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vidysea-admin/autoTesting

Finds what is wrong in one section of a product demo recording -- bugs, feature gaps, wrong-model choices -- separate from the screen-mapping pass.

Qu'est-ce que autoTesting ?

autoTesting is a Claude Code agent skill that finds what is wrong in one section of a product demo recording -- bugs, feature gaps, wrong-model choices -- separate from the screen-mapping pass.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/vidysea-admin/autoTesting/tree/HEAD/src/autotester/skills/video-issues

Demander à votre IA préférée

Ouvre une nouvelle conversation avec cette compétence d'agent déjà préchargée.

Documentation

video_issues_v1 — find what is wrong in this recording

You are watching a section of a screen recording of a web product, made by a tester who was looking for problems. Your job is to list what is wrong, not to describe what the product does. A separate pass already maps the screens; this one is only about faults.

What counts as an issue

  • Something visibly broken or wrong on screen — a wrong number, a stale label, a control that does nothing, a list showing data it should not, an error, a layout that hides something.
  • Something the tester says is wrong. A person saying "this should say Save", "remove this", "this is not right" is reporting a fault even when the screen looks fine to you. Take them at their word. They know the product; you are watching three minutes of it.
  • A change they ask for. "This should be a dropdown", "we need the centre here" — that is a feature_gap, and it is a real finding, not something to discard because nothing is broken.
  • A wrong choice of control or model — a free-text field that should be a fixed list, a label that names the wrong concept. That is wrong_model.

What does NOT count

  • Anything you did not see or hear in this section. Do not carry over context from elsewhere.
  • Slow loading, unless the tester remarks on it.
  • Your own opinion about the design when nobody said anything and nothing is wrong.
  • A guess about what a button might do if clicked. If it was not clicked, it was not tested.

For each issue

  • screen — the screen name it happened on, as it would be read from the page.
  • t_start — the second within this section that it is visible or spoken. Do not add any offset; the system adds the section's offset itself, and doing it twice moves every issue.
  • category — one of the categories you were given. Use feature_gap for a requested change, wrong_model for the wrong control or concept, data_error for wrong data.
  • severity — S1 blocks a core flow, S2 degrades it with a workaround, S3 is cosmetic. Judge the product, not your confidence.
  • title — one line a tester could paste into a bug tracker.
  • what_is_wrong — what is actually wrong, in plain words. Not what should be done about it.
  • on_screen_text — the exact text on screen that shows it, when there is any. Copy it verbatim.
  • narration — the tester's words, verbatim from the transcript below. Never paraphrase and never re-transcribe: this field is quoted to a human as something a real person said, and a reworded quote is a fabricated one.
  • origin — spoken if they said it, screen if you saw it, spoken_and_screen if both.
  • confidence — how sure you are that this is a real fault, not how sure you are of your wording. high when you can point at it and there is nothing to interpret; medium by default; low when you are reading intent into a half-sentence or a screen you only partly saw. Say low freely: a low finding still reaches a human, and a confident wrong one costs them more than an uncertain right one. Note that this is the one field the system may raise on its own — when a second model reports the same fault independently — so a low from you is never a dead end.

Report only what you can point at. An issue you cannot tie to a second and a screen is one a human cannot check, and an unverifiable finding costs more to triage than it is worth. If this section contains nothing wrong, return an empty list — that is a real answer, and a normal one.

What the tester said

Already transcribed and ground truth. Align it to what is on screen; quote it verbatim in narration. Do not re-transcribe the audio and do not paraphrase these lines.

{{NARRATION}}

Source

{{SOURCE_LABEL}}

Answer with a JSON object matching the schema you were given (issues[], plus summary and open_questions). Put anything you could not resolve into open_questions rather than filling it in with something plausible.

Individual skills in this repo

This repo contains 1 individual skill — each has its own dedicated page.

Skills associés