Agent Skills
› gooseworks-ai/goose-skills
› honest-review
honest-review
GitHub用于营销内容生成后的诚实审核流程，区分人工视觉检查与自动读取结果，客观报告差异，并在确认价格和次数限制后执行重做。
触发场景
营销素材生成后审核
广告图片视频质量检查
安装
npx skills add gooseworks-ai/goose-skills --skill honest-review -g -y
SKILL.md
Frontmatter
{
"name": "honest-review",
"version": "0.1.3",
"description": "Shared step of the marketing playbooks (make-static-ads, run-organic-social): how to review generated images and video honestly. Covers what was seen by eye versus read automatically, findings framed as things for the human to confirm, remakes only after a yes to the price, and at most two tries per item. Loaded by those playbooks; not for direct use."
}
honest-review
Used by: make-static-ads (its render-review step) and run-organic-social. The rubric — what to check — lives in each playbook; this atom is how to check and how to report.
1. Know what you can perceive
| You can | How | Say |
|---|---|---|
| See images | The host shows them, or a shell host downloads the file and opens it | "checked by eye" |
| Read an automatic description | gooseworks:media_analyze on the asset (description, visible text, for video a word-timed transcript and sampled shots) |
"the automatic read shows…" |
| Watch motion or hear audio | Almost never — a transcript and sampled frames are not watching | never claim it |
Decide the mode at the start and label every item with how it was checked. Never present the engine's own "passed" or "checked" as your review.
2. Compare against the truth, not against expectations
- Product: the brand's real product photos (curated — never an earlier generated image as the reference).
- Words: the approved text (script, the image brief's exact on-image text, on-image copy) word for word.
- Claims: the signed claims; anything drawn or spoken that is not on the approved text is a finding.
- When you cannot see: compare the automatic read of the output with the automatic read of the reference on named fields — part or item count, readable label or on-screen text, colour words, which product is in which scene — and report each mismatch specifically ("the reference reads 'DIVE DEEP' on the label; the output read shows 'DIVE DEEEP'").
3. Report findings honestly
- Each finding: what, where (region, scene, time), how it was found. From an automatic read: "the automatic read shows X at
— please confirm". Text absent from sampled frames is "not seen", never "missing"; a transcript difference may be the speech-to-text. - Items not checked by eye get a three-item checklist for the human, specific to that item ("zoom on the logo; count the lid loops; does the green match your Sage?").
- Never call generated media "ready" on automatic reads alone — "ready for your look" / "ready for your watch-through".
- Say plainly what only the human can judge (taste, motion, voice, pacing, music, overall feel).
4. Remakes
- Only for a defect that is real (seen by eye, or confirmed by the human).
- Only on a yes to the engine's quote for that remake, shown as given; never an unquoted paid edit, never your own price. A playbook may say its earlier yes already covers one retry at the quoted price; then that yes is enough for that one retry.
- At most two tries per item; then drop it (or hand the choice to the human) and say why.
- Changed words or claims in a remake are shown before it is made, even when an earlier yes covers the money.
5. Record
- Feedback to the engine (where the tool takes it): only the human's own words, and reason codes only for engine faults the human names (garbled text, wrong product, distortion); your own verdicts go in your record, not the feedback; strategy choices go in the comment.
- A durable lesson the human states becomes a brand rule only through the
save-learningskill (../save-learning/SKILL.md).
版本历史
- 1add09b 当前 2026-11-02 21:49
