nexu-io/open-design

image-poster

Single-image generation skill for posters, key art, and editorial illustrations. Defaults to gpt-image-2 but is provider-agnostic — the same workflow drives Flux, Imagen, or Midjourney via the active upstream tooling. Output is one or more PNG/JPEG files saved to the project folder.

73CollectingRuns scriptsReads files
See how to use itView GitHub source
npx skills add https://github.com/nexu-io/open-design --skill "design-templates/image-poster"
Automated source guideContent productionStandard source

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Source-grounded content guide: image-poster

Produce one finished image asset per turn unless the user asks for variations. Image generation rewards a tight, structured prompt — your job is to assemble that prompt from the user's brief, then dispatch.

npx skills add https://github.com/nexu-io/open-design --skill "design-templates/image-poster"
Check the pinned source

The pinned source supports a structured brief, but not an expanded tutorial. Only detected inputs, outputs, and sections are shown.

375 source words · 7 usable sections

Content workflow

Read image-poster through these 4 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Workflow

The active project carries imageModel, imageAspect, and (optional) imageStyle notes. Use them as the upstream model + canvas + style anchor; only ask the user to fill them in if they're marked (unknown — ask).

SKILL.md · Workflow
Subject + composition — what is in the frame, where, at whatLighting + mood — natural / studio / moody; warm / cool; keyPalette + textures — hex anchors when the user gave a brand
02

Step 0 — Read the project metadata

The active project carries imageModel, imageAspect, and (optional) imageStyle notes. Use them as the upstream model + canvas + style anchor; only ask the user to fill them in if they're marked (unknown — ask).

SKILL.md · Step 0 — Read the project metadata
The active project carries imageModel, imageAspect, and (optional) imageStyle notes. Use them as the upstream model + canvas + style anchor; only ask the user to fill them in if they're marked (unknown — ask).
03

Step 1 — Compose the prompt

Plan in this exact order before calling any tool:

SKILL.md · Step 1 — Compose the prompt
Subject + composition — what is in the frame, where, at whatLighting + mood — natural / studio / moody; warm / cool; keyPalette + textures — hex anchors when the user gave a brand
04

Step 2 — Dispatch via the media contract

Use the unified dispatcher — do not call upstream provider APIs by hand. Run from your shell tool:

SKILL.md · Step 2 — Dispatch via the media contract
Use the unified dispatcher — do not call upstream provider APIs by hand. Run from your shell tool:The command prints one line of JSON: {"file": {"name": "...", ...}}. The daemon writes the bytes into the project folder; the FileViewer picks it up automatically.

Content checklist

Verify each item before delivery

The source section “Workflow” has been checked.

The source section “Step 0 — Read the project metadata” has been checked.

The source section “Step 1 — Compose the prompt” has been checked.

The source section “Step 2 — Dispatch via the media contract” has been checked.

Static permission evidence

Inspect the exact source lines that triggered a signal

These are source excerpts matched by deterministic rules, not findings of malicious behavior, safety, or actual execution.

Choose a different workflow

When another Skill is the better fit

FAQ

What does the image-poster source document cover?

Produce one finished image asset per turn unless the user asks for variations. Image generation rewards a tight, structured prompt — your job is to assemble that prompt from the user's brief, then dispatch.

How do I install image-poster?

The source record exposes this install command: npx skills add https://github.com/nexu-io/open-design --skill "design-templates/image-poster". Inspect the command and pinned source before running it.

Which permission-related actions were detected?

Static rules flagged exec-script, read-files in the source; the page lists the matching lines and excerpts.

Repository stars
82,073
Repository forks
9,485
Quality
73/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

73/100
Documentation26/30
Specificity11/25
Maintenance20/20
Trust signals16/25

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View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 2 min

Image Poster Skill

Produce one finished image asset per turn unless the user asks for variations. Image generation rewards a tight, structured prompt — your job is to assemble that prompt from the user's brief, then dispatch.

Resource map

image-poster/
├── SKILL.md         ← you're reading this
└── example.html     ← what the resulting card looks like in Examples

Workflow

Step 0 — Read the project metadata

The active project carries imageModel, imageAspect, and (optional) imageStyle notes. Use them as the upstream model + canvas + style anchor; only ask the user to fill them in if they're marked (unknown — ask).

Step 1 — Compose the prompt

Plan in this exact order before calling any tool:

  1. Subject + composition — what is in the frame, where, at what scale; eye-line and crop.
  2. Lighting + mood — natural / studio / moody; warm / cool; key plus rim plus fill; time of day if outdoor.
  3. Palette + textures — hex anchors when the user gave a brand palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").
  4. Camera / lens — only if the user wants photographic realism ("85mm portrait, shallow DOF") or a specific film stock.
  5. What to avoid — common AI-slop patterns ("no extra fingers, no warped text, no logo placeholders").

Step 2 — Dispatch via the media contract

Use the unified dispatcher — do not call upstream provider APIs by hand. Run from your shell tool:

"$OD_NODE_BIN" "$OD_BIN" media generate \
  --project "$OD_PROJECT_ID" \
  --surface image \
  --model "<imageModel from metadata>" \
  --aspect "<imageAspect from metadata>" \
  --output "<short-descriptive-name>.png" \
  --prompt "<the full assembled prompt from Step 1>"

The command prints one line of JSON: {"file": {"name": "...", ...}}. The daemon writes the bytes into the project folder; the FileViewer picks it up automatically.

Step 3 — Hand off

Reply with a one-paragraph summary of the prompt you used and the filename returned by the dispatcher (e.g. I generated hero-poster.png with gpt-image-2 at 1:1.). Do not emit an <artifact> tag.

Hard rules

  • One image per turn unless asked for variations.
  • Honor imageAspect exactly — the upstream cost is the same; matching the aspect avoids a re-render.
  • No filler typography in the image itself unless the user asked for in-frame text. Real copy beats lorem.
  • Save every render — never describe an image without producing the file. The user expects something to open in the file viewer.
Skill path
design-templates/image-poster/SKILL.md
Commit SHA
89d6d4ef21ba
Repository license
Apache-2.0
Data collected