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 agentmods add skills/anastasiyaw/codex-claude-code-config/diffusion-engineeringnpx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineeringgit clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-configWrote 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/anastasiyaw/codex-claude-code-config/diffusion-engineering)<a href="https://agentmods.dev/skills/anastasiyaw/codex-claude-code-config/diffusion-engineering"><img src="https://agentmods.dev/badge/skills/anastasiyaw/codex-claude-code-config/diffusion-engineering.svg" alt="Measured on agentmods" 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 | $0.00287 | $0.01158 |
| Opus 5 | $0.00143 | $0.00579 |
| Sonnet 5 | $0.00057 | $0.00232 |
| Haiku 4.5 | $0.00029 | $0.00116 |
Grade A, and why
diffusion-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 3d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diffusion Engineering Skill
Быстрая ориентация
Три инженерных решения, которые больше всего влияют на качество/скорость/стоимость:
- Где идёт диффузия → пиксели (дорого) или латентное пространство (LDM/SD-семейство — практично)
- Backbone денойзера → UNet (классика, проще) или Transformer/DiT/Flow (масштабируется лучше)
- Управление сэмплингом → scheduler, число шагов, guidance_scale — часто дают больше, чем правка сети
Reference files — читать по задаче
| Тема | Файл | Когда читать |
|---|---|---|
| Архитектуры и data flow | references/architectures.md |
DDPM/SDE/LDM/DiT/Flux/VAE/SDXL, схема пайплайна |
| Schedulers и guidance | references/samplers.md |
DDIM/Euler/Heun/DPM-Solver/PNDM, CFG, prediction_type |
| Обучение и дообучение | references/training.md |
Loss/цели, LoRA/DreamBooth/full FT, гиперпараметры |
| Память и распределённость | references/memory.md |
AMP, checkpointing, ZeRO, FSDP, quantization, FP8 |
| Текст-энкодеры и данные | references/encoders-data.md |
CLIP/Qwen/multi-encoder, токенизация, data pipeline |
| Оценка и траблшутинг | references/eval-debug.md |
FID/CLIPScore/LPIPS, типовые поломки и фиксы, лицензии |
Быстрый чеклист «я строю/модифицирую diffusion»
- Backbone: UNet (проще) или DiT/Flow (масштабирование)?
- Модули зафиксированы: tokenizer → text encoder →
encoder_hidden_states→ denoiser → VAE decode - Scheduler выбран: DDIM / Euler / DPM-Solver — A/B на фиксированных seed
- Дообучение: начинать с LoRA, в full fine-tune только при необходимости
- Память: AMP включён, при необходимости checkpointing, при масштабе ZeRO/FSDP
- Данные: стриминг/шардинг (HF streaming, WebDataset), валидировать throughput dataloader
- Оценка: FID + CLIPScore + LPIPS + human rating; отдельно дневник промптов для overfitting
Trade-offs на один экран
| Ручка | Увеличить | Уменьшить |
|---|---|---|
num_inference_steps |
↑ качество | ↑ время |
guidance_scale (CFG) |
↑ adherence к промпту, риск «пережога» | ↑ разнообразие |
| LoRA rank | ↑ выразительность | ↑ параметры, риск overfitting |
| Шаги дообучения | ↑ адаптация | ↑ риск catastrophic forgetting |
| Batch size | ↑ стабильность градиентов | ↑ VRAM |
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.
- 3d ago First seen · 74 lines · 287 tokens per session scan A a3c4b7eb92e9
diffusion-engineering is a skill published in the GitHub repository AnastasiyaW/codex-claude-code-config (147 stars, last pushed 9d ago), licensed MIT. It adds 287 tokens to every session and 1,158 once invoked, about $0.0014 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.
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