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.
git clone --depth 1 https://github.com/jkitchin/discoptWrote 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/jkitchin/discopt/ml-embedding-expert)<a href="https://agentmods.dev/agents/jkitchin/discopt/ml-embedding-expert"><img src="https://agentmods.dev/badge/agents/jkitchin/discopt/ml-embedding-expert/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/agents/jkitchin/discopt/ml-embedding-expert"><img src="https://agentmods.dev/badge/agents/jkitchin/discopt/ml-embedding-expert.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 | $0.00102 | $0.02956 |
| Opus 5.5 | $0.00041 | $0.01182 |
| Sonnet 5.5 | $0.00020 | $0.00591 |
| Haiku 4.5 | $0.00010 | $0.00296 |
Grade A, and why
ml-embedding-expert 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 27d 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.
The source is not reproduced here
Licensed EPL-2.0
The repository is licensed EPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 27d ago Changed 609dac629584
- 1mo ago First seen · 163 lines · 102 tokens per session scan A 8266e5920e0c
ml-embedding-expert is an agent published in the GitHub repository jkitchin/discopt (29 stars, last pushed today), licensed EPL-2.0. It adds 102 tokens to every session and 2,956 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 2026-09-16.
Other agents, from other repositories
leakage-auditor
Specialized agent for detecting data leakage, train-test contamination, and temporal leakage in ML pipelines.
staff-data-sci
Personas are Opus-only. The Data Science Reviewer: data science, ML, and statistics review.
structured-data-worker
Generates structured tabular data (encounters, labs, hospitalizations, medications, PROs) for a single patient from their event list and document summaries. Reads table schemas from YAML files. Writes JSON output to a specified path. Spawned by the generate-synthetic-data skill -- do not invoke directly.
ml-engineer
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
bayesian-network-prediction
Constructs and operates probabilistic graphical models for causal inference, prediction under uncertainty, and dynamic belief updating with verified mathematical foundations and real-world integration.
Demonstrate
Agent for demonstrating VS Code features.