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 artokun/comfyui-mcp --skill prompt-engineeringgit clone --depth 1 https://github.com/artokun/comfyui-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/artokun/comfyui-mcp/prompt-engineering)<a href="https://agentmods.dev/skills/artokun/comfyui-mcp/prompt-engineering"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/prompt-engineering/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/artokun/comfyui-mcp/prompt-engineering"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/prompt-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.03031 |
| Opus 5 | $0.00014 | $0.01515 |
| Sonnet 5 | $0.00006 | $0.00606 |
| Haiku 4.5 | $0.00003 | $0.00303 |
Grade A, and why
prompt-engineering 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompt-engineering — 94% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ComfyUI Prompt Engineering
CLIP Text Encoding Fundamentals
ComfyUI uses CLIP (Contrastive Language-Image Pre-training) text encoders to convert text prompts into conditioning tensors. The CLIPTextEncode node takes a text string and a CLIP model, producing a CONDITIONING output for the KSampler.
Token Limit
CLIP processes text in 77-token chunks. Each word is typically 1-3 tokens. Prompts exceeding 77 tokens are silently truncated unless you use the BREAK token or a multi-clip encoding node.
Weight Syntax
Emphasis (Attention Weights)
Adjust how strongly the model attends to specific words or phrases:
| Syntax | Effect | Equivalent Weight |
|---|---|---|
(word:1.3) |
Increase emphasis by 30% | Explicit weight 1.3 |
(word:0.7) |
Decrease emphasis by 30% | Explicit weight 0.7 |
(word) |
Slight increase | (word:1.1) |
((word)) |
Moderate increase | (word:1.21) — 1.1^2 |
(((word))) |
Strong increase | (word:1.331) — 1.1^3 |
[word] |
Slight decrease | (word:0.9091) — 1/1.1 |
[[word]] |
Moderate decrease | (word:0.8264) — 1/1.1^2 |
Weight Rules
- Valid range: 0.0 to 2.0 (going beyond 1.5 often causes artifacts)
- Default weight: 1.0 for unmodified tokens
- Nesting stacks multiplicatively:
((word))= 1.1 * 1.1 =(word:1.21) - Phrases:
(red sports car:1.3)applies weight to the entire phrase - Mixing:
(detailed face:1.4), (blurry background:0.6), combined in one prompt
Examples
a (beautiful:1.3) woman with (flowing red hair:1.2), wearing a blue dress, (sharp focus:1.1)
(masterpiece:1.4), (best quality:1.3), a knight in (ornate armor:1.2), standing on a cliff, (dramatic lighting:1.1), cinematic
BREAK Token
The BREAK keyword forces CLIP to end the current 77-token chunk and start processing subsequent text in a new chunk. This is critical for long prompts.
When to Use BREAK
- Prompt exceeds ~60 words (approaching the 77-token limit)
- You want to separate conceptually distinct parts of the prompt
- Certain details are being ignored (they may be past the 77-token cutoff)
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.
- 10d ago First seen · 310 lines · 28 tokens per session scan A cc46604075a1
prompt-engineering is a skill published in the GitHub repository artokun/comfyui-mcp (730 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 3,031 once invoked, about $0.0001 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.
Other skills, from other repositories
minimax-h3
Use when writing or debugging prompts for MiniMax H3 (Hailuo 3) video-with-audio generation, running the open weights locally in ComfyUI, choosing a quant or an acceleration LoRA for the VRAM you have, wiring reference-to-video with images, video or audio, or when a generated clip produces gibberish speech, drifts off…
seedance
Use when writing or debugging prompts for ByteDance Seedance video models (Seedance 2.5, 2.0, 2.0 Mini, 1.5 Pro, 1.0) on Dreamina, Jimeng AI, Doubao, BytePlus ModelArk or ComfyUI, when a generated video drifts off the reference face, grows unwanted subtitles or watermarks, duplicates a character, jumps at an extension…
Prompt craft for ComfyUI generation
Use when writing prompts for ComfyUI image or video models: choosing tags vs literary format, fixing composition and anatomy artifacts, picking samplers per LoRA, and writing short movement prompts for video. Neutral examples only.
emotion-to-camera-language
A guide for turning vague visual feelings—such as cinematic, atmospheric, elegant, or healing—into concrete image or video prompt details. It uses lighting direction, depth of field, camera position, and the subject’s state.
comfyui-prompt-engineer
Craft model-specific prompts optimized for the target checkpoint and identity method. Handles FLUX, SDXL, SD1.5, and Wan video models with proper syntax, quality tags, and negative prompts. Use when generating or refining prompts for ComfyUI workflows.
comfyui-prompt-interview
Guided conversational interview to understand a user's creative vision before generating model-appropriate image prompts. Asks clarifying questions about subject, mood, style, and technical preferences (4-7 exchanges), then synthesizes positive prompt, negative prompt, recommended settings table, and pipeline…