Image poster
Skill onfire7777/universal-ai-skills-library/skills/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.From its SKILL.md
npx -y skills add onfire7777/universal-ai-skills-library --skill image-posterAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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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:
- Subject + composition — what is in the frame, where, at what scale; eye-line and crop.
- Lighting + mood — natural / studio / moody; warm / cool; key plus rim plus fill; time of day if outdoor.
- Palette + textures — hex anchors when the user gave a brand palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").
- Camera / lens — only if the user wants photographic realism ("85mm portrait, shallow DOF") or a specific film stock.
- 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.
What ships with it: 2 files
3.8 KB alongside SKILL.md
- example.html3.6 KB
- .openskills.json216 B