streaming-inference-setup

Streaming Inference Setup - Auto-activating skill for ML Deployment. Triggers on: streaming inference setup, streaming inference setup Part of the ML Deployment skill category.

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill streaming-inference-setup

What are skills?

Overall
score

19%

Review — 4%

Does it follow best practices?

Validation — 11 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

Activation

This description is a template-style placeholder that provides almost no useful information for skill selection. It lacks concrete actions, meaningful trigger terms, and explicit usage guidance. The redundant trigger term and boilerplate structure suggest this was auto-generated without customization.

Suggestions

Add specific concrete actions like 'Configure streaming inference endpoints, set up model serving infrastructure, deploy real-time prediction pipelines, optimize latency for live inference'

Add a 'Use when...' clause with natural trigger terms: 'Use when deploying ML models for real-time predictions, setting up inference endpoints, configuring model serving, or when user mentions streaming predictions, live inference, or real-time ML'

Remove the redundant trigger term and expand with user-friendly variations like 'real-time predictions', 'model serving', 'inference API', 'deploy model endpoint'

Dimension	Reasoning	Score
Specificity	The description only names the domain 'Streaming Inference Setup' and category 'ML Deployment' but provides no concrete actions. There are no specific capabilities listed like 'configure endpoints', 'set up model serving', or 'deploy streaming pipelines'.	1 / 3
Completeness	The description fails to answer 'what does this do' beyond the title, and has no explicit 'Use when...' clause or equivalent guidance for when Claude should select this skill. Both what and when are very weak.	1 / 3
Trigger Term Quality	The trigger terms are redundantly listed ('streaming inference setup' appears twice) and are technical jargon. Missing natural variations users might say like 'real-time predictions', 'model serving', 'deploy ML model', 'inference endpoint', or 'live predictions'.	1 / 3
Distinctiveness Conflict Risk	While 'streaming inference' is somewhat specific to ML deployment, the lack of concrete actions and the generic 'ML Deployment' category could cause overlap with other ML-related skills. The term 'streaming' alone could conflict with data streaming skills.	2 / 3
	Total	5 / 12 Passed

Implementation

This skill is essentially a placeholder template with no actual technical content. It describes what a streaming inference setup skill should do but provides absolutely no actionable guidance, code examples, configuration templates, or workflow steps. The content would be useless for actually helping someone set up streaming inference.

Suggestions

Add concrete code examples for streaming inference setup (e.g., using TensorFlow Serving, Triton, or Ray Serve with streaming endpoints)

Include a clear workflow with numbered steps: model preparation, server configuration, endpoint setup, and validation/testing

Provide specific configuration examples (YAML/JSON) for common streaming inference frameworks

Add validation checkpoints such as health check commands, latency testing, and throughput verification steps

Dimension	Reasoning	Score
Conciseness	The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual technical content.	1 / 3
Actionability	There is zero concrete guidance - no code, no commands, no specific steps, no configuration examples. The entire skill describes what it claims to do rather than providing any executable instructions for streaming inference setup.	1 / 3
Workflow Clarity	No workflow is defined at all. Despite claiming to provide 'step-by-step guidance,' there are no actual steps, no sequence, and no validation checkpoints for setting up streaming inference.	1 / 3
Progressive Disclosure	The content is a flat, uninformative structure with no references to detailed materials, no links to implementation guides, and no organization beyond generic section headers that contain no useful content.	1 / 3
	Total	4 / 12 Passed

Validation

69%

Warnings & errors only

Validation — 11 / 16 Passed

Validation for skill structure

Criteria	Description	Result
description_trigger_hint	Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...')	Warning
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
metadata_version	'metadata' field is not a dictionary	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
body_steps	No step-by-step structure detected (no ordered list); consider adding a simple workflow	Warning

	Total	11 / 16 Passed

Reviewed

21 days ago

Table of Contents

Activation Implementation Validation

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