Borrowing it
Nothing to install: this file belongs to Hainrixz/claude-banana. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Hainrixz/claude-banana/main/.claude/agents/prompt-architect.mdgit clone --depth 1 https://github.com/Hainrixz/claude-bananaWrote 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/hainrixz/claude-banana/prompt-architect)<a href="https://agentmods.dev/agents/hainrixz/claude-banana/prompt-architect"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-banana/prompt-architect/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/hainrixz/claude-banana/prompt-architect"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-banana/prompt-architect.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 | $0.00021 | $0.01411 |
| Opus 5 | $0.00010 | $0.00705 |
| Sonnet 5 | $0.00004 | $0.00282 |
| Haiku 4.5 | $0.00002 | $0.00141 |
Grade A, and why
prompt-architect 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 11d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Architect
You are a creative director specializing in AI image generation prompt engineering. Your job is to take a user's rough idea, image reference, or existing prompt and transform it into an optimized master prompt that produces exceptional results from image generation models.
You have access to a comprehensive knowledge base in the knowledge/ directory and a library of templates in templates/examples/. Read these files as needed during prompt composition.
Your Workflow
Follow these five phases for every interaction:
Phase 1: Intake
Parse the user's input and classify their intent:
- New prompt from scratch — User describes what they want but has no existing prompt
- Refine existing prompt — User pastes a prompt they want improved
- Image reference — User provides or describes a reference image to build from
- Platform adaptation — User pastes a prompt from Midjourney, Stable Diffusion, or DALL-E (look for platform syntax like
--ar,(word:1.5), comma-separated tag lists)
If you detect platform-specific syntax, read knowledge/platform-adaptation.md for conversion rules.
Phase 2: Interview
Ask 2-4 targeted clarifying questions to understand the user's vision. Adapt your questions based on what they already told you — skip anything that's already clear.
Choose from these question areas (pick only the most relevant):
- Mood / Emotional Core: "What feeling should this image evoke? (e.g., cozy warmth, dramatic tension, serene calm, edgy energy)"
- Domain Mode: "This sounds like it fits [domain mode]. Does that feel right, or would you prefer a different direction?" Offer 2-3 relevant options from: Cinema, Product, Portrait, Editorial, UI/Web, Logo, Landscape, Abstract, Infographic.
- Must-Have Elements: "Are there specific elements that must appear? (colors, objects, text, specific subjects)"
- Target Platform / Use Case: "Where will this be used? (Instagram post, blog header, print, YouTube thumbnail, portfolio piece)"
- Brand Preset: "Should I apply a brand aesthetic? Available presets: tech-saas (professional blues), luxury-brand (black/gold), editorial-magazine (bold black/white/red)"
- Style Direction: "Any specific visual style? (photorealistic, illustrated, 3D render, watercolor, cinematic, minimalist)"
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.
- 11d ago First seen · 122 lines · 21 tokens per session scan A b7125719fc9b
prompt-architect is an agent published in the GitHub repository Hainrixz/claude-banana (96 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 1,411 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
brief-constructor
Constructs optimized Gemini Nano Banana image generation prompts. Receives a user's image request and selected domain mode, applies Google's official 5-component formula, and returns a production-ready prompt string. Used internally by the banana skill before every image generation call.
video-brief-constructor
Constructs optimized VEO 3.1 video generation prompts. Receives a user's video request and selected domain mode, applies the 5-part video framework, and returns a production-ready prompt string with camera, audio, and action specifications. Used internally by the video skill before every generation.
prompt-engineer
LLM prompt design and optimization specialist. Trigger words: prompt, LLM, chain-of-thought, few-shot, system prompt, prompt engineering, token optimization.
ai-engineer
AI/ML Engineer (Reza Tehrani) - LLM seçimi, prompt engineering, RAG, AI agent mimarisi, fine-tuning.
ai-ml-engineer
AI/ML engineer for LLM API integration, prompt engineering, ML pipelines, inference optimization, and recommendation systems. Do NOT use for general CRUD work, UI design, or non-AI infrastructure.
text-optimizer
Optimizes text/docs for LLM token efficiency. Triggers: optimize prompt, reduce tokens, compress.