u/Pristine_Weight_4705

Honestly, I expected the closed models to win this pretty easily.

They did on realism—but not necessarily on art direction.

I ran the same fashion-editorial prompt through SenseNova U1.5, Nano Banana, and GPT Image 2. These are the first outputs—no rerolls, edits, or post-processing.

My take:

- Nano Banana wins on background detail. The station feels fuller and more believable.

- GPT Image 2 has the best atmosphere—darker, moodier, and more cinematic.

- SenseNova U1.5 gave me the strongest fashion-editorial look. The styling, composition, and color treatment feel the closest to an actual campaign.

Totally subjective, but for this specific fashion use case, U1.5 feels like it gets about 80% of the way to the closed models overall. And on art direction alone, I actually prefer it.

The remaining gap is mostly in realism: the face and skin still have a slightly plastic-looking AI sheen, while Nano handles the environment better and GPT feels more naturally cinematic.

If the goal is a fashion campaign rather than pure photorealism, I’d pick U1.5. It sells the outfit and art direction best, which is a pretty strong result for an open-source 8B preview model.

Obviously, one prompt isn’t a benchmark. I’ll put the full generation prompt in the comments.

Model links- SenseNova U1.5:

- https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT-Preview

- https://github.com/OpenSenseNova/SenseNova-U1

Would you trade some realism for stronger art direction, or does the plastic-looking skin already kill the U1.5 result for you?

I’ve been collecting Claude / agent skills for boring office work, and this is one of the more practical packs I found recently:

https://github.com/OpenSenseNova/SenseNova-Skills

Not affiliated. I just like skill packs that turn “please make this deliverable” into an actual workflow instead of one giant prompt.

Best Office Skills in the pack

1. Image & Visualization

This is probably the most immediately fun part.

sn-infographic turns a plain requirement into a structured infographic: content analysis, layout choice, visual style, and generation.

There is also sn-image-imitate, which is more interesting than a normal “copy this style” prompt. It extracts a layout blueprint first, rewrites the content while locking the visual structure, then reviews the result with a VLM and retries if needed.

Useful for:

• infographics
• explainer images
• report visuals
• social posts
• “make this ugly idea presentable” tasks

2. Presentations (PPT)

The PPT skills are split in a sensible way.

sn-ppt-standard is for editable business decks. Text stays editable, charts stay as native PPT objects, and the result is meant for follow-up edits.

sn-ppt-creative is for full-bleed visual presentations where each slide is more like a designed 16:9 scene.

That distinction matters. A lot of AI PPT tools make something that looks decent but is painful to edit.

3. Data Analysis (DA)

The data analysis workflow is not just “upload Excel and ask questions”.

sn-da-excel-workflow routes the job based on file size:

• under 10k rows: direct read
• 10k-100k rows: Parquet cache
• 100k+ rows: streaming large-file analysis

The large-file skill uses openpyxl read_only + iter_rows, chunked Parquet writes, and type downcasting to avoid the usual spreadsheet OOM mess.

Very unsexy. Very useful.

4. Deep Research

This one is closer to how research should be done.

sn-deep-research writes intermediate files instead of jumping straight from search results to final answer:

request.md -> plan.json -> sub_reports/*.md -> synthesis.md -> report.md

The nice part is that the synthesis step happens before the final report. So the model has to form a judgment first instead of just gluing notes together.

It also makes long research tasks resumable and easier to audit.

5. Search

The search skills are routed by evidence type:

• academic search
• code / GitHub / technical search
• English social search
• Chinese social search
• general web search

That is the right abstraction. Search should not be “one tool hits everything”. A paper, a GitHub issue, and a Reddit thread are different kinds of evidence.

Core insight

The useful part of skills is not that they save prompts.

It is that they turn repeatable office work into reusable workflows.

Decks, spreadsheets, research reports, and infographics all have hidden process. This repo tries to encode that process into skills instead of asking the model to improvise every time.

Worth starring if your Claude workflow includes actual deliverables, not just chat.

https://clawhub.ai/plugins/sensenova-skills