Honest Review
Shared step of the marketing playbooks (make-static-ads, run-organic-social): how to...
Install
npx skills add gooseworks-ai/goose-skills --skill honest-review -g -a claude-code -y- Repository
- gooseworks-ai/goose-skills1.3k
- Category
- Marketing
- Updated
- 9d ago
- License
- MIT
- Context cost
- Lightabout 793 tokens
- Runs scripts
- No, instructions only
What it does
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'").
