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event4u-app/agent-config

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.

94Collecting
See how to use itView GitHub source
npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/image-generation"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn image-generation's source instructions into a guide you can follow

According to the pinned SKILL.md from event4u-app/agent-config: 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.

npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/image-generation"
Check the pinned source

Best fit

  • 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.

Bring this context

  • A concrete task that matches the documented purpose of image-generation.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • Blueprint — provider choice + routing rationale + prompt string ready to copy.
  • Provider + prompt — adapter file reference, key params (aspect ratio, style,
  • Artifact path / dry-run note — the path returned by the adapter, or an explicit

Key source sections

Read image-generation through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

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 → Recr…

SKILL.md · Procedure
Capture the brief — extract: subject, output format (raster/vector/banner/icon),Route the provider via image-provider-routing —Author the prompt via prompt-engineering-image —
02

When to use

User asks to generate, create, or produce an image from a brief.

SKILL.md · 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.
03

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…

SKILL.md · Output format
Blueprint — provider choice + routing rationale + prompt string ready to copy.Provider + prompt — adapter file reference, key params (aspect ratio, style,Artifact path / dry-run note — the path returned by the adapter, or an explicit
04

Gotcha

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."

SKILL.md · Gotcha
Scaffold-tier adapters produce plans, not pixels — all four adapters (ideogram.sh,Seed reuse is the consistency lever — re-describing the subject more precisely- 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; actu…
05

Do NOT

Do NOT claim a rendered asset is produced while adapters are scaffold-tier — surface

SKILL.md · Do NOT
Do NOT claim a rendered asset is produced while adapters are scaffold-tier — surfaceDo NOT skip image-likeness-and-rights when the brief names a real person, a brandDo NOT bypass image-provider-routing — selecting the wrong provider for the job

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use image-generation to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned image-generation source to [task]. Pay particular attention to these source sections: “Procedure”, “When to use”, “Output format”, “Gotcha”, “Do NOT”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current image-generation result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Procedure” has been checked.

The source section “When to use” has been checked.

The source section “Output format” has been checked.

The source section “Gotcha” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

image-generation

Generate and edit images using Gemini Flash Image, and generate videos using Veo. Supports text-to-image, image editing, text-to-video, and image-to-video.

A separate implementation from sonichi/sutando; compare its source, maintenance signals, and permission requirements.

Open source detail

brand-landingpage

Brand-first landing page designer — runs a brand-identity interview (colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML. Use when the user asks to create, design, or build a landing page, homepage, or marketing page and has no established visual direction. Skip when they have a design mockup, need a dashboard or app UI, are working at component level, building a multi-page app, or restyling with known design tokens —

A separate implementation from wshobson/agents; compare its source, maintenance signals, and permission requirements.

Open source detail

prompt-engineering-image

Translate an image brief into provider-specific prompt grammar per model. Use when writing or refining an image-generation prompt for Ideogram, Flux, Gemini, GPT Image 2, or Recraft.

A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does image-generation do?

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.

How do I start using image-generation?

The catalog detected this source-specific install command: npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/image-generation". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
7
Repository forks
1
Quality
94/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

94/100
Documentation24/30
Specificity25/25
Maintenance20/20
Trust signals25/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

image-generation by sonichi

Generate and edit images using Gemini Flash Image, and generate videos using Veo. Supports text-to-image, image editing, text-to-video, and image-to-video.

brand-landingpage by wshobson

Brand-first landing page designer — runs a brand-identity interview (colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML. Use when the user asks to create, design, or build a landing page, homepage, or marketing page and has no established visual direction. Skip when they have a design mockup, need a dashboard or app UI, are working at component level, building a multi-page app, or restyling with known design tokens —

prompt-engineering-image by event4u-app

Translate an image brief into provider-specific prompt grammar per model. Use when writing or refining an image-generation prompt for Ideogram, Flux, Gemini, GPT Image 2, or Recraft.

videodb by affaan-m

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, v

ios-localization by dpearson2699

Implement, review, or improve localization and internationalization in iOS/macOS apps — String Catalogs (.xcstrings), generated localizable symbols, stable key naming, LocalizedStringKey, LocalizedStringResource, pluralization, FormatStyle for numbers/dates/measurements, right-to-left layout, Dynamic Type, and locale-aware formatting. Use when adding multi-language support, setting up String Catalogs, enabling generated symbols for compile-time-safe localization keys, handling plural forms, form

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 4 min

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

Skill path
src/skills/image-generation/SKILL.md
Commit SHA
0adf49a8ae84
Repository license
MIT
Data collected