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 ur-grue/autopunk-media-skills --skill cover-image-pipelinegit clone --depth 1 https://github.com/ur-grue/autopunk-media-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/ur-grue/autopunk-media-skills/cover-image-pipeline)<a href="https://agentmods.dev/skills/ur-grue/autopunk-media-skills/cover-image-pipeline"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/cover-image-pipeline/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/ur-grue/autopunk-media-skills/cover-image-pipeline"><img src="https://agentmods.dev/badge/skills/ur-grue/autopunk-media-skills/cover-image-pipeline.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 | $0.00054 | $0.02424 |
| Opus 5 | $0.00027 | $0.01212 |
| Sonnet 5 | $0.00011 | $0.00485 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade A, and why
cover-image-pipeline 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 8d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cover Image Pipeline
What This Skill Does
Generates a complete, coordinated set of image prompts for a cover image concept across multiple formats and crop ratios — so a single visual idea produces consistent, properly composed assets for podcast artwork, YouTube thumbnails, newsletter headers, and social media simultaneously.
When To Use This Skill
- You have a cover image concept and need to generate it in multiple formats (square for podcast, 16:9 for YouTube, vertical for Instagram) without it looking like a crop of the same image
- You are launching a new episode, series, or publication issue and need a full suite of coordinated visual assets from one image session
- You want to ensure that text placement areas, focal points, and compositional choices are optimized for each platform rather than just resized
- You need consistent visual identity across a release campaign that touches multiple platforms simultaneously
What You Need To Provide
Required:
- The core image concept (what the image should show — subject, scene, mood)
- The visual style (or reference to your project's visual identity brief)
- Which formats you need: select from podcast square (1:1), YouTube thumbnail (16:9), Instagram portrait (4:5), Instagram/Facebook landscape (1.91:1), newsletter header (3:1 approximately), or specify custom ratios
Optional:
- Whether the image needs to accommodate text overlay (title, episode number) and where
- The image generation tool being used (Midjourney, Flux, DALL-E) — each has different prompt grammar
- Any existing assets to maintain consistency with (describe or reference your style brief)
- The project's color palette if defined
- Priority format (the one that must be strongest if trade-offs are needed)
How the Assistant Approaches This
- Analyzes the core image concept and identifies which visual element is the focal point — this determines how each format is composed, not just cropped
- Designs each format as an intentional composition, not a crop: the 16:9 version may use the wide aspect to show context the square version cannot; the square version may push the focal point to center in a way that would be static in landscape
- Writes a coordinated prompt set where all versions share a consistent subject, style, and palette anchor, but each prompt is written to optimize for its specific ratio and use context
- Notes text-safe zones for each format — where title text can be placed without covering the focal point
- Identifies the generation sequence: which format to generate first (usually the most compositionally complex), and how to use that result's style/seed to anchor the others
- Closes with a "Next Step" note: confirm the first format generation before moving on to the others, note the seed number after the first successful result, and review the full set at actual display sizes before finalizing
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.
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.
- 8d ago First seen · 161 lines · 54 tokens per session scan A 10eeb94541f7
cover-image-pipeline is a skill published in the GitHub repository ur-grue/autopunk-media-skills (32 stars, last pushed 12d ago), licensed MIT. It adds 54 tokens to every session and 2,424 once invoked, about $0.0003 per session on Opus 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 2026-09-04.
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