Qa Execution logo

Qa Execution

Community
pedronauck
qa-execution

Run requested dogfooding through public product interfaces. Use existing QA journeys or qa-report to plan them; excludes routine code edits and CI-only verification.

Overview

Publisherpedronauck
Repositoryskills
Skill nameqa-execution
Stars
635
Forks
89
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Qa Execution 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/pedronauck/skills/tree/main/skills/mine/qa-execution
  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/pedronauck/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/mine/qa-execution .claude/skills/qa-execution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qa Execution 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 Qa Execution 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 Qa Execution 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.

Real-User QA Execution

QA the product the way a real person meets it: a persona walks a journey through the product's public interfaces, feels the friction, hits the edges, and reports what happened. This is dogfooding, not a scripted test pass — the session is the work, and the living QA docs tree remembers it.

Three non-negotiables hold every session:

  1. In persona. Every interaction and every verification goes through a surface a real user can reach — no dev-tools shortcut, no code-reading to decide what should happen, no patching over a stall.
  2. Proof, not optimism. A Pass is the expected observable seen, confirmed through an independent read path, surviving a refresh, with evidence captured. Optimistic UI is not confirmation.
  3. Write back or it didn't happen. Every session updates the tree — scenario-file verdicts, bug registry, and the dated report carrying the session debrief.

Input

  • qa-docs-path (optional): root of the living QA docs tree; defaults to docs/qa/. The tree is this skill's memory and its only output location — never a temp dir. If it doesn't exist, run qa-report first; it owns the tree and its bootstrap.

Steps

Choose the planned smoke/targeted/full scope. Read only the relevant procedure/schema sections and reuse current evidence for unchanged behavior. A small changed journey does not require all tours, edge categories, or a second full walk.

Step 1 — Resolve the tree, scope, and preconditions

  • Read, in order: <qa-docs-path>/README.md (entry points, dev-server command, area codes), the in-scope scenarios/ files, related open bugs/, and this cycle's charters. The tree is the memory; running without reading it recreates the duplication this design kills.
  • Scope: a branch/PR run covers the journeys its user-visible diff touches plus an adjacent canary when shared behavior could regress — no user-visible change, report that and stop. A release/full run covers the journeys the cycle plan marked in scope.
  • Preconditions: the applicable preconditions and existing required checks are satisfied (reuse current evidence; unrelated suites need not finish before a focused probe) and the product is reachable in a production-parity build (real dev server, real auth, no mocks). Not reachable → name the exact gap and stop.
  • Done when: scope is fixed and every precondition is met or its gap is surfaced.

Step 2 — Build the matrix and create the report now

  • Read references/status-and-reporting.md — it owns the six-value status enum and the report lifecycle.
  • Assemble the session matrix from the planned charters: persona × journey × tour × time-box, ordered by risk. A charter missing for an in-scope journey is drafted per ../qa-report/references/session-charters.md before running — never walk unplanned.
  • Create <qa-docs-path>/reports/<YYYY-MM-DD>-<scope>.md from the report template (project copy at <qa-docs-path>/templates/report.md, else assets/report-template.md) before the first session, with every matrix row Pending. This on-disk report is the source of truth for resume — update it after every session and every fix, never only at the end.
  • Done when: the report exists on disk carrying the full matrix, every row Pending.

Step 3 — Walk each journey in persona

  • Read references/session-protocol.md (the enter→act→verify→capture loop and the evidence standard) and references/persona-fidelity.md (the public-interface guardrails and stall-is-a-finding).
  • For each charter, in matrix order: adopt the persona (device, network, locale), enter through its real entry point, and walk the journey verb by verb to its true end state — verifying each step against the evidence standard.
  • Hunt paper cuts throughout: persona-felt friction no functional check fails; sharp ones become findings.
  • A leg only a human can complete (real payment, external email/SMS, real OAuth) is marked Blocked (needs human verify) with exact instructions — never faked.
  • Done when: every charter is walked to a recorded verdict, evidence captured at checkpoints and divergences, the debrief written to the report's Session Debriefs section, and the matrix row updated.

Step 4 — Run each tour and edge probe

  • Read references/tours.md (the 10-tour catalog and surface-to-tour matrix) and references/edge-cases.md (the non-technical user edge cases).
  • Run each charter's single tour against its surface, in persona, inside the box, asking at each action: "would this matter for this tour's theme?"
  • Choose distinct edge cases supported by the changed contract and risk; there is no minimum count. Attempted-and-clean is evidence too.
  • Done when: every charter's tour is run and its chosen edge cases are attempted and recorded.

Step 5 — Experiential lens pass

  • Read references/lenses.md — the six lenses and their severity defaults.
  • For a requested usability/full review or an unresolved experience risk, apply the relevant lenses to the affected journey. Reuse observations from the first walk and re-walk only the missing evidence.
  • Done when: the applicable experience risks have evidence, or this optional pass is not needed for the targeted scope.

Step 6 — File findings into the registry

  • Read ../qa-report/references/bug-registry.md — it owns ids, dedup, and the impact rubric.
  • Dedup first: search bugs/ and the affected scenarios' bug_ids. Re-found → append ## Re-found; regressed → reopen with ## Regressed; only a genuinely new symptom mints a new BUG-<YYYYMMDD>-<slug> id.
  • File with the user first — impact tier, persona, journey step, reproduction from the persona's entry point, evidence paths — then link the id into the affected scenario files.
  • Done when: every finding is deduped, filed, and linked to its rows.

Step 7 — Fix loop (governed)

  • Read references/fix-loop.md — the governor, the regression-test-per-fix rule, and Decisions for a Human.
  • Judge each fix against the governor before editing: only what passes all its bounds is auto-fixed, and each auto-fix has evidence at its owning suite/probe (add a regression only for an uncovered invariant) and re-walks the impacted journey, plus an adjacent journey when the failure can propagate. Everything else goes to the report's Decisions for a Human with options and a recommendation.
  • Done when: every finding is either fixed-and-retested or escalated with a recommendation, and no fix is left half-applied.

Step 8 — Close the round

  • Re-read the round-close checklist in references/status-and-reporting.md; map matrix verdicts to tracker enums per ../qa-report/references/state-schema.md.
  • Close the requested QA scope with the observed findings, coverage, and limitations. After fixes, run affected checks and any project checks required for that work, reusing valid evidence. When the request also includes PR delivery or release readiness, record the applicable delivery gates and current-head CI; missing or failed required evidence prevents that readiness claim, not an honest QA report.
  • Done when: zero matrix rows are Pending, scenario-file verdicts and bug statuses are current, every session's debrief is in the report, and Final Status states the QA outcome with totals by impact tier and evidence from the inspected build. State PR/release readiness only when that decision is in scope.

Companion skills

  • qa-report — plans what this skill runs and owns the tree's schemas (tracker, bug registry, charters, personas, journeys). Results written here feed the next cycle's planning.
  • agent-output-audit — owns CI gates, AI test-hygiene scans, task-status reconciliation, and flaky-test triage. A session that uncovers those files the finding and names that gate; it does not pivot mid-session.
  • agent-browser — the browser driver for Steps 3-5; its command surface lives in references/session-protocol.md.

Error handling

  • Dev server or browser tooling unavailable: mark the browser legs Blocked (needs human verify) with the exact missing prerequisite, and continue with CLI/HTTP journeys still walkable in persona.
  • A flow hangs: close the session, record it, retry once from a clean session, then mark it blocked. A stall is a finding to file, never a thing to nudge past (references/persona-fidelity.md).
  • Credentials or test data missing: mark those sessions blocked with the exact prerequisite and proceed with the rest.
  • Matrix larger than the window: cut by risk (Blocks-Completion candidates first, then Data-Loss, then Trust-Damage), mark the cut rows Skipped with reasoning, and disclose it in Final Status — coverage shrinks visibly or not at all.

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 Qa Execution AI skill do?

Run requested dogfooding through public product interfaces. Use existing QA journeys or qa-report to plan them; excludes routine code edits and CI-only verification.

Why use Qa Execution on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pedronauck/skills/tree/main/skills/mine/qa-execution. 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 Qa Execution?

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 Qa Execution?

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

Is the Qa Execution AI skill free?

It is published on GitHub by pedronauck. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇