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 ondrej-svec/heart-of-gold-toolkit --skill linkedin-carouselgit clone --depth 1 https://github.com/ondrej-svec/heart-of-gold-toolkitWrote 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/ondrej-svec/heart-of-gold-toolkit/linkedin-carousel)<a href="https://agentmods.dev/skills/ondrej-svec/heart-of-gold-toolkit/linkedin-carousel"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/linkedin-carousel/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/ondrej-svec/heart-of-gold-toolkit/linkedin-carousel"><img src="https://agentmods.dev/badge/skills/ondrej-svec/heart-of-gold-toolkit/linkedin-carousel.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 | $0.00058 | $0.01531 |
| Opus 5 | $0.00029 | $0.00766 |
| Sonnet 5 | $0.00012 | $0.00306 |
| Haiku 4.5 | $0.00006 | $0.00153 |
Grade A, and why
linkedin-carousel 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 12d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Carousel — Babel Fish
Translating a stack of screenshots into a feed-ready document post. The trick isn't the PDF — it's making the padding invisible.
Boundaries
- MAY: read source images, run
magick, write PNGs and PDFs to the requested output path. - MAY NOT: upload to LinkedIn, post on the user's behalf, or modify the source images in place.
Prerequisites
- ImageMagick installed (
magickon PATH). Check withwhich magick. - Sources are raster images (PNG/JPG). For non-image inputs, ask the user to export first.
Phase 0 — Understand
Entry: User asked for a LinkedIn carousel.
Gather, asking only what's missing:
- Sources: ordered list of image paths (order = page order in the carousel).
- Slug: kebab-case filename for the PDF (e.g.
harness-lab-carousel). If the user gives a topic, derive it; otherwise ask. - Output path: default
thoughts/social-media/carousels/<slug>.pdfif the repo has that dir; otherwise ask. - Aspect preference: portrait (best feed performance), square, or landscape. If the user has no preference, infer from sources in Phase 1.
Exit: Source list, output path, and aspect intent are known.
Phase 1 — Plan
Entry: Inputs gathered.
Reason step-by-step before generating:
- Run
magick identifyon each source to get width × height. - Pick canvas aspect to minimize padding:
- User asked for portrait →
1080×1350(×2 =2160×2700). - User asked for square →
1080×1080(×2 =2160×2160). - No preference → if the sources cluster around one aspect, pick the median w/h ratio — this minimizes total padding across the deck. If sources span a wide range (e.g. 1.6:1 → 2.6:1), pick the aspect that makes the most pages padding-free, and accept that the outliers will get bands.
- User asked for portrait →
- Sample background color per page — the move that makes seams invisible. Need the dimensions first, then sample three corners:
If all three corners agree, use that color. If they disagree, the source has no clean border — fall back toread W H < <(magick identify -format "%w %h" "$src") magick "$src" -format \ "tl=%[pixel:p{5,5}] tr=%[pixel:p{$((W-5)),5}] bl=%[pixel:p{5,$((H-5))}]\n" \ info:#F4EFE3(or another neutral the user prefers) and tell them. - Render at 2× target resolution with Lanczos resampling. 300 DPI in the final PDF.
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.
- 12d ago First seen · 140 lines · 58 tokens per session scan A 40402de523e9
linkedin-carousel is a skill published in the GitHub repository ondrej-svec/heart-of-gold-toolkit (19 stars, last pushed 22d ago), licensed MIT. It adds 58 tokens to every session and 1,531 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 2026-08-30.
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