---
name: Img2mo Learn
slug: img2mo-learn
category: AI Engineering
description: Img2mo Learn captures timing, pose, style, prompt, and failure lessons from reference motion assets into img2mo-knowledge/ for later /img2motion generation. Use it with videos, frame sequences, spritesheets, Spine assets, or prior outputs.
github: "https://github.com/WU-HAOTIAN34/2dimg2motion/tree/main/skills/img2mo-learn"
language: Python
stars: 186
forks: 5
install: "npx degit https://github.com/WU-HAOTIAN34/2dimg2motion/tree/main/skills/img2mo-learn ~/.claude/skills/img2mo-learn"
installs_to: ~/.claude/skills/img2mo-learn
source_path: skills/img2mo-learn/SKILL.md
collection_size: 3
category_size: 3278
collection_url: "https://dirskills.com/collections/WU-HAOTIAN34/2dimg2motion"
added: 2026-09-06T05:20:25.907Z
last_synced: 2026-09-06T05:20:25.907Z
canonical_url: "https://dirskills.com/skills/img2mo-learn"
---

# Img2mo Learn

Img2mo Learn captures timing, pose, style, prompt, and failure lessons from reference motion assets into img2mo-knowledge/ for later /img2motion generation. Use it with videos, frame sequences, spritesheets, Spine assets, or prior outputs.

**Install:**

```bash
npx degit https://github.com/WU-HAOTIAN34/2dimg2motion/tree/main/skills/img2mo-learn ~/.claude/skills/img2mo-learn
```

## README

# Img2mo-learn

## Overview

Use this skill when the user invokes:

```text
/img2mo-learn <resource-path-or-folder>
img2mo-learn <resource-path-or-folder>
```

The goal is to turn finished or reference motion assets into reusable project knowledge. Store learned knowledge in the project-level `img2mo-knowledge/` folder, never in the installed Codex skill directory during normal work.

## Input Resolution

Resolve the argument after `img2mo-learn` as follows:

1. If it is an existing relative or absolute path, use it directly.
2. If it is a bare name, first try `sample\<name>`, then `output\<name>`, then `motion\<name>`.
3. If it is a folder, inspect likely assets in this order: `manifest.json`, `preview.gif`, `contact-sheet.*`, `spritesheet.*`, `fullframe/`, `frames/`, Spine `.json/.atlas/.skel`, then videos.
4. If no matching resource exists, report the missing path and ask for the correct path.

Supported resources:

- video files such as `.mp4`, `.mov`, `.webm`;
- PNG frame folders, spritesheets, contact sheets, or GIF previews;
- project outputs from this skill such as `output/<action-id>/`;
- Spine-style assets such as `.json`, `.atlas`, `.skel`, texture folders;
- local reference-library folders under `motion/`.

## Knowledge Location

Create this structure if missing:

```text
img2mo-knowledge/
|-- index.md
|-- learnings.jsonl
|-- action-patterns.md
|-- style-patterns.md
|-- prompt-patterns.md
`-- failures.md
```

Append one JSON object per learning session to `img2mo-knowledge/learnings.jsonl`. Keep Markdown files concise and curated; do not paste huge logs, full prompts, or complete frame listings.

## Learning Workflow

1. **Identify the resource type.**
   - For video: read frame size, fps, duration, and frame count with `ffprobe` when available.
   - For frame sequences: count frames, inspect canvas sizes, alpha/background, and contact sheet if present.
   - For spritesheets: infer grid/cell count when possible; otherwise describe visible beats.
   - For Spine assets: inspect animation names, bone/slot names, skins, attachments, timeline names, and texture organization without assuming rendered motion if frames are not available.

2. **Create review surfaces if useful.**
   - For video or frame folders, create temporary or output-side contact sheets and preview GIFs if they do not exist.
   - Do not alter the source resource.
   - Do not store bulky extracted frames in `img2mo-knowledge/`; store outputs under `output/` or `tmp/` and reference their paths in JSON.

3. **Summarize motion timing.**
   - Identify action type: attack, walk, idle, block, suffer, death, born, skill/cast, or other.
   - Record frame count, fps, loop behavior, and major beats.
   - For attacks, prefer beat labels such as `guard`, `anticipation`, `acceleration`, `contact`, `contact hold`, `follow-through`, `recovery`.
   - Record which frame ranges are most useful as key poses.

4. **Summarize pose and topology lessons.**
   - Record active limb/feature, weapon or prop owner, anchor limb/surface, facing direction, and stable baseline behavior.
   - Note silhouette expansion, squash/stretch, center drift, foot/bottom baseline, and whether motion needs extra canvas margin.

5. **Summarize style lessons.**
   - Record line weight, palette, shading, material treatment, outline softness, shape language, effects style, and background/keying considerations.
   - Distinguish character style from detached effects.

6. **Summarize prompt lessons.**
   - Write reusable prompt clauses that could improve later generation.
   - Keep prompt clauses short and parameterized; avoid overfitting to one character name unless the lesson is character-specific.

7. **Summarize failures and constraints.**
   - Record what should be rejected: hand swaps, scale popping, bad alpha, cut weapons, over-crowded sheets, text/watermark contamination, or mismatched style.

8. **Write project knowledge.**
   - Append structured session data to `learnings.jsonl`.
   - Update the relevant Markdown files with durable, reusable lessons.
   - If the learning is only useful for one output, also write `output/<action-id>/retro.md`.

## JSONL Schema

Each line in `img2mo-knowledge/learnings.jsonl` should be a compact JSON object:

```json
{
  "id": "learn-YYYYMMDD-HHMMSS-short-name",
  "date": "YYYY-MM-DD",
  "source": "relative/or/absolute/path",
  "resource_type": "video|frame_sequence|spritesheet|spine|output|reference_folder",
  "action_type": "attack|walk|idle|block|suffer|death|born|skill|unknown",
  "fps": 24,
  "frame_count": 14,
  "loop": true,
  "beats": [
    {"name": "anticipation", "frames": "02-04", "notes": "body compresses before strike"}
  ],
  "key_pose_guidance": ["frame 05 should be the clearest contact silhouette"],
  "topology": {
    "active": "screen-left sword hand",
    "anchor": "screen-right hand",
    "weapon_owner": "screen-left hand"
  },
  "style": ["thick dark outline", "warm orange shadow shapes"],
  "prompt_clauses": ["same foot/bottom baseline in every cell"],
  "failure_lessons": ["reject sheets where the weapon hand swaps"],
  "artifacts": ["output/.../contact-sheet.jpg"]
}
```

Use `null` for unknown scalar fields and `[]` for empty lists. Keep one line per session.

## Markdown Update Rules

- `index.md`: list recent learning sessions and high-level tags.
- `action-patterns.md`: update durable motion timing rules by action type.
- `style-patterns.md`: update durable visual style observations.
- `prompt-patterns.md`: update short reusable prompt clauses and anti-clauses.
- `failures.md`: update rejection checks and known failure modes.

When adding Markdown entries, include the source path and date. Keep entries short enough that `/img2motion` can read them quickly.

## Use During Generation

Later `/img2motion` work must read `img2mo-knowledge/index.md` first when it exists. Then read only the relevant knowledge files for the requested action/style:

- action timing: `action-patterns.md`;
- visual style: `style-patterns.md`;
- prompt wording: `prompt-patterns.md`;
- known pitfalls: `failures.md`;
- detailed recent examples: search `learnings.jsonl` for matching `action_type`, source tags, or character style.

Do not let learned knowledge override the current user's explicit request or the current baseline image identity. Treat project knowledge as guidance, not ground truth.