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 mishahanin/heading-os-marketplace --skill image-promptgit clone --depth 1 https://github.com/mishahanin/heading-os-marketplaceWrote 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/mishahanin/heading-os-marketplace/image-prompt)<a href="https://agentmods.dev/skills/mishahanin/heading-os-marketplace/image-prompt"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/image-prompt/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/mishahanin/heading-os-marketplace/image-prompt"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/image-prompt.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 | $0.00101 | $0.01029 |
| Opus 5.5 | $0.00040 | $0.00412 |
| Sonnet 5.5 | $0.00020 | $0.00206 |
| Haiku 4.5 | $0.00010 | $0.00103 |
Grade A, and why
image-prompt 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.
This is a copy
100% identical to image-prompt — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Prompt Generator
Analyze the preceding text, article, or post and generate a detailed, platform-ready image prompt for AI text-to-image tools.
Process
- Identify the Central Idea - Distill the main concept or narrative into one sentence
- Extract Visual Elements - Find symbols, metaphors, settings, or scenarios that represent the content's essence
- Define the Mood - Match the emotional atmosphere: inspirational, urgent, contemplative, dynamic, warm, authoritative
- Construct the Prompt - Compose a 50-150 word description with subject, composition, lighting, color palette, atmosphere
Visual Element Selection for 31C Content
Match content type to visual language:
| Content Type | Visual Direction |
|---|---|
| Maritime/leadership metaphors | Sailing, navigation, open water, helm, compass, horizon |
| Product/technical | Networks, data flows, light particles, fiber optics, server rooms |
| Platform/building narrative | Architecture, bridges, foundations, construction, engineering |
| Expansion/market | Landscapes, coastlines, cities at dawn, maps, new horizons |
| Tribe/culture | Warm light, human silhouettes, collaborative spaces, depth |
| Sovereignty/independence | National landmarks, flags in wind, fortified structures, vaults |
| Speed/growth | Motion blur, launch trajectories, acceleration, time-lapse |
Default brand palette: deep blues, teals, warm amber accents.
Output Format
Deliver exactly this structure:
**Core Theme Identified:** [One sentence - the central idea of the source content]
**Image Prompt:**
[50-150 words. Specific, descriptive, platform-ready. Include: subject matter, composition,
lighting, color palette, atmosphere. Only tangible, photographable elements.]
**Parameters:**
- Aspect Ratio: 16:9
- Style: Photorealistic, high-resolution, natural lighting, authentic textures
- No text, typography, words, letters, numbers, or written elements
**Platform Suffix (Midjourney):** --ar 16:9 --v 6.1 --style raw --s 250
What ships with it
2 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 · 100 lines · 101 tokens per session scan A c6f0b34fece5
image-prompt is a skill published in the GitHub repository mishahanin/heading-os-marketplace (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,029 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to image-prompt, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
domain-modeling
Use when pinning down domain terminology, building a ubiquitous language or project glossary, disambiguating overloaded or vague terms, or maintaining a CONTEXT.md — or when another skill needs to sharpen the domain model. Do NOT use for recording architectural decisions or for writing implementation specs.
code-review
Iterative, human-in-the-loop code review that detects gaps, presents them for selection, and fixes them in cycles. Supports both diff-based PR review and holistic codebase analysis.
address-pr-review
Address reviewer and bot comments on a GitHub pull request — validate each comment, apply fixes where valid, reply with reasoning, and resolve threads.
interview
Deep requirements gathering through probing questions that surface hidden requirements, edge cases, and trade-offs before implementation.
ideate
Facilitate brainstorming as a thinking partner that extracts and expands latent ideas rather than generating them outright.
writing-artifacts
Use when writing or revising a durable written artifact — README, ADR, design doc, PR description, release notes, runbook, error message, user-facing docs. Gives a positive writing system (reader model, sentence positions, document jobs), not a ban-list. Do NOT use for conversational replies to the user (global…