mcpbeat

Image Gen

notque/image-gen

AI image generation: Gemini and Nano Banana backends; single/series/batch workflows with prompt-to-disk.

17k tokens
context cost
the whole folder, loaded on every use
9
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
413
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/notque/vexjoy-agent --skill image-gen

The instruction itself

8 sections, as written by the author

image-gen

Backend-agnostic image generation workflow: single images, series with anchor-chain consistency, and batch pipelines. Two backends: Gemini (API) and Nano Banana (local scripts with post-processing).

Reference Loading Table

| Signal | Load These Files | Why |

|---|---|---|

| Every request (always load) | references/series-consistency.md | Anchor-chain and prompt-file-first rules apply to all generation |

| Every request (always load) | references/backend-selection.md | Mode decision required before every generation |

| Script output gemini | references/backends/gemini.md | Gemini API models, env vars, flags |

| Script output nano-banana | references/backends/nano-banana.md | Nano Banana subcommands, flags, aspect ratios |

Phase 1: Detect Mode and Load References

Run the backend detection script — it reads environment variables and outputs a single word:

python3 skills/content/image-gen/scripts/detect-backend.py

Output values:

  • gemini — GEMINI_API_KEY or GOOGLE_API_KEY is set
  • ask — no key found; ask the user which backend to use

Load references based on output:

  • Load references/series-consistency.md (always — applies to every generation).
  • Load references/backend-selection.md (always — needed to pick mode and script).
  • Load references/backends/gemini.md when output is gemini.
  • Ask the user to set GEMINI_API_KEY or confirm they want to use local scripts when output is ask.

Gate: references loaded, backend confirmed before Phase 2.

Phase 2: Write Prompt File

Write the complete prompt to disk before any API call. Prompt files serve as the generation record and the anchor-chain input for series — writing them first means the full intent is on disk before any quota is spent.

File naming:

  • Single image: prompts/YYYY-MM-DD-{slug}.md
  • Series: prompts/{series-name}-01.md, prompts/{series-name}-02.md, ...

Prompt file format:

---
model: gemini-3-pro-image-preview
aspect-ratio: 1:1
flags: []
---

Full prompt text here. Be explicit about subject, style, background, and constraints.

Create the prompts/ directory if absent:

mkdir -p prompts

For a series, write all prompt files before calling any generation script. See references/series-consistency.md for the anchor-chain algorithm and why this ordering prevents drift.

Gate: all prompt files written and reviewed before Phase 3.

Phase 3: Select Mode and Script

Use references/backend-selection.md to map the request to the correct script and subcommand.

| Use case | Script | Notes |

|---|---|---|

| Single image, Gemini | scripts/generate_image.py | --prompt flag |

| Batch from prompt file, Gemini | scripts/generate_image.py | --batch flag |

| Single or batch with post-processing | scripts/nano-banana-generate.py | Full flag set in backend ref |

| Series with anchor chain | scripts/nano-banana-generate.py with-reference | Load ref images from previous outputs |

| Post-processing only | scripts/nano-banana-process.py | crop, remove-bg, pipeline subcommands |

Gate: script and subcommand identified before Phase 4.

Phase 4: Generate

Call the selected script with absolute paths for output files — relative paths break when scripts run from different working directories.

For series generation, follow the anchor-chain sequence from references/series-consistency.md:

  • Generate image 1 with no reference.
  • Use output of image 1 as --reference for image 2.
  • Continue: each image references the previous output.

Show the full script output — the user needs status messages, warnings, and partial failure information.

Gate: script exits 0 before Phase 5.

Phase 5: Verify and Report

Visual inspection is mandatory. Read the generated image file to verify:

  • Subject matches the prompt
  • No unwanted watermarks, logos, or artifacts
  • Aspect ratio and framing are correct
  • No excessive padding or dark borders that need cropping

If visual inspection fails: regenerate with an adjusted prompt. Report the issue clearly before retrying.

Report to the user:

  • Output file path (absolute)
  • Image dimensions
  • Model used
  • Post-processing applied (if any)
  • Visual verification result

Report only what was requested. The user did not ask for style suggestions or additional generations.

Error Handling

| Error | Cause | Resolution |

|---|---|---|

| GEMINI_API_KEY not set | Missing env var | export GEMINI_API_KEY=your_key or export GOOGLE_API_KEY=your_key |

| No image in response | Prompt triggered safety filter or text-only response | Adjust prompt phrasing; check for policy-violating content |

| Missing dependency: google-genai | Package not installed | pip install google-genai pillow |

| Rate limit exceeded (429) | Too many API calls | Increase --delay; default 2s may be too aggressive on free tier |

| Content policy violation (400) | Restricted prompt content | Rephrase using neutral language; this restriction is API-side |

| No image data in response | API returned text only | Set response_modalities=["IMAGE", "TEXT"] in config |

How to use it

Copy the folder

Take notque/image-gen from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.