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 Galbaz1/video-research-mcp --skill image-generationgit clone --depth 1 https://github.com/Galbaz1/video-research-mcpWrote 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/galbaz1/video-research-mcp/image-generation)<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/image-generation"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/image-generation/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/galbaz1/video-research-mcp/image-generation"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/image-generation.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 | $0.00045 | $0.01547 |
| Opus 5.5 | $0.00018 | $0.00619 |
| Sonnet 5.5 | $0.00009 | $0.00309 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
image-generation 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 25d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Prompt Best Practices
This plugin does not bundle an image-generation tool. Discover the active provider, inspect its schema, and confirm spend/attempt limits before requesting images. Provider-specific parameter names such as inputImagePath are not portable.
Verified on 2026-09-29: Google documents gemini-3.1-flash-image for general generation/editing, gemini-3.1-flash-lite-image for inexpensive 1K work, and gemini-3-pro-image for complex design. See official image guidance for exact API, supported references, sizes, and pricing; use the provider available to the session.
Prompt Structure
Enhance every image generation prompt around three core elements:
1. SUBJECT (What)
The main focus of the image.
- Physical characteristics: textures, materials, colors, scale
- Actions, poses, expressions if applicable
- Distinctive features that define the subject
2. CONTEXT (Where/When)
The environment and conditions.
- Setting, background, spatial relationships (foreground, midground, background)
- Time of day, weather, atmospheric conditions
- Mood and emotional tone of the scene
3. STYLE (How)
The visual treatment.
- Artistic or photographic approach: reference specific artists, movements, or styles
- Lighting design: direction, quality, color temperature, shadows
- Camera/lens choices: specify focal length, aperture, and shooting angle when photographic
Core Principles
- Preserve intent -- Enrich the user's original vision, never override it
- Preserve constraints -- Describe desired elements positively and retain explicit exclusions using supported instructions or a negative-prompt field
- Specific over vague -- "golden hour sunlight at 15 degree angle" beats "nice lighting"
- Natural flow -- Weave elements into a single flowing description, not a bullet list
Enhancement Patterns
Hyper-Specific Details
Add concrete visual details where the user left gaps:
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.
- 25d ago Changed · +8 lines 26f409e46415
- 1mo ago First seen · 143 lines · 45 tokens per session scan A 54964f229e9c
image-generation is a skill published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed yesterday), licensed MIT. It adds 45 tokens to every session and 1,547 once invoked, about $0.0002 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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Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.
add-prompt
Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.