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.
36
3%
Does it follow best practices?
Impact
97%
1.02xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/streaming-inference-setup/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is extremely weak, essentially just restating the skill title without providing any concrete actions, meaningful trigger terms, or explicit usage guidance. It reads as auto-generated boilerplate with a duplicated trigger term and no substantive content to help Claude distinguish when to select this skill.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Configures streaming inference endpoints, sets up real-time model serving with frameworks like TensorFlow Serving or Triton, and optimizes latency for production deployments.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about deploying models for real-time predictions, setting up inference endpoints, streaming model outputs, or configuring low-latency serving infrastructure.'
Remove the duplicated trigger term and expand with varied natural language keywords users might actually use, such as 'real-time inference', 'model serving', 'inference pipeline', 'deploy model endpoint', 'online predictions'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the topic 'Streaming Inference Setup' without describing any concrete actions. There are no specific capabilities listed such as configuring endpoints, setting up model serving, or handling real-time predictions. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond restating the title, and the 'when' clause is essentially just the skill name repeated as a trigger. There is no explicit 'Use when...' guidance with meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'streaming inference setup' repeated twice, which is overly narrow technical jargon. It misses natural variations users might say like 'real-time predictions', 'model serving', 'deploy streaming model', 'inference endpoint', or 'low-latency inference'. | 1 / 3 |
Distinctiveness Conflict Risk | The term 'streaming inference setup' is somewhat specific to a niche within ML deployment, which provides some distinctiveness. However, the lack of concrete detail means it could overlap with other ML deployment skills covering model serving or inference pipelines. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an empty shell with no actionable content whatsoever. It consists entirely of meta-descriptions and trigger phrases that repeat 'streaming inference setup' without providing any actual guidance, code, configurations, or workflows for setting up streaming inference. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable code examples for setting up streaming inference (e.g., using gRPC streaming, Server-Sent Events, or frameworks like Triton Inference Server, TensorFlow Serving, or vLLM with streaming enabled).
Define a clear multi-step workflow with validation checkpoints, such as: 1) Configure model server for streaming, 2) Implement streaming endpoint, 3) Test with sample requests, 4) Validate latency/throughput metrics.
Remove all meta-description sections ('Purpose', 'When to Use', 'Example Triggers', 'Capabilities') that describe the skill abstractly and replace them with actual technical content covering streaming protocols, buffering strategies, and production configuration.
Add references to detailed sub-files for advanced topics like load balancing streaming connections, monitoring streaming latency, and handling backpressure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague idea—'streaming inference setup'—without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific configurations, no architecture patterns, no library recommendations. The skill describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. The content merely states it can provide 'step-by-step guidance' without including any actual steps. | 1 / 3 |
Progressive Disclosure | There is no meaningful content to organize, no references to detailed files, and no structured navigation. The sections are superficial headers over repetitive placeholder text with no depth or layering. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
3076d78
- Reviewed
Table of Contents
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