Conciseness

Mostly efficient — nearly everything is Harbor-specific commands rather than concepts Claude already knows — but there is real duplication that could be tightened: the per-service config tables (Ollama, llama.cpp, WebUI, SearXNG, Open Terminal) are repeated verbatim in the 'Environment Variable Quick Reference', and the web-search, code-execution, and tunnel workflows appear in both 'Agent Decision Trees' and 'Common Workflows'. This is more than the minor over-explanation of anchor 4.

Actionability

Fully executable, copy-paste-ready commands throughout, covering the common cases: 'harbor pull qwen3:4b && harbor up', 'harbor vllm args --max-model-len 4096 --enforce-eager', 'docker logs harbor.<service>', working curl examples for the router API, and complete numbered troubleshooting commands. No pseudocode or vague direction.

Workflow Clarity

Clear sequences with most checkpoints present: the 5-step setup workflow includes explicit verification ('harbor ps → confirm services healthy', 'harbor doctor', 'Wait for "Application startup complete"'), and the GPU/OOM troubleshooting playbooks are numbered with verification commands. Not 5 because the sandbox reset ('rm -rf services/openterminal/data') is a destructive step with only log/health checks before it and no real validation gate.

Progressive Disclosure

Section structure is good — clear headers, per-service sections, quick-reference tables — but there are no reference files at all: reference-grade material (per-service CLI listings, config tables, and environment variable tables for 8+ services) is inlined in a single ~1300-line SKILL.md. This fits anchor 3 (content that should be separate is inline, though well organized) rather than anchor 2's unstructured blob.

Specificity

Lists multiple concrete actions with comprehensive coverage: 'setting up and running local LLMs', 'set up or troubleshoot Ollama, Open WebUI, llama.cpp, vLLM, SearXNG, Open Terminal', 'per-service configuration, VRAM optimization, GPU troubleshooting, web search integration, code execution, profiles, tunnels'. Coverage spans setup, configuration, optimization, troubleshooting, and integrations; only trivial filler ('advanced features') keeps it from being perfectly lean, which is not enough to drop to 4.

Completeness

Explicitly answers both parts: what ('Comprehensive guide for setting up and running local LLMs using Harbor... per-service configuration, VRAM optimization, GPU troubleshooting') and when ('Use when user wants to run LLMs locally, set up or troubleshoot Ollama... or similar local AI services') with concrete trigger phrases, matching anchor 5.

Trigger Term Quality

Good keyword coverage with natural user phrasing: 'run LLMs locally', 'Ollama, Open WebUI, llama.cpp, vLLM', 'GPU troubleshooting', 'web search'. A few natural synonyms users would say are missing (e.g. 'self-hosted', 'run a model'), so it fits anchor 4 rather than the comprehensive synonym coverage of anchor 5.

Distinctiveness Conflict Risk

Clear niche (local LLM infrastructure via Harbor) with distinct triggers, but the description triggers on individual tool names ('Ollama', 'vLLM', 'SearXNG') that could overlap with dedicated per-tool skills, and 'or similar local AI services' broadens scope — minor overlap risk with closely related skills (anchor 4) rather than minimal conflict risk (anchor 5).