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/andisab/swe-marketplaceWrote 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/plugins/andisab/swe-marketplace/machine-learning)<a href="https://agentmods.dev/plugins/andisab/swe-marketplace/machine-learning"><img src="https://agentmods.dev/badge/plugins/andisab/swe-marketplace/machine-learning/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/plugins/andisab/swe-marketplace/machine-learning"><img src="https://agentmods.dev/badge/plugins/andisab/swe-marketplace/machine-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
machine-learning 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 today.
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.
What it actually says
{
"name": "machine-learning",
"description": "Machine learning framework specialists for PyTorch, TensorFlow, and scikit-learn. Expert guidance for ML model development and training.",
"version": "1.0.0",
"author": {
"name": "Andis A. Blukis",
"email": "[email protected]",
"url": "https://github.com/andisab"
},
"homepage": "https://github.com/andisab/swe-marketplace",
"repository": "https://github.com/andisab/swe-marketplace",
"license": "MIT",
"keywords": [
"machine-learning",
"ml",
"pytorch",
"tensorflow",
"scikit-learn",
"deep-learning"
],
"agents": [
"./agents/ml-pytorch-expert.md",
"./agents/ml-scikit-learn-expert.md",
"./agents/ml-tensorflow-expert.md"
]
}
What it installs
The manifest is a name and a version. 3 agents travel with it, and installing the plugin installs all of them — 88 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- today First seen · 27 lines scan A d725b951cc93
machine-learning is a plugin published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed today), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-10-17.
Other plugins, from other repositories
commander-hud
Commander HUD: Fable state, running agents, tool-call budget and update pill above the prompt.
commander
Guided AI PM for Claude Code, running on The Fable Method — 12 enforceable gates so any model produces Fable-shaped results. 84 plugin skills (click-first /ccc- workflows, incl. /ccc-codex for the Codex CLI), 22 specialist sub-agents (architect, reviewer, debugger, designer, security-auditor…), 30 lifecycle hooks × 51.
data-converter
Advanced data transformation and processing plugin for JSON, CSV, XML, YAML with powerful querying, filtering, and conversion capabilities.
ai-toolkit
Skills for Deep Learning (Keras 3), Reinforcement Learning (Python & C++), Claude Prompt Optimization, x402 Web3 Payments, and OpenClaw Setup.
ai-trend-tracker
On-demand AI/ML news, tools, papers, courses, and channel recommendations — always live-searched, never stale.
llm-language
Self-evolving prompt meta-compiler for the Fable 5 / Opus 4.8 era (Mythos-class aware) with xhigh/max effort, adaptive thinking, task budgets, dual-layer memory (ROSETTA.md + auto-memory), AND skill-level meta-refinement. v4.4: FABLE-5-ERA RETARGET — complexity map gains a model dimension (Opus 4.8 workhorse default…