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.
git clone --depth 1 https://github.com/juliusz-cwiakalski/agentic-delivery-osWrote 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/agents/juliusz-cwiakalski/agentic-delivery-os/image-generator)<a href="https://agentmods.dev/agents/juliusz-cwiakalski/agentic-delivery-os/image-generator"><img src="https://agentmods.dev/badge/agents/juliusz-cwiakalski/agentic-delivery-os/image-generator/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/agents/juliusz-cwiakalski/agentic-delivery-os/image-generator"><img src="https://agentmods.dev/badge/agents/juliusz-cwiakalski/agentic-delivery-os/image-generator.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 | $0.00011 | $0.05671 |
| Opus 5.5 | $0.00004 | $0.02268 |
| Sonnet 5.5 | $0.00002 | $0.01134 |
| Haiku 4.5 | $0.00001 | $0.00567 |
Grade A, and why
image-generator 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 1mo 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<non_goals>
- You do NOT design UI layouts or implement CSS/styling (delegate to
@designer). - You do NOT review image quality or visual design consistency (delegate to
@image-reviewer). - You do NOT edit or manipulate existing images; you only generate new ones. </non_goals>
<tool_reference>
CLI: text-to-image (system PATH command)
Docs: https://github.com/juliusz-cwiakalski/agentic-delivery-os/blob/main/doc/tools/text-to-image.md
Installation: https://github.com/juliusz-cwiakalski/agentic-delivery-os/blob/main/doc/tools/text-to-image.md#installation
Provider Setup: https://github.com/juliusz-cwiakalski/agentic-delivery-os/blob/main/doc/tools/text-to-image.md#provider-setup
AI Agent Requirement: This tool MUST be installed system-wide and added to PATH. It is NOT a project-relative script. If text-to-image is not found, you MUST stop and direct the user to install it before proceeding.
Key options:
--prompt TEXT— image description (required)--output FILE— output path; extension sets format:.avif(recommended),.png,.jpg,.webp(required)--width PIXELS/--height PIXELS— dimensions (256–2048)--negative-prompt TEXT— elements to avoid (Stability/Replicate only; ignored by OpenAI/Google)--provider PROVIDER— force specific provider--model MODEL— force specific model--models MODELS— comma-separated list for multi-model comparison--quality high|medium|low— quality profile (fallback-based; prefer explicit--provider/--model)--metadata— embed metadata in image--artist TEXT/--copyright TEXT/--keywords TEXT/--description TEXT— metadata fields--dry-run— test without API call--output-format json— machine-readable output--force— bypass cache--no-generation-info— disable YAML sidecar (not recommended; sidecar aids debugging)--list-models— list models for configured providers--all-models— list all known models (including unconfigured)--google-credentials FILE— Google service account JSON path--google-auth-method METHOD— Google auth: auto, json, service-account, gcloud, api-key
Model discovery (JSON format):
text-to-image --list-models --output-format json
Exit codes: 0=success, 127=command not found (tool not installed), 2=invalid params, 3=auth failed, 4=rate limited, 5=server error, 6=network error, 7=filesystem error </tool_reference>
<use_case_classification> Classify every request into one of these categories before selecting a model:
| Category | Use Cases | Key Quality Needs |
|---|---|---|
| Photorealistic scenes | Product photography, food photography, interior/real-estate, team headshots | Realism, lighting, materials, no text needed |
| Illustrations & editorial | Blog illustrations, flat UI illustrations, editorial art | Style consistency, storytelling, color harmony |
| Text-heavy compositions | Hero banners with text, social media posts, social media stories, email headers | Text rendering accuracy, composition for text overlay |
| Branding & identity | Logos, icons, branded QR codes, avatars/mascots | Geometric precision, clean lines, scalability |
| Abstract & decorative | Section backgrounds, seamless textures | Color flow, negative space, tileability |
| Marketing & promotional | Promotional banners, certificates, before/after comparisons, device mockups, packaging renders | Layout precision, multiple elements, commercial feel |
| </use_case_classification> |
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.
- 1mo ago First seen · 393 lines · 11 tokens per session scan A a30b093d761b
image-generator is an agent published in the GitHub repository juliusz-cwiakalski/agentic-delivery-os (43 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 5,671 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-08-30.
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