Photo Abstract Editorial V3 skill

A skill that makes an editorial poster from a photo: the shot stays unchanged, with an abstract panel and a local title beside it.

  • 131 ★ on the repository
  • Last commit
  • AGPL-3.0
Skill

Minimal access

Why this level

  • The skill processes your photos and may call host-side image generation.
All reasons and checks

kwhi6693-web/photo-abstract-editorial

Install

In your terminal, with SkillFoxx CLI

npx skillfoxx add skills/photo-abstract-editorial-v3

Detects the agents on your machine, shows the access level and pins the version.

Other ways to install

Access and checks

How we review

Minimal accessThe tool runs nothing and makes no network calls.

  • The skill processes your photos and may call host-side image generation.

This is third-party code. Review the repository files before installing.

What it does

Photo Abstract Editorial takes a photograph and builds an editorial layout from it. The skill picks a layout for the scene: portrait, landscape, architecture or still life. The source photo stays in the result unchanged, with an abstract panel derived from the shapes and colors of the frame beside it. The title is rendered locally in Strict Fidelity mode while the photo pixels remain exact. The user sets sliders from 0 to 100: abstraction, creative freedom, identity preservation and spatial fidelity. Before delivery the result goes through a validator. The skill is validated in Codex and in the DeepSeek Harness build, and on other hosts it depends on their capabilities.

Who it is for. Designers and post authors who need a series of covers from their own photos with one style and control over abstraction.

Good fit when

  • You need an article cover where the frame itself is not distorted.
  • A photo series must come out in one editorial style.
  • The poster title has to be drawn by a precise local font, free of generation distortion.
  • A portrait has to be styled so the face stays recognizable.

Not a fit when

  • You need a full redraw of the frame in another style.
  • The photo itself has to be redrawn or retouched.
  • The agent runs on a host without image generation.

Example request

Create an adaptive editorial poster from this photo, abstraction 60, identity preservation 80.

Limitations

The license is AGPL-3.0, which matters when embedding into closed products. The skill is validated in Codex and on the DeepSeek Harness path, other hosts depend on their capabilities, and some steps use image generation. The repository weighs about 83 MB because of examples, so a release archive is a better starting point. An Original mode exists and works in Codex only. The README gives no detailed environment requirements, so test on one photo before a series.

How to disable. Delete the skill folder from the agent skills directory. Finished posters are stored separately and are unaffected.

FAQ

What stays unchanged?

The source photograph. In Strict Fidelity mode the validator confirms the photo region in the result matches the original pixel for pixel.

Which settings exist?

Four parameters from 0 to 100: abstraction, creative freedom, identity preservation and spatial fidelity.

How does V3 differ from Original?

V3 adapts the layout to the scene and accepts settings. Original is kept as a fixed workflow and works in Codex only.

How do I install the skill?

Download the release archive and install it, or ask the agent to install the skill through skill-installer from the repository link.

Does Claude Code work?

The README names Codex and DeepSeek Harness as validated. For Claude Code compatibility depends on host capabilities and is not stated separately.

Discussion

No questions or reviews yet.

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README in short

The README shows before and after pairs, a five-result gallery and a table comparing Original with V3. Installation takes one agent request and the Strict Fidelity mode locks the photo exactly.

README badge

Original SKILL.md

---
name: photo-abstract-editorial
description: Use when one or more photographs must become adaptive photo-plus-abstraction editorial compositions while source facts, spatial relationships, and strict photo preservation remain controlled.
---

# Photo Abstract Editorial

Create a lightweight editorial composition from a supplied photograph. The photograph remains the sole factual source; generated content is an abstract motif, never a replacement for the final photo region or exact title.

## Capability routing

Choose the highest-fidelity mode the host can actually execute:

- **Strict Fidelity:** host visual-understanding capability, host image-generation capability, local filesystem access, local Python/script execution, Pillow-compatible image processing, and a usable serif font. This mode deterministically assembles photo, panel, motif, and typography, then runs machine validation.
- **Native Image Edit:** host visual-understanding capability plus host image-editing/composition capability, without the complete local Strict toolchain. Use the source as the edit/reference image and disclose that pixel-exact, deterministic, machine-verified photo preservation is unavailable.
- **Reference Generation:** host visual-understanding capability, host image-generation capability, and reference-image support. Use the source as the sole factual reference, perform best-effort fidelity, and disclose the same Strict guarantees are unavailable.

If visual understanding or every image-generation/editing capability is absent, stop rather than fabricate a result.

## Workflow

1. Validate the required image input and inspect the source.
2. Classify the scene and resolve the four controls in [control-system.md](references/control-system.md) using [scene-profiles.md](references/scene-profiles.md).
3. Select a layout from [layout-profiles.md](references/layout-profiles.md) using scene facts, orientation, dominant axis, subject location, center of gravity, and negative space. Omitted layout falls back to Lower Editorial.
4. Read [art-direction.md](references/art-direction.md). Generate or edit only what the selected mode permits; keep the motif sparse, traceable, text-free, and source-derived. When the source supports a decisive vertical structure, use one primary and one subordinate irregular vertical stroke, unequal in height and width.
5. In Strict Fidelity, run [remove_chroma_key.py](scripts/remove_chroma_key.py), [compose_editorial.py](scripts/compose_editorial.py), and [validate_editorial.py](scripts/validate_editorial.py). Never overwrite source, motif, output, or manifest.
6. Run the structured eight-dimension QA in [quality-check.md](references/quality-check.md). Strict machine validation is necessary but not sufficient; visual/editorial QA is also required.
7. Apply at most one targeted correction. Re-run the relevant generation/composition, machine validation, and QA; stop after the second attempt if a critical HARD FAIL remains.
8. For an explicitly requested multi-image series only, load [series-style.md](references/series-style.md) and lock the visual family while re-analyzing each new source.

The [validated example pair](references/example-pair.md) demonstrates package structure and regression expectations; do not copy its subject, palette, title, or coordinates.

Return the final artifact, saved path, selected mode, and concise QA result. Do not claim Strict guarantees for Native Image Edit or Reference Generation.

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