image-poster
nexu-io/open-design · Agent Skill
Generates images for posters, key art, and illustrations, utilizing various selectable models.
Jump to install ↓Install options
The review-first prompt is selected by default. You can switch to a direct command.
Copy the prompt, paste it into Claude, Codex, or another assistant, and it will review the skill page and install it.
I'd like to install this Claude Code skill: https://github.com/nexu-io/open-design/tree/main/design-templates/image-poster Please review the SKILL.md file, verify the skill is legitimate, then copy the skill folder into .claude/skills/ of my project.
A direct command skips the review — inspect the source first.
npx skills add https://github.com/nexu-io/open-design/tree/main/design-templates/image-poster
Read SKILL.md and any companion files before deciding whether to install. Click a file to preview it.
SKILL.md contents
Original author text · read-onlyImage 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. When a value is not provided, infer a safe default from the brief and
media contract. Ask only when the choice would materially change the requested
result and no safe default can be inferred.
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
imageAspectexactly — 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.