Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add event4u-app/agent-config --skill image-provider-routinggit clone --depth 1 https://github.com/event4u-app/agent-configWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/event4u-app/agent-config/image-provider-routing)<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-provider-routing"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-provider-routing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/event4u-app/agent-config/image-provider-routing"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/image-provider-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01167 |
| Opus 5 | $0.00019 | $0.00583 |
| Sonnet 5 | $0.00008 | $0.00233 |
| Haiku 4.5 | $0.00004 | $0.00117 |
Grade A, and why
image-provider-routing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
image-provider-routing
Route an image generation job to the right provider based on job shape. Adapters
are scaffold-tier (dry-run only) — selection produces a plan; execution waits on
promotion per provider-lifecycle-discipline.
When to use
- Choosing which image model fits a brief (logo, banner, photo, icon, general art).
- Before calling any
node_modules/@event4u/agent-config/src/scripts/ai-image/adapters/*.shadapter. - When the user asks "which image model should I use?" or "route this image job."
Provider decision table
| Job shape | Provider | Why |
|---|---|---|
| Text-in-image (logo, banner, typographic art) | Ideogram | Best glyph rendering; raster models garble text |
| Photoreal (product shot, portrait, scene) | Flux | Photorealism specialist; routed via fal/Replicate |
| Vector / SVG logo or icon | Recraft | Produces genuine <path> SVG; raster cannot |
| General art / edit / multimodal | Gemini-image or GPT Image 2 | Broad capability; natural-language prompts |
| Budget-conscious 4K upscale | Flux via Replicate | Cost-competitive at high resolution |
Decision order: text-in-image → Ideogram; must-be-vector → Recraft; photoreal → Flux; everything else → Gemini-image (default) or GPT Image 2 (when OpenAI key available and editing an existing image).
Procedure
- Extract job shape from the brief: does it need embedded text? vector output? photorealistic rendering? or general illustration?
- Apply table — match the dominant shape to the provider row above.
- Check lifecycle tier — all adapters are
experimental(scaffold). Surface this before any live invocation. - Confirm with the user when shape is ambiguous (e.g., logo that could be vector OR raster typographic art → ask once).
- Emit the routing decision in the Output format below.
Output format
- Chosen provider — name + one-line rationale citing the job-shape match.
- Lifecycle-tier caveat — "adapter is experimental (scaffold tier); dry-run
only until a smoke trace is captured per
provider-lifecycle-discipline." - Fallback — if the primary adapter is unavailable or the smoke trace is missing, name the next best provider and what changes.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 100 lines · 39 tokens per session scan A 460144757f21
image-provider-routing is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,167 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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