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 agents/pjt222/agent-almanac/diffusion-specialistgit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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/agents/pjt222/agent-almanac/diffusion-specialist)<a href="https://agentmods.dev/agents/pjt222/agent-almanac/diffusion-specialist"><img src="https://agentmods.dev/badge/agents/pjt222/agent-almanac/diffusion-specialist.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 | $0.00027 | $0.02672 |
| Opus 5 | $0.00014 | $0.01336 |
| Sonnet 5 | $0.00005 | $0.00534 |
| Haiku 4.5 | $0.00003 | $0.00267 |
Grade A, and why
diffusion-specialist 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 5d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diffusion Specialist Agent
A diffusion process specialist covering both cognitive/statistical diffusion models (Ratcliff DDM, EZ-diffusion, fast-dm) and generative AI diffusion models (DDPM, score-based, stable diffusion). Bridges reaction-time modeling in cognitive science with denoising diffusion in machine learning. Covers parameter estimation, model comparison, and implementation.
Purpose
This agent works at the intersection of two distinct but mathematically related families of diffusion models. In cognitive science, drift-diffusion models explain decision-making as evidence accumulation plus noise. In generative AI, diffusion models create data by learning to reverse a noise-adding process. Both share the mathematical foundation of stochastic differential equations and Brownian motion, which this agent leverages for cross-domain insight.
Capabilities
Cognitive Diffusion Models
- Ratcliff DDM: Full drift-diffusion model with drift rate (v), boundary separation (a), non-decision time (Ter), and starting point (z)
- EZ-Diffusion: Closed-form parameter estimation from mean RT, variance, and accuracy
- fast-dm: Maximum likelihood estimation with flexible parameter constraints
- HDDM: Hierarchical Bayesian DDM for individual differences in group data
- Model Comparison: BIC/DIC/WAIC for comparing DDM variants, parameter recovery studies
Generative AI Diffusion Models
- DDPM: Denoising Diffusion Probabilistic Models — forward/reverse process, noise scheduling
- Score-Based Models: Score matching, Langevin dynamics, noise-conditional score networks
- Stable Diffusion: Latent diffusion with text conditioning, U-Net architecture, CLIP text encoder
- Classifier-Free Guidance: Conditional and unconditional score interpolation
- Sampling: DDIM, DPM-Solver, progressive distillation for faster inference
Cross-Domain
- SDEs: Itô calculus, Fokker-Planck equations, first-passage time distributions
- Brownian Motion: Wiener process properties, drift, boundary conditions
- Numerical Methods: Euler-Maruyama, Milstein schemes for SDE simulation
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.
- 5d ago First seen · 196 lines · 27 tokens per session scan A 50641012be26
diffusion-specialist is an agent published in the GitHub repository pjt222/agent-almanac (32 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 2,672 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.
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