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 agentmods add skills/opendcai/gamefactory-3a/imagenpx skills add OpenDCAI/GameFactory-3A --skill imagegit clone --depth 1 https://github.com/OpenDCAI/GameFactory-3AWrote 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/opendcai/gamefactory-3a/image)<a href="https://agentmods.dev/skills/opendcai/gamefactory-3a/image"><img src="https://agentmods.dev/badge/skills/opendcai/gamefactory-3a/image.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.02409 |
| Opus 5 | $0.00000 | $0.01205 |
| Sonnet 5 | $0.00000 | $0.00482 |
| Haiku 4.5 | $0.00000 | $0.00241 |
Grade A, and why
image 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 5d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image preparation and T-pose generation Skill
Use this Skill when a game plan needs a single-subject concept image, a character reference cleaned for image-to-3D, or a front-facing transparent T-pose image for downstream character reconstruction. T-pose generation is an image-generation and image-editing task; it is not a separate asset category.
Scope and handoff
| Need | Route | Next step |
|---|---|---|
| Single-object concept art for reconstruction | <REPO_PATH>/models/gen_image/sdxl_turbo.py |
Review image, then use <REPO_PATH>/agent_skills/asset_qa/3d_object/SKILL.md |
| Convert an existing character image into a T-pose | <REPO_PATH>/pipeline/assets_gen/gen_tpose_image/run.py |
Use the transparent PNG as input to the selected 3D-object or motion workflow |
| Validate a generated T-pose | This Skill | Confirm pose, identity, silhouette, alpha, framing, and style before handoff |
Do not use multi-view sheets, collages, multiple characters, busy scenery, watermarks, floor shadows, or cropped limbs as input to an image-to-3D workflow. They tend to become fused geometry, baked texture, or missing body parts.
Models and pipeline
| Component | Location | Responsibility |
|---|---|---|
| Concept image model | <REPO_PATH>/models/gen_image/sdxl_turbo.py |
Fast single-object text-to-image concept art; tuned for reconstruction inputs |
| Image editor (local) | <REPO_PATH>/models/gen_image/qwen_edit_model.py |
Turns a supplied character reference into a white-background T-pose render |
| Image editor (cloud API) | <REPO_PATH>/models/gen_image/seedream_model.py |
Uses Seedream image editing as a swappable T-pose generation backend |
| Foreground extraction | <REPO_PATH>/models/tools/image_matting/rmbg_model.py or <REPO_PATH>/models/tools/image_matting/depth_anything_model.py |
Creates a foreground alpha mask |
| Task operator | <REPO_PATH>/operators/gen_tpose_image/operator.py |
Reads a task, generates the T-pose, saves artifacts and metadata |
| Runner | <REPO_PATH>/pipeline/assets_gen/gen_tpose_image/run.py |
Loads models, accepts CLI/JSONL tasks, and writes result summaries |
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.
- 5d ago First seen · 225 lines · 0 tokens per session scan A 75dc2d98968e
image is a skill published in the GitHub repository OpenDCAI/GameFactory-3A (360 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,409 tokens. 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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