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.
npx skills add SupercmoHQ/superCMO-skills --skill adapting-formatsgit clone --depth 1 https://github.com/SupercmoHQ/superCMO-skillsWrote 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.
[](https://agentmods.dev/skills/supercmohq/supercmo-skills/adapting-formats)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/adapting-formats"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/adapting-formats/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.
<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/adapting-formats"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/adapting-formats.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00157 | $0.02769 |
| Opus 5.5 | $0.00063 | $0.01108 |
| Sonnet 5.5 | $0.00031 | $0.00554 |
| Haiku 4.5 | $0.00016 | $0.00277 |
Grade A, and why
adapting-formats 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resizing an ad
Take an image ad that already exists and produce it in the other shapes it has to run in.
Each shape is regenerated, not cropped. A crop cuts the headline in half and pushes the product out of frame; the ad is rebuilt for the new proportions with the original passed in as a reference, so it stays the same ad rather than becoming a different one.
Workflow
Step 1: Get the ad and the shapes
- The ad — the image file or URL, at the largest version the user has. Without it there is nothing to resize; ask for it and stop. Several ads at once is normal: each is read and planned separately, though they can all be generated in one call at the end.
- The shapes — the ratios, or the channel they run on. Where the user named a channel rather than
ratios, take them from
references/ratios-and-safe-zones.md. Where neither is given, ask which sizes they need, offering that table's channels and a free-text way out. - The product photo, where they have one — a clean shot of the product on its own. It is worth asking for: the ad may show the product small, angled or partly behind copy, and the photo holds its identity far better than the ad can.
Drop the shape the ad is already in. An ad supplied at 1:1 and asked for the Meta family needs
4:5 and 9:16 — regenerating its own 1:1 bills for a reconstruction of something the user already
has.
Step 2: Read the ad
Run image_analysis on it once, and separate what the ad is made of — the parts a resize moves, not a
description of the picture:
- The product — what it is, and whatever has to stay identical wherever it appears: its shape, colour and label, the text printed on it, or anything else that would give it away as redrawn.
- Every drawn string — captured word for word, wherever it sits: a headline, a support line, a call to action, a price or a badge. These are regenerated, so a word read wrong here is a word wrong in the ad.
- The background — the surface, the scene, the light direction and the perspective, since this is what gets extended into any new area.
- The look — the palette, the character of the type, and whatever else makes it read as this brand's ad.
- The layout — what the eye lands on first, and how the parts sit relative to each other.
What ships with it
3 files 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.
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.
- 1mo ago First seen · 190 lines · 157 tokens per session scan A 2d4dbfafe73a
adapting-formats is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (242 stars, last pushed 6d ago), licensed Apache-2.0. It adds 157 tokens to every session and 2,769 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
render-3d-product-showcase
Assemble a premium 3D product-showcase ad from a config — four beat clips (an orbiting hero rotation, a macro push-in, a physics reveal, a typographic close) normalized to the chosen backdrop colour, hard-concatenated in order, closed on a deterministic Playwright brand end card, and mixed under one instrumental bed…
render-airdrop-carousel
Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…
email-lifecycle-flow-writer
Step of the email-lifecycle playbook: writes one lifecycle flow as a build sheet for the brand's email tool, every message finished in the brand voice. Run email-lifecycle instead; not for direct use.
work-with-creators-creator-vetting
Vets creators you have already named from their public pages. Covers the median views of their last 12 to 20 posts (sponsored and organic apart), a read of their comments, how often they post, audience fit with how sure it is, about 12 months of brand-safety checks, rival partnerships and rules for regulated…
loop-engineering
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).