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 agentmods add skills/agentic-dev3o/devx-plugins/banana-peoplenpx skills add agentic-dev3o/devx-plugins --skill banana-peoplegit clone --depth 1 https://github.com/agentic-dev3o/devx-pluginsWrote 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/agentic-dev3o/devx-plugins/banana-people)<a href="https://agentmods.dev/skills/agentic-dev3o/devx-plugins/banana-people"><img src="https://agentmods.dev/badge/skills/agentic-dev3o/devx-plugins/banana-people.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.00948 |
| Opus 5 | $0.00023 | $0.00474 |
| Sonnet 5 | $0.00009 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
banana-people 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 4d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People & Portrait Photography
Input: $ARGUMENTS (optional — subject description and scene)
If $ARGUMENTS is provided, use it as the portrait brief and skip to prompt building. If empty, ask about the subject and setting.
Requirements
uvinstalledGEMINI_API_KEYenvironment variable set (get one at https://aistudio.google.com/apikey)
Workflow
- Understand — Parse the user's request. Identify the subject (age, build, expression), setting, and intended use. If the subject or intent is unclear, ask ONE clarifying question.
- Build prompt — Construct a detailed Gemini prompt using the Prompt Formula below. Always describe clothing, environment, and camera distance explicitly.
- Configure — Choose aspect ratio, resolution, and lens based on the Domain Defaults table. Use defaults unless the user specifies otherwise.
- Generate — Run the script.
- Deliver — Report the saved file path. Do NOT read the image file back. Offer to iterate: adjust pose, lighting, framing, or prompt.
Prompt Formula
Build every people/portrait prompt using this structure:
[Person: age/build/expression/clothing] + [Natural Action/Pose] + [Real-World Environment] + [Portrait Framing + Rule of Thirds] + [Natural Light Photography, specific lens]
Rules
- Positive framing: Describe what IS in the image, never what is absent.
- Strong verb opener: Start with Capture, Photograph, Shoot, Frame, Compose.
- Clothing and environment: Always describe what the person is wearing and the specific setting they are in.
- Camera distance: Specify explicitly — close-up, medium shot, full-body, three-quarter length.
- Mood keywords: Use "candid" for natural, spontaneous moments. Use "editorial" for styled, intentional compositions.
- No celebrity likenesses: Never reference real public figures. Describe features generically (e.g., "a person in their 30s with short dark hair").
Domain Defaults
| Domain | Aspect Ratio | Lens | Notes |
|---|---|---|---|
| Headshot | 3:4 | 85mm f/1.4 | Tight crop, shallow depth of field, subject fills frame |
| Editorial / Lifestyle | 3:2 | 35mm f/2.0 | Environmental context, wider framing |
| Group scene / Team | 16:9 | 24mm f/4.0 | Deep focus, everyone sharp |
| Resolution | 2K | All people domains default to 2K | |
| Lighting | Natural window light or golden hour | Soft, flattering, directional |
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.
- 4d ago First seen · 88 lines · 45 tokens per session scan A c9b1ce44ec83
banana-people is a skill published in the GitHub repository agentic-dev3o/devx-plugins (11 stars, last pushed 16d ago), licensed MIT. It adds 45 tokens to every session and 948 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 2026-08-30.
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