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 fluentlc/shiny-skills --skill image-to-prompt-skillgit clone --depth 1 https://github.com/fluentlc/shiny-skillsWrote 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/fluentlc/shiny-skills/image-to-prompt-skill)<a href="https://agentmods.dev/skills/fluentlc/shiny-skills/image-to-prompt-skill"><img src="https://agentmods.dev/badge/skills/fluentlc/shiny-skills/image-to-prompt-skill/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/fluentlc/shiny-skills/image-to-prompt-skill"><img src="https://agentmods.dev/badge/skills/fluentlc/shiny-skills/image-to-prompt-skill.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 | $0.00071 | $0.01810 |
| Opus 5.5 | $0.00028 | $0.00724 |
| Sonnet 5.5 | $0.00014 | $0.00362 |
| Haiku 4.5 | $0.00007 | $0.00181 |
Grade A, and why
image-to-prompt-skill 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 2mo 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image to Prompt Skill
Overview
Reverse-engineers uploaded images into two artifacts:
- Prompt Template — A reusable structured prompt with
[PLACEHOLDER]variables for generating similar images with different subjects. - Prompt Case — The concrete prompt with placeholders filled using actual content observed in the input image.
This skill is the reverse counterpart of image-creation-prompt-skill:
image-creation-prompt-skill= forward: text description -> structured promptimage-to-prompt-skill= reverse: image -> structured prompt + reusable template
Trigger Conditions
Activate when:
- User uploads an image (base64, file path, or URL) and asks for prompt generation
- Explicit requests: "根据这张图生成 prompt", "reverse engineer this image", "分析这张图片的风格", "拆解这张图的结构"
- Keywords: "image to prompt", "图片转 prompt", "prompt 模板", "逆向图片", "根据图片生成 prompt"
10-Step Visual Analysis Framework
Before generating output, systematically analyze the image across these 10 dimensions:
| Step | Dimension | What to Extract |
|---|---|---|
| 1 | Overall Style | Art movement, visual genre, aesthetic label (e.g., "graffiti collage poster", "minimalist flat illustration") |
| 2 | Color Scheme | Primary/secondary/accent colors, contrast level, saturation, palette type (monochrome, complementary, analogous, triadic) |
| 3 | Main Subject | Central figure/object: identity, pose, expression, clothing, accessories, physical traits, proportions relative to frame |
| 4 | Background Design | Environment, depth of field, background elements, layering, spatial relationship to subject |
| 5 | Text & Typography | All visible text content, font styles, sizes, orientations, languages, placement strategy, hierarchy |
| 6 | Composition & Perspective | Framing, rule of thirds, symmetry/asymmetry, diagonal lines, camera angle, focal point, visual hierarchy |
| 7 | Material & Texture | Surface qualities: paper, metal, fabric, digital smoothness, brush strokes, torn edges, grain, gloss/matte |
| 8 | Lighting & Mood | Light direction, intensity, shadows, highlights, emotional tone, atmosphere, time of day feel |
| 9 | Decorative & Auxiliary Elements | Icons, borders, geometric shapes, patterns, filters, overlays, vignettes, watermarks, corner decorations |
| 10 | Quality & Technical Parameters | Estimated aspect ratio, resolution cues, suspected AI model parameters (e.g., --ar 9:16 --v 5 --style raw) |
What ships with it
6 files 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.
- 2mo ago First seen · 123 lines · 71 tokens per session scan A b1fe5e9b95b1
image-to-prompt-skill is a skill published in the GitHub repository fluentlc/shiny-skills (17 stars, last pushed 5mo ago), licensed MIT. It adds 71 tokens to every session and 1,810 once invoked, about $0.0003 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.
image-to-prompt-skill compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| image-to-prompt-skillfluentlc/shiny-skills | Skill | 17 | 5mo ago | MIT | A | 71 | Claude Code, Codex |
| llm-app-patternsdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 54 | Claude Code, Codex |
| prompt-engineerJeffallan/claude-skills | Skill | 12k | 7d ago | MIT | A | 93 | Claude Code |
| prompt-optimizationlangwatch/langwatch | Skill | 5.0k | yesterday | Apache-2.0 | A | 105 | Claude Code, Codex |
| omh-model-optimizationrlaope/oh-my-hermes | Skill | 5.2k | yesterday | MIT | A | 84 | Claude Code, Codex |
| ideogram4digitalsamba/claude-code-video-toolkit | Skill | 2.5k | yesterday | MIT | A | 99 | Claude Code |
| prompt-library-for-startupsaws/agent-toolkit-for-aws | Skill | 3.4k | yesterday | Apache-2.0 | A | 228 | Claude Code |