Skip to content
cccskills
FreePublished on GitHub

image-generation

Generate an image from a brief — provider-agnostic blueprint then provider-specific translation, with ref-image/seed reuse for consistency. Use when generating/creating an image.

The English summary is being prepared. Showing the original description.

Works withGemini
See how to install

Files included (2)

  • SKILL.md5.2 KB
  • evals/triggers.json1.6 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

image-generation

Generate an image end-to-end: capture the brief, route to the right provider, author a provider-specific prompt, reuse seeds for consistency, invoke the adapter, and apply governance. All adapters are scaffold-tier (dry-run) until promoted to stable.

When to use

  • User asks to generate, create, or produce an image from a brief.
  • End-to-end image production (routing + prompting + adapter invocation + governance).
  • When a consistent series of images needs seed/ref-image reuse across renders.

Procedure

  1. Capture the brief — extract: subject, output format (raster/vector/banner/icon), style intent, any existing ref images or seed values for consistency.
  2. Route the provider via image-provider-routing — match job shape (text-in-image → Ideogram, photoreal → Flux, vector → Recraft, general → Gemini/GPT Image 2).
  3. Author the prompt via prompt-engineering-image — apply provider-specific grammar (text-literal first for Ideogram, noun-phrase for Flux, style: param for Recraft, natural language for Gemini/GPT).
  4. Reuse ref-image / seed for consistency — if the brief is part of a series, carry the seed value or ref-image path forward. Seed reuse is the primary consistency lever; re-describing the subject each time is not.
  5. Invoke the adapter (dry-run today) — run node_modules/@event4u/agent-config/src/scripts/ai-image/adapters/<provider>.sh with the assembled params. Validate the returned artifact path or dry-run confirmation. All adapters are experimental (scaffold-tier); no live generation occurs until a maintainer promotes the adapter via provider-lifecycle-discipline.
  6. Apply governance — run the rights check (image-likeness-and-rights) when the brief names a real person, brand mark, or living artist's style. Attach the AI-disclosure footer per media-governance-routing before delivering the output.

Output format

  1. Blueprint — provider choice + routing rationale + prompt string ready to copy.
  2. Provider + prompt — adapter file reference, key params (aspect ratio, style, negative prompts), and any seed/ref-image value carried forward.
  3. Artifact path / dry-run note — the path returned by the adapter, or an explicit note: "adapter is experimental (scaffold-tier) — dry-run plan only; no rendered asset until promotion per provider-lifecycle-discipline."

Gotcha

  • Scaffold-tier adapters produce plans, not pixels — all four adapters (ideogram.sh, flux.sh, recraft.sh, gemini-image.sh) are scaffold-tier (dry-run only). This skill produces the blueprint + dry-run confirmation; actual renders require a maintainer to capture a smoke trace and promote the adapter to stable. Claiming a rendered asset exists when no adapter is stable misleads the caller.
  • Seed reuse is the consistency lever — re-describing the subject more precisely in each prompt does not lock character or style; carrying the seed value or ref-image path forward does. Drop the seed and character drift is inevitable across a series.

Good example: Brief for a product-shot series → route to Flux (photoreal), author noun-phrase prompt, carry seed: 42 across all five renders, invoke adapter, note "experimental — dry-run plan only."

Bad example: Invoking the adapter and telling the user "here is your rendered image" while all adapters are still scaffold-tier.

Do NOT

  • Do NOT claim a rendered asset is produced while adapters are scaffold-tier — surface the dry-run caveat explicitly every time.
  • Do NOT skip image-likeness-and-rights when the brief names a real person, a brand mark, or a named living artist's style.
  • Do NOT bypass image-provider-routing — selecting the wrong provider for the job shape (e.g. Flux for a text-in-image logo) produces garbled output.
  • Do NOT ignore seed/ref-image values when consistency across a series is required — seed reuse is the only reliable consistency mechanism.

See also

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when reviewing UI for accessibility — WCAG 2.2 AA, keyboard nav, focus, ARIA, contrast, screen-reader semantics — even on 'is this a11y-OK?' or 'mach das barrierefrei'.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

Use when defining or auditing the activation event — aha-moment selection, retention correlation, falsifiable definition. Triggers on 'what is our aha moment', 'redefine activation'.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

Use when capturing an architectural decision — file naming, next ADR number, Status / Context / Decision / Consequences, index regen; fires even without saying 'ADR'.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

Adversarial critique — devil's advocate, stress-test, honest teardown ('poke holes', 'be brutal', 'was hältst du davon'); explicit request only. Routine code or design review → code-review.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

Use when reading, creating, or updating agent documentation, module docs, roadmaps, or AGENTS.md. Understands the full .augment/, agents/, and copilot-instructions structure.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

Use for an adversarial red-team / blue-team / auditor review of an AI agent's CONFIG + behaviour (rules, skills, MCP, hooks, permissions) — attack-chain → defensive-gap list, not a code audit.

The English summary is being prepared. Showing the original description.

event4u-app/agent-config12Updated Oct 21, 2026

See all skills by event4u-app

Report a problem with this skill