agent-meme-forge

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

agent-meme-forge

(★ 53)

Codex skill that turns one image or text concept into a WeChat-ready Chinese sticker pack people will actually send. Sendability-gated, 4-keypose → 16-frame, no model-drawn text.

agent-meme-forge Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# agent-meme-forge

> **没人用的表情包就是垃圾。** A Codex skill that turns one reference image — or a text concept — into a WeChat-ready animated Chinese sticker pack that people will actually send.

[![License: MIT](https://img.shields.io/badge/License-MIT-black.svg)](https://raw.githubusercontent.com/lisamsung/agent-meme-forge/main/LICENSE)
[![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://raw.githubusercontent.com/lisamsung/agent-meme-forge/main/pyproject.toml)
[![Codex Skill](https://img.shields.io/badge/codex-skill-ff4f64.svg)](https://raw.githubusercontent.com/lisamsung/agent-meme-forge/main/skills/generate-meme-gif-pack/SKILL.md)
[![中文 README](https://img.shields.io/badge/README-中文-green.svg)](https://raw.githubusercontent.com/lisamsung/agent-meme-forge/main/README.zh-CN.md)
[![Live demo](https://img.shields.io/badge/demo-GitHub%20Pages-2563eb.svg)](https://lisamsung.github.io/agent-meme-forge/)

Three stickers from a 24-pack for <code>法律硕士毕业求职人</code>: HR-read silence, summoning an offer, and giving up to sleep. All produced from clean 4-keypose sheets and rendered locally to 16-frame 240×240 GIFs.

---

## Why this exists

Most "AI sticker pack" tools optimize for *cute*. Cute doesn't get sent in WeChat. **Sendable** does.

Every sticker this skill plans must clear a `sendability_gate`:

1. **Reuse trigger** — a real chat moment where someone would actually send it.
2. **Emotional value** — relief, sarcasm, agreement, panic, comfort, delay.
3. **Creative hook** — an idea, not just a caption pasted on a face.
4. **Visual gag** — readable at 240×240, on a phone, in one second.

If a sticker is only decorative, only cute, only "vibe" — the skill is told to rewrite it before generation, not after.

## What it builds

```
your reference image  ┐
   or text concept    ├──▶  plan ──▶  4 keyposes  ──▶  16-frame GIF  ──▶  WeChat upload pack
   + persona + style  ┘    (JSON)   (one image_gen      (local render,         (24 items,
                                     call per sticker)   Chinese captions)      manifest, QC report)
```

A finished build looks like this:

```
output/my-pack/
├── named-gifs/                # 收到离线.gif, 加载中.gif … (for sharing)
├── wechat-submit/
│   ├── main/01.gif … 24.gif   # numbered upload sequence
│   ├── thumbs/01.png … 24.png # 120×120 thumbnails
│   ├── cover.png  icon.png  banner.png
│   └── reward-guide.png       # optional, when 接受赞赏 is enabled
├── preview.html               # browser preview of the whole pack
├── manifest.json / .csv       # full machine-readable build manifest
└── qc_report.json             # per-sticker QC + continuity gates
```

Under the hood:

- **Plan** — character card + 24 sendable meme entries + per-sticker no-text `image_gen` prompts.
- **Render** — 4 keyposes → 16 deterministic frames, using motion templates (`soul_offline`, `loading_loop`, `pretend_understand`, …) with local effects (soul puffs, loading dots, sweat drops, awkward lines). The image model never has to draw 16 coherent frames.
- **QC** — fake-checkerboard detection, edge-touch, bbox drift, prop position/area jumps, one-frame prop flicker, face/head-shape drift, loop closure, low-motion-energy guard. Strict mode for WeChat submission.
- **Pack** — 16 or 24 GIFs at 240×240 < 500 KB, thumbnails, cover, icon, banner, dual filename schemes (numbered for upload, Chinese for sharing), CSV + JSON manifest.
- **Submit (optional)** — Playwright + headed Microsoft Edge runbook for the WeChat Sticker Open Platform, with QR-login, CAPTCHA, real-name, and rejection-flow handling.

## Quickstart

```bash
# 1. install
python3 -m venv .venv && . .venv/bin/activate
pip install -e '.[dev]'

# 2. plan a pack interactively
python skills/generate-meme-gif-pack/scripts/meme_pack.py plan-wizard

# 3. after you've generated and accepted the keypose sheets,
#    build the WeChat upload package
python skills/generate-meme-gif-pack/scripts/meme_pack.py build-pack \
  --source-dir output/raw-frames/MyPack \
  --output-dir output/my-pack \
  --persona 科研打工人 \
  --style clean-sticker \
  --pack-size 24 --mode wechat \
  --pack-name 我的表情包 \
  --source-mode keyposes --keypose-layout 2x2 --render-frame-count 16 \
  --quality-mode submission --strict-qc --strict-continuity-qc
```

Open `output/my-pack/preview.html` to inspect the full pack, then upload `wechat-submit/` to the WeChat Sticker Open Platform.

> **Tip — first 3 first.** Don't generate 24 keypose sheets in one go. Run `build-preview --preview-count 3` after the first three sheets pass `qc-sheet`. If they aren't sendable yet, fix captions or motion templates before scaling out.

## Install as a Codex skill

```bash
mkdir -p ~/.codex-switcher/skills
cp -R skills/generate-meme-gif-pack ~/.codex-switcher/skills/
# restart your Codex session
```

Then ask Codex something like *"做一个科研打工人的微信表情包"* — it will run intake, plan the pack, and walk you through keypose generation and QC across turns.

## How it works

The skill is built around two product decisions that are unusual for a sticker tool:

**1 · The image model only draws 4 stable keyposes per sticker.**
Asking a diffusion model to freely draw 16 coherent frames produces jumpy, unrelated frames most of the time. Instead, this skill asks for a clean `2×2` sheet of *start / anticipation / peak gag / loop return*, and a deterministic local renderer interpolates the 16 frames, holds, anticipation, rebound, and loop closure. Motion templates (`soul_offline`, `loading_loop`, `pretend_understand`, …) layer on the comic effects locally — soul puffs, loading dots, sweat lines — so the model can't randomly invent or skip them.

**2 · No Chinese text is ever drawn by the model.**
Captions are typeset by the local processor with consistent fonts, sizing, and clipping rules, so 24 stickers actually look like one pack. Visual prompts hard-ban text, captions, speech bubbles, UI, and logos.

The result: more reliable identity, fewer reroll cycles, predictable WeChat-spec output.

**Smoothest path — dense real frames (recommended).** Decision #1 keeps keyposes as the safe default that runs on any provider. When the image model is strong enough to draw ~8 genuinely different, consistent frames in a single sheet, the `dense_frames` source mode asks for exactly that — and a local pass normalizes every frame to a uniform size and head position, times them, and engineers the loop. It's the recommended path for maximum smoothness (expect a few stickers per pack to need regenerating). See **[dense real frames](skills/generate-meme-gif-pack/references/dense-frames.md)**.

## WeChat spec defaults

| Asset | Spec |
|---|---|
| Main GIF | `240×240`, looped, < 500 KB (target < 480 KB) |
| Thumbnail PNG | `120×120`, < 50 KB |
| Album icon | `50×50` transparent, < 30 KB |
| Cover | `240×240` transparent, < 80 KB |
| Detail banner | `750×400`, no text, < 80 KB |
| Pack size | **16 or 24** (this skill defaults to 24) |
| Optional reward guide | `750×560`, when `接受赞赏` is enabled |
| Optional reward thanks | `750×750`, when `接受赞赏` is enabled |

Always re-verify against the WeChat Sticker Open Platform docs before commercial submission — official rules change.

## Project layout

```
skills/generate-meme-gif-pack/
├── SKILL.md                 # agent rules and workflow
├── scripts/meme_pack.py     # plan → accept → qc → preview → build pipeline
└── references/
    ├── personas.md  styles.md  meme-library.md
    ├── prompt-rules.md      # raw image prompting + sendability gate
    ├── wechat-spec.md       # output constraints
    └── wechat-platform-upload.md   # optional Playwright submission runbook

docs/                        # GitHub Pages product site + sample assets
tests/                       # pytest suite (run with `pytest -q`)
```

## Docs

- [Live product page](https://lisamsung.github.io/agent-meme-forge/)
- [Skill rules and workflow](skills/generate-meme-gif-pack/SKILL.md)
- [Dense real frames — recommended for smoothness](skills/generate-meme-gif-pack/references/dense-frames.md)
- [Example test report](docs/example-test-report.md)
- [WeChat public-account draft (中文)](docs/wechat-public-account-draft.md)
- [中文 README](README.zh-CN.md)

## Status & contributing

- Test suite: `pytest -q` (covers WeChat size constraints, keypose planning, motion-template rendering, continuity QC, prop-flicker / face-drift gates, sheet splitting, caption layout, and full submission-pack structure).
- Verified end-to-end on four sample packs: 24 × `科研打工人`, 16 × `码农`, 18 × `都市丽人` (self-use), and a 24-pack of an original AI mascot in `2×4` strict-QC mode.
- WeChat platform rules can change at any time — please re-check the official help center before commercial submission.

Issues and PRs welcome, especially: new motion templates, persona libraries, or local effects.

## Image rights & content review

If your reference image is a real person, you must own the rights or have explicit permission. The default policy is "stylized but recognizable", **not** photo-realistic face swap. For public publishing, avoid politics, hate, sexual content, slurs, doxxing, medical claims, and harassment — WeChat review will reject these and so will this skill.

## License

[MIT](LICENSE)

agent-meme-forge

agent-meme-forge Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

agent-meme-forge

agent-meme-forge Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README