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 squadcodercom/squadcoder --skill image-gen-promptinggit clone --depth 1 https://github.com/squadcodercom/squadcoderWrote 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/squadcodercom/squadcoder/image-gen-prompting)<a href="https://agentmods.dev/skills/squadcodercom/squadcoder/image-gen-prompting"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/image-gen-prompting/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/squadcodercom/squadcoder/image-gen-prompting"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/image-gen-prompting.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 | $0.00095 | $0.00820 |
| Opus 5.5 | $0.00038 | $0.00328 |
| Sonnet 5.5 | $0.00019 | $0.00164 |
| Haiku 4.5 | $0.00010 | $0.00082 |
Grade A, and why
sc:image-gen-prompting 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 1mo 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Image Prompting
Turn a request into a precise, generator-ready prompt. A good prompt is specific and ordered, not a pile of adjectives.
Prompt formula (in this order)
[subject + action] , [environment/context] , [composition/shot] , [style/medium] , [lighting] , [color/mood] , [quality/render] , [params]
Example: "a ceramic coffee mug on a marble countertop, morning kitchen, close-up product shot, soft natural window light from the left, warm muted palette, photorealistic, shallow depth of field --ar 4:5"
Levers (pick deliberately)
- Shot: close-up / macro / medium / wide / aerial / flat-lay / eye-level.
- Lens/feel: 35mm, 85mm portrait, tilt-shift, bokeh, long exposure.
- Style: photorealistic, 3D render, isometric, watercolor, line art, brand-flat, cyberpunk, claymation.
- Lighting: soft natural, golden hour, studio softbox, rim light, neon, chiaroscuro.
- Mood/color: warm/cool, pastel, high-contrast, monochrome, on-brand hex.
Negative prompts (SD/Flux)
List what to avoid: extra fingers, deformed hands, text, watermark, blurry, low-res, jpeg artifacts, duplicate.
Aspect ratios by use
- Square 1:1 (IG feed, avatars) · Portrait 4:5 (IG/FB feed) · 9:16 (Stories/Reels/TikTok) · 16:9 (YouTube/web hero) · 1.91:1 (link previews/ads).
Model-specific notes
- Midjourney: terse, comma-separated; use
--ar,--s(stylize),--c(chaos),--nofor negatives. Quality over sentence grammar. - DALL·E / GPT-Image: full natural-language sentences; great at following instructions + text-in-image; describe layout explicitly.
- Stable Diffusion / Flux: weighted tokens
(keyword:1.3), explicit negative prompt, set steps/CFG; good for control + LoRAs. - Higgsfield: motion/video-oriented — specify camera move (push-in, orbit, pan), duration, and a clear single subject; keep scenes simple.
Product / ad creatives (most common ask)
- Isolate the product, name the surface + background, specify lighting direction, leave negative space for copy if it's an ad.
- Keep brand colors; request a clean composition; generate 3–4 variations and pick.
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.
- 1mo ago First seen · 49 lines · 95 tokens per session scan A 11c859011b07
sc:image-gen-prompting is a skill published in the GitHub repository squadcodercom/squadcoder (12 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 820 once invoked, about $0.0004 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.
Other skills, from other repositories
autoresearch
Self-improving optimization via Karpathy autoresearch pattern. Generates → evaluates → scores → mutates prompts/descriptions in a loop. Targets — tool-selection, system-prompt, skill, decision-parser. Use when "optimize tools", "autoresearch", "improve skill X", "self-improve prompts", "optimize tool descriptions".
hatch3r-ai-feature
Eval-driven development workflow for shipping AI features — write eval before prompt, measure, iterate, ship with caching + cost telemetry + model fallback + hallucination SLI.
system-prompts
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
v4-best-practices
Use when working with deepseek-v4-pro or deepseek-v4-flash in thinking mode on multi-step or plan-driven tasks. Provides rules to prevent stale references, unverified plan assumptions, and vague plan output.
refine
Improve a prompt using official OpenAI or Claude guidance. Text or @file mode; --for openai or --for claude selects guidance, not a runtime model.