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sticker-factory

sticker-factory is a skill for Claude Code from guanyang/super-publisher. It costs 65 tokens per session (1,090 once invoked), scanned A, original, MIT.

A workflow for creating a complete custom WeChat sticker pack from a theme or character idea. It generates a 4-by-4 sheet of 16 illustrations and packages the result for WeChat, a messaging app with sticker packs.

In plain words
What is it for?
Use it to make a themed sticker set from scratch or produce multiple related reaction stickers in one batch.
Why use it?
It removes the repetitive work of planning many matching stickers and preparing the image files in the format WeChat accepts.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./skills/wechat-sticker-maker/scripts/run.sh "PATH_TO_GENERATED_IMAGE" --output "OUTPUT_DIR" --layout 4x4.

Part of the super-publisher plugin — 10 skills shipped together

not rated 30repo +1 1mo ago A scan 65 tokens original MIT

Good fit Use it to make a themed sticker set from scratch or produce multiple related reaction stickers in one batch.

Compare 6 skills from other repositories ↓
View source ↗ guanyang/super-publisher
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/guanyang/super-publisher
agentmods
npx agentmods add skills/guanyang/super-publisher/sticker-factory

Made for: Claude Code.

Or install super-publisher, the plugin that ships this one along with the rest of its 10 skills.

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 sticker-factory

README.md
[![agentmods](https://agentmods.dev/badge/skills/guanyang/super-publisher/sticker-factory/github.svg)](https://agentmods.dev/skills/guanyang/super-publisher/sticker-factory)
Your own site
<a href="https://agentmods.dev/skills/guanyang/super-publisher/sticker-factory"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/sticker-factory/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 sticker-factory

Your own site · 80×15
<a href="https://agentmods.dev/skills/guanyang/super-publisher/sticker-factory"><img src="https://agentmods.dev/badge/skills/guanyang/super-publisher/sticker-factory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,090 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 $0.00065 $0.01090
Opus 5 $0.00032 $0.00545
Sonnet 5 $0.00013 $0.00218
Haiku 4.5 $0.00006 $0.00109

Measured 12d ago against content hash e6e8b474c3e6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

sticker-factory 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.

skills/sticker-factory/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sticker Factory (Auto-Generator & Packer)

This skill serves as an Automated Production Line that orchestrates the entire lifecycle of sticker creation: from AI generation to WeChat-compliant asset packaging.

Execution Workflow

Step 1: Generate Sticker Grid

Adhere to the Universal Grid Logic defined in skills/grid-sticker-generator/SKILL.md.

  • Prompt Construction:

    • Core: User's described character/theme.
    • Format Constraints (CRITICAL): Inherit all Visual Design Standards from grid-sticker-generator (4x4 Grid, No Text, Flat Vector).
    • Content Logic:
      • Default: Load the Office Worker Template (references/office_worker_template.md) from the generator skill to fill slots with office memes.
      • Custom: If user specifies a different theme, generate 16 varied actions relevant to that theme satisfying the grid constraints.

  • Action:

    • Call generate_image with the constructed prompt.

Step 2: Auto-Process with Sticker Maker

Once the image is successfully generated, immediately trigger the packaging robot.

  • Command:

    ./skills/wechat-sticker-maker/scripts/run.sh "PATH_TO_GENERATED_IMAGE" --output "OUTPUT_DIR" --layout 4x4
    • PATH_TO_GENERATED_IMAGE: The absolute path returned by the generate_image tool.
    • OUTPUT_DIR: If user specified a custom path, use it. Otherwise, default to output/stickers_[theme].

  • Metadata Auto-Population (CRITICAL): Before moving to the next step, the Agent must:

    • Automatically edit and fill in info.txt and meta.txt with actual names, descriptions, and sticker meaning words, replacing the template placeholders, based on the theme generated.
    • Copy the original generated grid image into the output folder (e.g. as source.png or grid_image.png).

Step 3: Generate Matching WeChat Sticker Assets

Orchestrate the design of matching promotional materials based on the generated IP character using skills/wechat-sticker-assets-designer/SKILL.md.

Read the full file on GitHub · 68 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. 12d ago First seen · 68 lines · 65 tokens per session scan A e6e8b474c3e6

Subscribe to this mod's changes

sticker-factory is a skill published in the GitHub repository guanyang/super-publisher (30 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,090 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.

Creative and mediaagentagentic-aiai-skillsautomated-publishingskillstoutiao-publisher
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