make-static-ads-render-review

make-static-ads-render-review is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 68 tokens per session (1,645 once invoked), scanned A, original, MIT.

A review step for generated static ad images. It checks each render against a fixed judging guide, retries once when allowed, and records which versions pass or fail.

In plain words
What is it for?
It reviews ad images, records verdicts and notes, tags the creatives, and updates the batch record with any dropped versions.
Why use it?
It catches visual or rule violations before an ad is used and removes renders that still fail review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit It reviews ad images, records verdicts and notes, tags the creatives, and updates the batch record with any dropped versions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/make-static-ads-render-review
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,265 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gooseworks-ai/goose-skills --skill make-static-ads-render-review
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for make-static-ads-render-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-static-ads-render-review/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/make-static-ads-render-review)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/make-static-ads-render-review"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-static-ads-render-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for make-static-ads-render-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/make-static-ads-render-review"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-static-ads-render-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,645 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.01645
Opus 5.5 $0.00027 $0.00658
Sonnet 5.5 $0.00014 $0.00329
Haiku 4.5 $0.00007 $0.00164

Measured today against content hash 2113f4169e87, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

make-static-ads-render-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/marketing/make-static-ads-render-review/SKILL.md · 34 lines

How it starts

The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.

render-review

Shared principles for reviewing generated media (looking vs reading metadata, unseen images): the honest-review skill (../honest-review/SKILL.md). Checks and their severities: ../make-static-ads/references/review-rubric.md. This file is the one home of the retry rule.

Reads: each render (gooseworks:ads_creative_read creative_id, include: ["renders"]), its board row (level, style and template), its exact prompt and copy, profile.md with two of the brand's own ads, the official product photos or UI screenshots, and the logo. Writes: per render the verdict and the failed checks; retries; the drop list; tags; all of it in the batch file.

  1. Failed or empty render. Say so in one line. Read the job (gooseworks:job_get): charged, or no charge shown → it counts as this ad and ratio's first try; its one retry (step 5) is still covered; clearly uncharged → submit the same quoted request once more, outside the retry. If that fails too, stop for that ad and ratio and tell the marketer.
  2. Place the logo on every try (unless the marketer said none; then, as with no shell, the raw render is the kept version): on a shell host run ../make-static-ads/scripts/place_logo.py with the real logo file (corner and width as the prompt left clear), upload the result with gooseworks:media_upload (scope: "brand", kind: "image", source: {type: "bytes"}, base64, under 8 MB), and gooseworks:ads_save it with the raw render's creative as parent_creative_id. This logo version is what the judge sees, what gets the tags, and what the marketer receives; tag the raw render superseded. Record the box the script prints. No shell: the marketer's choice (playbook Step 2, or graphic-request step 3): a clear corner they fill, or the model's logo; the raw render is then the kept version.
  3. The judge. A fresh subagent gets only: the kept version (step 1), the product photos or screenshots and logo, the exact on-image copy, the board row's visual idea, style, protected choice, level, and the template's image with its description, profile.md with two of the brand's own ads, and the rubric. Not the conversation. It runs every rubric check except 1, 7, 12, 17, 19, 20, 21 and 29, and returns each failed check's number and severity with one sentence each. It works in the rubric's Order. For software, the screen must match the real screenshot in layout, colours and its large labels; soft small UI text is a NOTE, never a retry. No subagents → you judge from exactly those inputs, and the batch file says so.
  4. Your checks, on every try: 1 by Pass B of the make-static-ads-claims-extract skill (../make-static-ads-claims-extract/SKILL.md: invented badges, stars, prices, changed words); 7 by the price rule (../make-static-ads/references/claims-and-policy.md §2); 21 against the copy (12 and 17 ran at the copy step; re-run them only on changed copy); 20 against the permissions on file; 29 against the made-before list; for a direct competitor's template, 22's side-by-side with both logos covered.
  5. Verdict, in this order:
    • Label drift only (scene, shape and pose pass): the free repair first. A shell host may patch the official label's real pixels in with ../make-static-ads/scripts/repair_patch.py (../make-static-ads/scripts/repair_patch.md), then judge the patched image again in full; keep the original render. A seam, altered geometry or wrong product still fails.
    • A stray mark or small artifact outside the protected choice and the words: a free pixel fix on a shell host, then judged again in full.
    • A fault only in the copy around the image → fix the copy (free), run Pass A and the policy check on the new line; no re-render.
    • A BLOCK no re-render can fix (5, 20, a copied template) → drop the ad and say why, or offer a new board row with its own quote. A REMAKE no re-render can fix → judge:needs-review, shown to the marketer.
    • Try 1: any other BLOCK or REMAKE → retry (step 5); NOTEs only → pass. Never retry for logo drift (step 1 places the real logo) or tiny pack print.
    • Try 2: a BLOCK → drop the ad; a REMAKE → judge:needs-review, shown to the marketer with the reason; NOTEs only → judge:pass. Never a third render without a new quote and yes.
  6. The retry (one per ad and ratio, covered by the Step 7 yes at its quoted price). The same prompt plus one line: "Fix exactly this, change nothing else: ". Spell out misdrawn words letter by letter. A lost idea (the event didn't happen) is fixed by rewriting the scene sentences. A rewritten prompt or changed words go through Pass A and the policy check again, and changed words are shown to the marketer before it is sent. dry_run: true first; a price above the quote needs a new yes.
  7. Content refusal. The model refused → remove the line or visual that triggered it, re-check claims, show the marketer what changed, dry_run: true again (a new price needs a new yes), then send once more. That is this ad and ratio's retry.
  8. The set. After all renders: rubric check 19. Do two images say the same thing?
  9. Record. In the batch file: verdict per try, who judged, and each retry's creative id next to the one it replaces. The marketer gets one image per ad and ratio (the kept try). Tags on each kept version (step 1), as ../make-static-ads/references/handoff-format.md §1 lists (read current tags, merge, write once, lower-case), including judge:pass / judge:needs-review / judge:unjudged and try:1 / try:2; earlier tries get superseded, and a raw render only when a logo version replaced it. Render feedback follows the honest-review skill §5.

Read the full file on GitHub · 34 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +2 lines · -12 tokens per session 2113f4169e87
  2. 5d ago First seen · 32 lines · 80 tokens per session scan A 0086feffec21

Subscribe to this mod's changes

make-static-ads-render-review is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 1,645 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-21.

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