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Manual Testing

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lucasfcosta
manual-testing

Verifies a feature works by running the real app, not by trusting green tests. Use when a backpressured loop reaches the before-done gate (Phase 3) and needs to confirm new behavior end-to-end — curling live API endpoints and driving a real browser via Playwright — including the cheap unhappy paths, before the task is called done.

Overview

Publisherlucasfcosta
Repositorybackpressured
Skill namemanual-testing
Stars
63
Forks
2
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 lucasfcosta on GitHub. Read the source before you install it.

Installation

Install the Manual 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/lucasfcosta/backpressured/tree/main/skills/manual-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/lucasfcosta/backpressured.git /tmp/backpressured
mkdir -p .claude/skills
cp -r /tmp/backpressured/skills/manual-testing .claude/skills/manual-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manual 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 Manual 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 Manual 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.

Manual Testing

Overview

You are the machine that uses the feature the way a client or a person will, so a human isn't the first to find out it doesn't actually work. Automated green is necessary, not sufficient: tests exercise the code, not the running system. Wiring, integration, env, and the error paths break in ways a passing unit test never sees. This is the "run it for real" gate.

Core principle: you only pass this on what you personally observed in the running app. Not "tests pass, so it works." Not "I curled the happy path." You booted it, you drove it, you saw each acceptance criterion — and the cheap failure cases — behave. Evidence, not inference.

When to Use

  • A backpressured loop's Phase 3, after automated checks are green, before the task is done. Always run it here — once before done, every time, even when the suite (including integration and end-to-end tests) is green. Automated tests are never a substitute (see Boundaries). The only thing that removes this gate is an explicit manual-testing opt-out in BACKPRESSURE.md's Skip section. It's slower than tests, so don't run it every iteration — mid-loop, run it only when something warrants it; but the before-done gate is not optional.

Not for: replacing automated tests or per-iteration checks (this is the end gate, not every patch). Gate by what exists: an API → curl it; a UI → drive the browser; genuinely nothing runnable (or an explicit BACKPRESSURE.md opt-out) → skip with a note saying why. A green test suite is not a reason to skip.

Step 1 — Get the app running, for real

Discover how to run it yourself — package.json scripts, Makefile, docker-compose, README, .env.example (or use project-specific run skills if they exist). Then actually boot it: start it in the background, tail the logs until it's genuinely healthy ("listening on :PORT", no startup errors), and note the URLs.

Set up the minimum real dependencies to exercise the feature: bring up the DB, run migrations, seed at least one real row, and obtain a valid auth token and a real id if the feature needs them. If it genuinely can't boot (missing secret, broken dep), report BLOCKED — do not fake it or skip to "tests pass."

Step 2 — Walk each acceptance criterion through the running system

Exercise every acceptance criterion against the live app, not the test suite.

  • API: curl -i (status line visible) each endpoint. Check the body shape, headers, and content the consumer depends on — not just a 2xx. (CSV is actually CSV with the right Content-Type, not an empty body or a stack trace.)
  • UI: drive a real browser via the Playwright MCP — navigate, fill, click the actual flow. Don't substitute a curl for a user action.
  • Transient states flicker. A state like "button disabled while submitting" is too fast to catch by default — throttle the network (or observe the disabled attribute toggling) so you actually see it, rather than assuming it.

Step 3 — Hit the cheap unhappy paths

The failure cases are where manual testing earns its keep over automated tests — and they're trivial by hand. Exercise the ones in the acceptance criteria and the obvious ones around them: error responses (400 / 401 / 403 / 404), empty or invalid input, missing fields, the permission-denied case. Confirm the app returns the status and the user-facing behavior the feature promises (the inline error renders; the toast shows), not a 500 or a silent no-op.

Step 4 — The UI: confirm function always, judge appearance only without a reference

Two different things — don't conflate them:

  • Functional state — always yours, reference or not. Confirm the states the criteria promise actually happen: the submit button's disabled toggles, the inline error text renders, the toast appears. Capture screenshots / DOM observations of these as evidence regardless of whether a design reference exists — this is functional verification, not styling.
  • Appearance — conditional. No design reference provided → take sanity screenshots and look for obvious breakage (blank page, broken layout, missing element). Judge only that it isn't visibly broken; something that merely looks off (placement, wording) is a finding, not a failure. A Figma/Linear reference exists → styling fidelity is not your job — defer it to [[visual-review]]; you still confirm the feature functions (the bullet above).

Step 5 — Record the evidence, then judge

"Tested manually, works" is not a passing check. Record the actual curl commands and their status/bodies, what you clicked in the browser, and what the screenshots showed. Passing = you personally observed, in the running app, every acceptance criterion and the cheap unhappy paths behaving correctly. Anything wrong is unresolved work: fix it and re-run, or stop and name the blocker — never wave it through or call it done off green tests.

Boundaries

  • vs [[visual-review]]: manual-testing = does it work; visual-review = does it match the design. Functional verification is always manual-testing's; only appearance judgment splits by whether a reference exists (Step 4).
  • vs the reviewer subagent: the reviewer reads the diff; you run it.
  • vs automated tests: this is the for-real pass that catches what tests miss — a complement, never a substitute.

Common rationalizations

RationalizationReality
"Tests pass, so it works"Tests exercise the code, not the running system. Wiring, env, and integration fail outside them. Run it.
"I curled the happy path, ship it"You didn't drive the UI or hit a single error path. The happy path is the case you already knew worked.
"Manual testing is slow, skip it this once"It's the before-done gate, not an optional extra. Run it every time, unless BACKPRESSURE.md's Skip section opts out — an ad-hoc "just this once" is not an opt-out.
"Integration/E2E tests already drive the app, so this is covered"They run automated, against the code, and share automated tests' blind spots. This gate still runs unless BACKPRESSURE.md explicitly opts out of manual testing.
"The disabled-while-submitting state is too fast to see"Throttle the network and observe it. "Too fast to see" is not "verified".
"No design reference, so I can't check the UI"You can still confirm it renders and works. Take sanity screenshots; look for obvious breakage.
"I tested it manually, it's fine"With no recorded commands/responses/screenshots, that's an assertion, not a check.

Red flags — STOP

  • Calling the task done off green tests without running the app.
  • Curl happy-path only — never drove the UI, never hit an error path.
  • Claiming a manual pass with no recorded evidence of what you ran and saw.
  • Skipping the gate "just this once" (the only legitimate skip is an explicit opt-out in BACKPRESSURE.md's Skip section).
  • Judging UI styling as pass/fail with no design reference (that's a finding, or [[visual-review]]'s job — not a manual-testing failure).

Frequently asked questions

What does the Manual Testing AI skill do?

Verifies a feature works by running the real app, not by trusting green tests. Use when a backpressured loop reaches the before-done gate (Phase 3) and needs to confirm new behavior end-to-end — curling live API endpoints and driving a real browser via Playwright — including the cheap unhappy paths, before the task is called done.

Why use Manual Testing on TypingMind?

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

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

Which AI models can use Manual 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 Manual Testing?

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

Is the Manual Testing AI skill free?

Yes. It is published on GitHub by lucasfcosta 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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