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vllm-sr-agent-operations

Install, configure, and verify vLLM Semantic Router on a Docker host (CPU, AMD, or NVIDIA GPU) or a Kubernetes cluster, then operate it through its CLI and Router API. Use for a first install, an upgrade, routing verification, configuration changes, recipe tuning, and single-model/MoM evaluation.

74

Quality

93%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

96%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.

An exceptionally operational skill: every step is executable with named pass criteria, recovery paths, and an explicit error-feedback loop, with advanced material correctly deferred to six real one-level-deep reference files. The only notable gap is navigational — reference links point to web URLs instead of the bundled local files.

Suggestions

Point reference links at the bundled files with relative paths (e.g. [Deployment](references/deployment-loop.md)) instead of absolute https://vllm-sr.ai URLs, so the links resolve directly in the local bundle.

Consider moving the long GPU end-to-end probe block in step 6 (environment exports, isolated stack, systemone request) into the Deployment reference's GPU section, leaving just the pass criteria inline, to shorten the core walkthrough.

DimensionReasoningScore

Conciseness

The body is lean and imperative throughout ("Run the steps in order. Each one says what to run, what success looks like and what to do otherwise") and never explains concepts Claude already knows; the architectural notes ("It does not run the models that answer users; those are the user's endpoints") are non-obvious product facts that earn their tokens. Re-reading anchors 4 and 5: there are essentially no over-explanation instances to trim, so the anchor-5 fit holds.

5 / 5

Actionability

Fully executable, copy-paste-ready commands with expected outputs at every step — the install pipeline with a version check ("grep -q -- '--data-parallel-size'"), a complete config.yaml, and verification curls grepping exact headers ("x-vsr-selected-decision: code-route", "x-vsr-selected-model: MODEL") plus a CLI probe with --expect flags covering CPU, ROCm, and CUDA paths.

5 / 5

Workflow Clarity

A clearly sequenced 7-step workflow (Preflight → Choose path → Install → Configure → Start → Verify → Hand off) where every step has explicit pass criteria and an 'Otherwise' recovery column, verification is a 6-item checklist, and an explicit feedback loop is stated ("fix one thing, rerun that step, and report the change"). The destructive/batch cap does not apply since validation is pervasive.

5 / 5

Progressive Disclosure

Six topic-labeled reference files are cleanly split off into an 'After the install' table and inline contextual links, all six exist in references/ and are one level deep (bundle files link only back to SKILL.md and siblings). However, every reference link is an absolute https://vllm-sr.ai URL rather than a relative path to the bundled references/ files, which is exactly the kind of minor organization gap that separates anchor 4 from anchor 5's 'easy navigation'.

4 / 5

Total

19

/

20

Passed

Description

87%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 description with an explicit and well-enumerated 'Use for' clause and a distinctive product-named niche. Its only weaknesses are the slightly generic phrase 'operate it through its CLI and Router API' and a few missing natural synonyms in the trigger list.

Suggestions

Replace the generic 'operate it through its CLI and Router API' with the concrete operational actions it covers, e.g. 'route traffic between models, preview and probe routing decisions, and run sr-bench evaluations'.

Add common trigger synonyms such as 'deploy', 'vllm-sr', or 'stand up the router' to the 'Use for' clause so users phrasing the task differently still match.

DimensionReasoningScore

Specificity

Lists several concrete actions ("Install, configure, and verify", "an upgrade", "routing verification") and names platforms ("Docker host (CPU, AMD, or NVIDIA GPU) or a Kubernetes cluster"), but "operate it through its CLI and Router API" is generic compared to the crisp, fully enumerated action lists of the anchor-5 example.

4 / 5

Completeness

Explicitly answers both what ("Install, configure, and verify vLLM Semantic Router on a Docker host ... or a Kubernetes cluster, then operate it through its CLI and Router API") and when ("Use for a first install, an upgrade, routing verification, configuration changes, recipe tuning, and single-model/MoM evaluation") with concrete trigger scenarios, matching the anchor-5 example structure.

5 / 5

Trigger Term Quality

Good natural-phrase coverage in the trigger clause ("first install", "upgrade", "routing verification", "configuration changes", "recipe tuning", "single-model/MoM evaluation") plus the product name, but common variations like "deploy", "vllm-sr", or "troubleshoot" are absent.

4 / 5

Distinctiveness Conflict Risk

The named product ("vLLM Semantic Router") carves out a clear niche and the trigger clause is tied to that product's lifecycle, so it is unlikely to fire for generic container, vLLM-serving, or configuration skills.

5 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

vllm-project/semantic-router

Path

tools/agent/skills/vllm-sr-agent-operations/SKILL.md

Reviewed

Table of Contents

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