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 HK-hub/AgentSkills --skill gif-sticker-makergit clone --depth 1 https://github.com/HK-hub/AgentSkillsWrote 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/hk-hub/agentskills/gif-sticker-maker)<a href="https://agentmods.dev/skills/hk-hub/agentskills/gif-sticker-maker"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/gif-sticker-maker/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/hk-hub/agentskills/gif-sticker-maker"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/gif-sticker-maker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00075 | $0.01285 |
| Opus 5 | $0.00037 | $0.00642 |
| Sonnet 5 | $0.00015 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
gif-sticker-maker 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 9d 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.
This is a copy
100% identical to gif-sticker-maker — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GIF Sticker Maker
Convert user photos into 4 animated GIF stickers (Funko Pop / Pop Mart style).
Style Spec
- Funko Pop / Pop Mart blind box 3D figurine
- C4D / Octane rendering quality
- White background, soft studio lighting
- Caption: black text + white outline, bottom of image
Prerequisites
Before starting any generation step, ensure:
- Python venv is activated with dependencies from requirements.txt installed
MINIMAX_API_KEYis exported (e.g.export MINIMAX_API_KEY='your-key')ffmpegis available on PATH (for Step 3 GIF conversion)
If any prerequisite is missing, set it up first. Do NOT proceed to generation without all three.
Workflow
Step 0: Collect Captions
Ask user (in their language):
"Would you like to customize the captions for your stickers, or use the defaults?"
- Custom: Collect 4 short captions (1–3 words). Actions auto-match caption meaning.
- Default: Look up captions table by detected user language. Never mix languages.
Step 1: Generate 4 Static Sticker Images
Tool: scripts/minimax_image.py
- Analyze the user's photo — identify subject type (person / animal / object / logo).
- For each of the 4 stickers, build a prompt from image-prompt-template.txt by filling
{action}and{caption}. - If subject is a person: pass
--subject-ref <user_photo_path>so the generated figurine preserves the person's actual facial likeness. - Generate (all 4 are independent — run concurrently):
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_hi.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_laugh.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_cry.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_love.png --ratio 1:1 --subject-ref <photo>
What ships with it
7 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.
- 9d ago First seen · 128 lines · 75 tokens per session scan A 57002b47b7aa
gif-sticker-maker is a skill published in the GitHub repository HK-hub/AgentSkills (6 stars, last pushed 25d ago), licensed MIT. It adds 75 tokens to every session and 1,285 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gif-sticker-maker, differing in 0 lines, and is treated as a copy.
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