BC local-llm-router
Local LLM model router for Llama, Qwen, DeepSeek, Phi, Mistral, and Gemma across multiple devices. Self-hosted local LLM inference routing on macOS, Linux, and Windows. Local LLM 7-signal scoring engine picks the optimal machine for every local LLM request. OpenAI-compatible local LLM API with context protection, VRAM-aware fallback, and auto-retry. 本地LLM路由 inference router | LLM local enrutador de inferencia. Use when the user wants to optimize local LLM routing, reduce local LLM latency, or load balance local LLM across machines.
Local LLM model router for Llama, Qwen, DeepSeek, Phi, Mistral, and Gemma across multiple devices.
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 25 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2154 tokens
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 537: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (18 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.