run-llms

Comprehensive guide for setting up and running local LLMs using Harbor. Use when user wants to run LLMs locally, set up or troubleshoot Ollama, Open WebUI, llama.cpp, vLLM, SearXNG, Open Terminal, or similar local AI services. Covers full setup from Docker prerequisites through running models, per-service configuration, VRAM optimization, GPU troubleshooting, web search integration, code execution, profiles, tunnels, and advanced features. Includes decision trees for autonomous agent workflows and step-by-step troubleshooting playbooks.

Quality

89%

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Critical

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Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, command-dense reference with clear sequenced workflows and good verify checkpoints. Its main weaknesses are monolithic structure (no external reference files) and duplication of config/env-var tables that inflates token usage.

Suggestions

Move the Environment Variable Quick Reference and per-service config tables into a references/ file (e.g. CONFIG.md) and link to it from SKILL.md to reduce duplication and enable progressive disclosure.

Consolidate Common Workflows with the Agent Decision Trees / Initial Setup to remove restated sequences and trim token cost.

Add explicit validate→fix→retry loops to the troubleshooting playbooks (e.g. 'run fixfs, then re-check harbor ps; if still failing, ...') to lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	Mostly lean command/table reference that avoids explaining concepts Claude already knows, but the ~1320-line body duplicates material (the Environment Variable Quick Reference table repeats per-service config tables, and Common Workflows restate the decision trees/setup steps), so not every token earns its place.	4 / 5
Actionability	Dense with copy-paste-ready, fully executable `harbor ...` commands and concrete config examples that cover the common cases across every service.	5 / 5
Workflow Clarity	Decision trees and the Initial Setup workflow are clearly sequenced with explicit verify checkpoints (e.g. 'harbor ps → confirm services healthy', 'harbor doctor'), but the linear troubleshooting playbooks lack consistent validate→fix→retry feedback loops, so some validation is implicit.	4 / 5
Progressive Disclosure	No bundle files exist and everything — env-var reference tables, full per-service config blocks, troubleshooting playbooks — is inlined into one monolithic SKILL.md; section headers provide structure, but content that clearly belongs in separate reference files is not split out or signaled.	3 / 5
	Total	16 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that names the toolkit, enumerates concrete capabilities, and gives explicit 'Use when' triggers with natural service names. It clearly answers what and when with minimal conflict risk.

Dimension	Reasoning	Score
Specificity	Lists many concrete actions ('setting up and running local LLMs', 'per-service configuration, VRAM optimization, GPU troubleshooting, web search integration, code execution, profiles, tunnels') across named services, giving comprehensive coverage rather than vague abstractions.	5 / 5
Completeness	Explicitly answers both 'what' ('setting up and running local LLMs using Harbor', plus the coverage list) and 'when' ('Use when user wants to run LLMs locally, set up or troubleshoot ...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes natural product names users actually say ('Ollama', 'Open WebUI', 'llama.cpp', 'vLLM', 'SearXNG', 'Open Terminal') plus the generic phrase 'run LLMs locally' and 'local AI services', covering synonyms and specific tools.	5 / 5
Distinctiveness Conflict Risk	Anchored to a specific toolkit (Harbor) and named local-LLM services, giving it a clear niche with minimal overlap risk against other skills.	5 / 5
	Total	20 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1322 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: av/harbor
Path: skills/run-llms/SKILL.md
Commit: 67cbe66

Reviewed: 2 days ago

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