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gooseworks-ai

Honest Review skill

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.

$npx skills add gooseworks-ai/goose-skills --skill honest-review
View this skill on GitHubgooseworks-ai/goose-skills/skills/marketing/honest-review/SKILL.md
Pack stars
1,313
Pack installs
12k
Alyn Score
58/99
Updated
4 days ago
License
MIT
– views – installs via Alyn

Install Honest Review

Pick your agent. Every command is ready to paste.

$npx skills add gooseworks-ai/goose-skills --skill honest-review

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads honest-review when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the honest-review skill”. In Codex, type $honest-review.

The skill itself

SKILL.md

FolderGitHub

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-learning skill (../save-learning/SKILL.md).

Tips for Honest Review

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • It is part of a pack with 29 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Honest Review

What does the Honest Review skill do?

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.

How do I install Honest Review?

Run "npx skills add gooseworks-ai/goose-skills --skill honest-review" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Honest Review work in Cursor and Codex?

Yes. Honest Review uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Honest Review free?

Honest Review is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.

Who made Honest Review?

Honest Review is published by gooseworks-ai in the gooseworks-ai/goose-skills repository, together with 28 other skills.

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