run-llms

run-llms is a skill for Claude Code, Codex from av/harbor. It costs 114 tokens per session (10,701 once invoked), scanned A, original, Apache-2.0.

A guide to running large language models (LLMs)—AI models that generate text or code—on your own computer with Harbor. Harbor packages local AI services such as Ollama, llama.cpp, vLLM, and Open WebUI in containers managed with Docker.

In plain words
What is it for?
Use it to install Harbor, download and run local models, set up a web interface, diagnose service or GPU issues, and operate related tools such as SearXNG and Open Terminal.
Why use it?
It gives you setup and troubleshooting steps for local AI services, including checking Docker, choosing a model format, handling GPU problems, and verifying that services are running.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for aider. Also seen: built for aider.

Good fit Use it to install Harbor, download and run local models, set up a web interface, diagnose service or GPU issues, and operate related tools such as SearXNG and Open Terminal.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/av/harbor/run-llms
About the project

av/harbor is a command-line tool and companion app that uses Docker Compose to start a connected local stack of language-model backends, user interfaces, and supporting AI services. People use it to run services such as Ollama, llama.cpp, vLLM, Open WebUI, search, voice, and image-generation tools without configuring their connections manually, while the catalogue provides agent workflows for operating Harbor.

av/harbor · 3,249 stars · on GitHub · discord.gg

Install

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.

Any agent
npx skills add av/harbor --skill run-llms
Clone the repo
git clone --depth 1 https://github.com/av/harbor

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for run-llms

README.md
[![agentmods](https://agentmods.dev/badge/skills/av/harbor/run-llms/github.svg)](https://agentmods.dev/skills/av/harbor/run-llms)
Your own site
<a href="https://agentmods.dev/skills/av/harbor/run-llms"><img src="https://agentmods.dev/badge/skills/av/harbor/run-llms/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.

agentmods 80×15 button for run-llms

Your own site · 80×15
<a href="https://agentmods.dev/skills/av/harbor/run-llms"><img src="https://agentmods.dev/badge/skills/av/harbor/run-llms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,701 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Sonnet 5 · 6 Sept 2026 📄 Read the review Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 17 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Tool Misuse · line 118
    Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.
    Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
  • high Supply Chain · line 134
    Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
    Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
  • high Tool Misuse · line 134
    Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.
    Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
  • high Tool Misuse · line 239
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 483
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 876
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Privilege Escalation · line 1217
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Memory Poisoning · line 1228
    Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.
    Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
  • medium MCP Rug Pull · line 40
    Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.
    Fix: Pin the image: image:tag or image@sha256:abc123
  • medium MCP Rug Pull · line 981
    Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.
    Fix: Pin the image: image:tag or image@sha256:abc123
  • medium Privilege Escalation · line 47
    Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.
    Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
  • medium Privilege Escalation · line 118
    Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.
    Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
  • medium Privilege Escalation · line 123
    Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.
    Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
  • medium Data Exfiltration · line 483
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium MCP Rug Pull · line 639
    Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.
    Fix: Pin the image: image:tag or image@sha256:abc123
  • medium Memory Poisoning · line 743
    Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.
    Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
  • medium Privilege Escalation · line 989
    Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.
    Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00114 $0.10701
Opus 5.5 $0.00046 $0.04280
Sonnet 5.5 $0.00023 $0.02140
Haiku 4.5 $0.00011 $0.01070

Measured 1mo ago against content hash b7bc007b97e4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

run-llms 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 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.

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.

skills/run-llms/SKILL.md · 1,322 lines

How it starts

The opening of the file, as written. The whole thing — 1,322 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run LLMs Locally with Harbor

Harbor is a containerized LLM toolkit. This skill enables autonomous setup, configuration, troubleshooting, and operation of local LLM infrastructure.

Agent Decision Trees

Use these decision trees to determine what action to take for common user requests.

User wants to run an LLM

1. Is Harbor installed?
   → NO: Install Harbor (see Initial Setup)
   → YES: Continue
2. Is Docker running?
   → Run: docker info
   → FAIL: Start Docker daemon, check installation
   → OK: Continue
3. Does the user have a specific model in mind?
   → YES: Determine format (Ollama tag, GGUF, HF safetensors)
     → Ollama tag (e.g. qwen3:4b): harbor pull <model> && harbor up
     → GGUF from HuggingFace: harbor pull <org/repo> && harbor up llamacpp
     → Safetensors/HF model: harbor vllm model <user/repo> && harbor up vllm
   → NO: Recommend a small default: harbor pull qwen3:4b && harbor up
4. Verify: harbor ps → confirm services healthy
5. Open UI: harbor open

User has GPU issues

1. Check NVIDIA drivers: nvidia-smi
   → FAIL: User needs to install NVIDIA drivers
   → OK: Continue
2. Check Container Toolkit: docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi
   → FAIL: Install NVIDIA Container Toolkit, restart Docker
   → OK: Continue
3. Check service logs: harbor logs <service>  # ⚠️ TAILS INDEFINITELY! (Agents: use `docker logs harbor.<service>` instead)
   → Look for: "CUDA error", "out of memory", "no GPU"
   → OOM: See "Model won't load / OOM" troubleshooting
   → No GPU detected: Check /etc/docker/daemon.json for nvidia runtime
4. Restart: sudo systemctl restart docker && harbor down && harbor up

User wants web search in chat

1. Start SearXNG: harbor up searxng
   → SearXNG auto-wires to Open WebUI when both run together
2. If WebUI was already running: harbor restart webui
3. Verify: harbor ps | grep searxng
4. Open UI: harbor open → Web search is now available in chat

User wants to change the model

Read the full file on GitHub · 1,322 lines

Changes

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.

  1. 1mo ago First seen · 1,322 lines · 114 tokens per session scan F b7bc007b97e4

Subscribe to this mod's changes

run-llms is a skill published in the GitHub repository av/harbor (3,249 stars, last pushed today), licensed Apache-2.0. It adds 114 tokens to every session and 10,701 once invoked, about $0.0005 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 2026-08-30.

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