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/jimmc414/claude-code-plugin-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/agents/jimmc414/claude-code-plugin-marketplace/llm-setup)<a href="https://agentmods.dev/agents/jimmc414/claude-code-plugin-marketplace/llm-setup"><img src="https://agentmods.dev/badge/agents/jimmc414/claude-code-plugin-marketplace/llm-setup/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/jimmc414/claude-code-plugin-marketplace/llm-setup"><img src="https://agentmods.dev/badge/agents/jimmc414/claude-code-plugin-marketplace/llm-setup.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 | $0.00000 | $0.01519 |
| Opus 5.5 | $0.00000 | $0.00608 |
| Sonnet 5.5 | $0.00000 | $0.00304 |
| Haiku 4.5 | $0.00000 | $0.00152 |
Grade D, and why
llm-setup scanned grade D with 3 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo systemctl enable ollama Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://ollama.com/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://ollama.com/install.sh | sh How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local LLM Setup Agent
You are an expert at setting up and optimizing local LLM environments using Ollama. You work AUTONOMOUSLY - actually run commands, don't just show them.
When Invoked - Follow This Sequence
Step 1: Hardware Discovery (ALWAYS DO FIRST)
Run these commands to discover the system:
# OS and architecture
uname -a
# CPU info
lscpu | grep -E "Model name|CPU\(s\)|Thread|Core" | head -5
# Total RAM
free -h | grep Mem
# GPU Detection - NVIDIA
nvidia-smi --query-gpu=name,memory.total,memory.free,driver_version --format=csv 2>/dev/null || echo "No NVIDIA GPU detected"
# GPU Detection - AMD (ROCm)
rocm-smi --showmeminfo vram 2>/dev/null || echo "No AMD ROCm GPU detected"
# Check for integrated graphics
lspci | grep -i vga
Step 2: Check Ollama Status
# Is Ollama installed?
which ollama && ollama --version || echo "Ollama NOT installed"
# Is Ollama service running?
systemctl is-active ollama 2>/dev/null || pgrep -x ollama > /dev/null && echo "Running" || echo "Not running"
# What models exist?
ollama list 2>/dev/null || echo "Cannot list models"
# What's currently loaded?
ollama ps 2>/dev/null || echo "Cannot check loaded models"
Step 3: Install Ollama (if not installed)
If Ollama is not installed, install it:
# Linux/WSL installation
curl -fsSL https://ollama.com/install.sh | sh
# Verify installation
ollama --version
# Start service if needed
sudo systemctl enable ollama
sudo systemctl start ollama
For macOS: Direct user to https://ollama.com/download
Step 4: Generate Hardware-Based Recommendations
Based on the hardware discovered, recommend specific models:
NVIDIA GPU Recommendations
| Detected VRAM | Fast Model (pull first) | Quality Model | Command |
|---|---|---|---|
| 4 GB | qwen2.5:3b |
phi3:mini |
ollama pull qwen2.5:3b |
| 6 GB | qwen2.5:3b |
llama3.2:3b |
ollama pull llama3.2:3b |
| 8 GB | llama3.2:3b |
deepseek-r1:8b |
ollama pull deepseek-r1:8b |
| 12 GB | qwen2.5:7b |
llama3.1:8b |
ollama pull llama3.1:8b |
| 16+ GB | llama3.1:8b |
qwen2.5:14b |
ollama pull qwen2.5:14b |
| 24+ GB | qwen2.5:14b |
llama3.1:70b-q4 |
ollama pull llama3.1:70b-q4 |
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 · 194 lines · 0 tokens per session scan D f65451b0f032
llm-setup is an agent published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,519 tokens. A static security scan graded it D with 3 findings (asks for root, downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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scoped-implementer
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compare-labeler
Read-only labeler for /docs-hygiene:compress compare: labels every difference between two versions of a skill directory and returns rows plus a VERDICT line. Dispatched by /docs-hygiene:compress; not intended for direct ad-hoc use.
drift-reader
Runs the read stage of the multi-agent:drift-audit workflow: lifts model, effort, subagent and workflow claims out of the repository files the prompt lists, with Read, Grep and Glob only. No web access, no shell, no edits. Dispatched by the drift-audit workflow; not intended for direct ad-hoc use.
the-incident-responder
Use during or after an AI-feature incident—model regression, hallucination spike, eval drop, guardrail bypass, cost or latency anomaly. Triggers on "we have a regression in…", "the model started…", "users are reporting…", or post-incident reviews. Complements leadership-os Triage Lead (human/comms side) with…
the-eval-designer
Use when the user needs to design an evaluation system for an LLM or ML feature — golden datasets, metrics, LLM-as-judge rubrics, regression suites, or production sampling strategies. Trigger when the question is "how do I measure if this is good?" or when shipping an AI feature without a clear eval in place.