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 sandyup/comfyui-mcp --skill prompt-engineeringgit clone --depth 1 https://github.com/sandyup/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/sandyup/comfyui-mcp/prompt-engineering)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/prompt-engineering"><img src="https://agentmods.dev/badge/skills/sandyup/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/sandyup/comfyui-mcp/prompt-engineering"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/prompt-engineering.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 | $0.00028 | $0.02952 |
| Opus 5.5 | $0.00011 | $0.01181 |
| Sonnet 5.5 | $0.00006 | $0.00590 |
| Haiku 4.5 | $0.00003 | $0.00295 |
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 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.
This is a copy
94% identical to prompt-engineering — 39 lines 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 — 305 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)— combine 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.
- 1mo ago First seen · 305 lines · 28 tokens per session scan A ad90d86aedc4
prompt-engineering is a skill published in the GitHub repository sandyup/comfyui-mcp (2 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 2,952 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to prompt-engineering, differing in 39 lines, and is treated as a copy.
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