from the repository root:

bash
hot-updater-agent -json manual start standalone-kysely \
  -platform ios \
  -ref "$(git rev-parse HEAD)" \
  -env-target examples/v0.85.0/.env.hotupdater \
  -ttl 2h

The command waits for readiness. Treat the returned JSON handoff as the only source of truth. It includes:

  • session ID, status, expiry, resolved ref, and log path;
  • retained worktree, scenario root, and injected env target;
  • control, runtime-config, and update-server URLs;
  • platform, device ID, app ID, release artifact path, and agent-device session;
  • exact boot, install, open, snapshot, and close commands;
  • the exact cleanup command.

Do not reconstruct fixed ports, paths, app IDs, devices, or agent-device flags. Use the values and commands returned for this session.

Turn Natural Language Into A Manual Plan

Before touching the device, translate the request into a small observable test plan containing:

  • required preconditions and OTA/provider state;
  • user-visible actions in order;
  • expected results and the evidence that will prove each result;
  • any destructive or intentionally crashing step that needs special handling.

Keep the user's scope. Do not silently expand a focused request into the fixed regression suite. If a required expectation is genuinely ambiguous after inspecting the app and control contracts, stop and name the ambiguity instead of inventing a pass condition.

Use the prepared worktree to discover how to carry out the plan. Read only the relevant parts of:

  • examples/v0.85.0/src/e2eApp for routes, actions, testIDs, and visible evidence;
  • e2e/shared/control-server/routes.ts for supported control operations;
  • e2e/mobile/driver.ts when launch, reload, screen-state wait, crash, or Android reattachment semantics matter;
  • e2e/shared/scenarios only as optional implementation examples for a similar step, never as the accepted input vocabulary or an exact script to run.

Do not create an automated scenario file or run the full E2E suite.

Execute With Control APIs And Agent Device

  • Before the first app step, run the returned bootCommand, installCommand, and openCommand in that order.
  • Use the returned agent-device session/device binding for every app command. Open the focused route for the intended action or evidence, take an interactive snapshot, then use refs/selectors with --settle for mutations.
  • Verify named expectations with agent-device wait, get, is, find, or settled diffs. A screenshot or bare snapshot alone is not a pass condition.
  • POST infrastructure/deploy/state operations to <handoff.controlBaseUrl><path>. For responses containing jobId, poll GET /e2e/jobs/<jobId> until succeeded; fail on failed or cancelled.
  • Record every returned Bundle ID, Release ID, authority, scope, generation, cohort, marker, and other value needed by a later assertion. Never continue with an empty or guessed value.
  • Before a fresh launch, POST /e2e/prepare-app-launch; for reload, close the app, prepare the launch, then reopen it. Use /e2e/reset-local-app-state only when the user's precondition requires a clean local state.
  • For mapped action results, mirror driver.ts: reset the relevant /e2e/screen-state field to idle, perform the UI action, and wait through runtimeConfigUrl until the field reaches the expected value. Reattach Android after external restart or crash-recovery operations when required.
  • Preserve intentional crash/disconnect semantics and gather recovery evidence after relaunch; do not convert a module-scope crash case into an unrelated UI crash.

Use curl -fsS with content-type: application/json for control calls. Keep a small evidence ledger that links each expected result to the observed UI, control response, or native state.

OTA Assertion Rules

  • Release IDs prove selection and policy; Bundle IDs prove bytes, patches, manifests, storage, and crash history.
  • Public launch statuses are exactly UNCHANGED | UPDATE_APPLIED | RECOVERED. Directional identity is fromReleaseId, fromBundleId, toReleaseId, and toBundleId.
  • notifyAppReady is read-only and must not affect crash detection or receipt promotion.
  • Crash bundles must throw at module scope. Never convert crash steps into UI crashes.
  • Deploy only through the control server/pnpm hot-updater deploy; never seed Bundle or Release database rows directly.
  • Use release artifacts only. Never start Metro or install a debug build.
  • Use interactive snapshots for structural evidence; take screenshots only when asked.

Cleanup

Always clean up in a finally-style path, including assertion failure or user interruption:

  1. Run the returned closeCommand if the app/session was opened.
  2. Run hot-updater-agent manual stop <session-id>.
  3. Confirm hot-updater-agent manual status <session-id> is stopped (or expired if the lease elapsed).

Do not start Android until the iOS session has stopped. A manual session owns an exclusive dashboard E2E lease, so leaving it ready blocks queued E2E work.

Reporting

Report the user's scenario, resolved manual plan, profile, platform, prepared commit, session ID, deployed Release/Bundle pairs, saved runtime variables, each assertion and evidence source, final status, and cleanup result. On failure include the failing stage, control response or snapshot evidence, and the session log path.

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 Manual Qa AI skill do?

Execute a caller's natural-language HotUpdater QA scenario on `examples/v0.85.0` with profile-scoped infrastructure prepared by `hot-updater-agent` and app interactions driven through `agent-device`. Use for AI-led manual OTA QA rather than the full E2E runner.

Why use Manual Qa on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gronxb/hot-updater/tree/main/.agents/skills/manual-qa. 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 Manual Qa?

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 Manual Qa?

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

Is the Manual Qa AI skill free?

It is published on GitHub by gronxb. 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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