SKILLEMALL.ai
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AB deployment

Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).

NVIDIA/Model-Optimizer Agent Skills author: NVIDIA Apache-2.0 10 files · 1 script body ≈ 3 158 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationDockerInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
98
Quality 40%
99
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Dangerous commands cmd-background-process scripts/deploy.sh:292
      Starts a background / autostarted process
      nohup "${cmd[@]}" >"$LOG_FILE" 2>&1 &
    • low Dangerous commands cmd-background-process scripts/deploy.sh:319
      Starts a background / autostarted process
      nohup "${cmd[@]}" >"$LOG_FILE" 2>&1 &

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
    • 100Steps. 20 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3158 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (13 tags): a typed call is more reliable
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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

    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 448: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.