nexu-io/open-design/design-templates/image-poster/SKILL.md
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.
- Source repository stars
- 82,073
- Declared platforms
- 0
- Static risk flags
- 2
- Last source update
- 2026-07-28
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
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.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/nexu-io/open-design --skill "design-templates/image-poster"Inspect the Agent Skill "image-poster" from https://github.com/nexu-io/open-design/blob/89d6d4ef21baf80f871595abdf6f7de6e941dd44/design-templates/image-poster/SKILL.md at commit 89d6d4ef21baf80f871595abdf6f7de6e941dd44. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 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).
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).
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:
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:
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.
Permission review
Static risk signals and limitations
Runs scripts
The documentation asks the agent to run terminal commands or scripts.
hand. Run from your shell tool:Reads files
The documentation asks the agent to read local files, directories, or repositories.
file. The user expects something to open in the file viewer.Evidence record
Why each signal appears
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 73/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 82,073 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- nexu-io/open-design
- Skill path
- design-templates/image-poster/SKILL.md
- Commit
- 89d6d4ef21baf80f871595abdf6f7de6e941dd44
- License
- Apache-2.0
- Collected
- 2026-07-28
- Default branch
- main
View the original SKILL.md
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.
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