Best for
- 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.
Source profileQuality 94/100
event4u-app/agent-config/src/skills/image-generation/SKILL.md
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.
- Source repository stars
- 9
- Declared platforms
- 0
- Static risk flags
- 0
- Last source update
- 2026-08-28
- Source checked
- 2026-08-28
Decision brief
What it does: where it fits
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.
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.
Source-detected install commandSource
npx skills add https://github.com/event4u-app/agent-config --skill "src/skills/image-generation"Safe inspection promptEditorial
Inspect the Agent Skill "image-generation" from https://github.com/event4u-app/agent-config/blob/6a5670b7881a676c0da90d2afb950298087c4ccb/src/skills/image-generation/SKILL.md at commit 6a5670b7881a676c0da90d2afb950298087c4ccb. 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
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…
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.
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…
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."
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
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
Permission review
Static risk signals and limitations
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
Why each signal appears
EvidenceSourceComputedTestedEditorial
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 94/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 9 | 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
- event4u-app/agent-config
- Skill path
- src/skills/image-generation/SKILL.md
- Commit
- 6a5670b7881a676c0da90d2afb950298087c4ccb
- License
- MIT
- Collected
- 2026-08-28
- Default branch
- main
View the original SKILL.md
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
- Capture the brief — extract: subject, output format (raster/vector/banner/icon), style intent, any existing ref images or seed values for consistency.
- Route the provider via
image-provider-routing— match job shape (text-in-image → Ideogram, photoreal → Flux, vector → Recraft, general → Gemini/GPT Image 2). - 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). - 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.
- Invoke the adapter (dry-run today) — run
node_modules/@event4u/agent-config/src/scripts/ai-image/adapters/<provider>.shwith the assembled params. Validate the returned artifact path or dry-run confirmation. All adapters areexperimental(scaffold-tier); no live generation occurs until a maintainer promotes the adapter viaprovider-lifecycle-discipline. - 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 permedia-governance-routingbefore delivering the output.
Output format
- Blueprint — provider choice + routing rationale + prompt string ready to copy.
- Provider + prompt — adapter file reference, key params (aspect ratio, style, negative prompts), and any seed/ref-image value carried forward.
- 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 tostable. 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-rightswhen 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
image-provider-routing— select the right provider before writing the prompt.prompt-engineering-image— translate the brief into provider-specific prompt grammar.image-likeness-and-rights— rights check before generating real-person likenesses or brand marks.provider-lifecycle-discipline— lifecycle tier gates; read before any adapter invocation.
Frequently asked questions
What to verify before installation and use
What does the image-generation source document cover?
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 install image-generation?
The source record exposes this 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.
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