mphinance/alpha-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.

First seen May 9, 2026

Installation

$ npx skills add mphinance/alpha-skills --skill image-poster

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 21
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,474 B
  • docs SUMMARY.md 305 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 83 installs

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:

  1. Subject + composition — what is in the frame, where, at what

scale; eye-line and crop.

  1. Lighting + mood — natural / studio / moody; warm / cool; key

plus rim plus fill; time of day if outdoor.

  1. Palette + textures — hex anchors when the user gave a brand

palette; otherwise a 3-word mood tag (e.g. "muted ochre + ink").

  1. Camera / lens — only if the user wants photographic realism

("85mm portrait, shallow DOF") or a specific film stock.

  1. 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:

node "$OD_BIN" media generate \
  --project "$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.