inference-latency-profiler
Inference Latency Profiler - Auto-activating skill for ML Deployment. Triggers on: inference latency profiler, inference latency profiler Part of the ML Deployment skill category.
35
3%
Does it follow best practices?
Impact
92%
1.01xAverage 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/inference-latency-profiler/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 essentially a placeholder that names the skill and its category but provides no substantive information about what it does or when to use it. It lacks concrete actions, meaningful trigger terms, and explicit usage guidance. The duplicate trigger term suggests auto-generated content with no human refinement.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Profiles model inference latency, identifies bottlenecks in serving pipelines, measures p50/p95/p99 response times, and generates optimization recommendations.'
Add a 'Use when...' clause with natural trigger terms like 'Use when the user mentions slow inference, model latency, serving performance, response time profiling, deployment bottlenecks, or needs to optimize ML model serving speed.'
Remove the duplicate trigger term and expand with varied natural language terms users would actually say, such as 'slow predictions', 'model speed', 'latency optimization', 'inference benchmarking'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('ML Deployment') and a tool name ('Inference Latency Profiler') but describes no concrete actions whatsoever. There are no verbs indicating what the skill actually does—no 'profiles', 'measures', 'analyzes', or any other actionable capability. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' clause is just a self-referential trigger ('triggers on: inference latency profiler'). There is no explicit 'Use when...' guidance with meaningful context. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'inference latency profiler' repeated twice. There are no natural user keywords like 'slow inference', 'model latency', 'response time', 'profiling', 'bottleneck', 'serving performance', or other terms a user would naturally use. | 1 / 3 |
Distinctiveness Conflict Risk | The name 'Inference Latency Profiler' is fairly specific and unlikely to conflict with many other skills, but the lack of concrete capability descriptions and the vague 'ML Deployment' category could cause overlap with other ML-related skills. | 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 substantive content. It consists entirely of generic boilerplate that could apply to any topic—there is no actual guidance on inference latency profiling, no code examples, no tool recommendations, no metrics, and no workflows. It provides zero value beyond what the skill's title already communicates.
Suggestions
Add concrete, executable code examples for profiling inference latency (e.g., using Python's time module, NVIDIA Nsight, PyTorch Profiler, or TensorRT) with specific metrics like P50/P95/P99 latency.
Define a clear multi-step workflow: instrument model serving endpoint → collect latency samples → analyze distribution → identify bottlenecks → optimize → validate improvement, with explicit validation checkpoints.
Include specific tool recommendations and configurations (e.g., Triton Inference Server metrics, MLflow tracking, Prometheus/Grafana dashboards) with copy-paste ready setup commands.
Remove all generic boilerplate sections ('When to Use', 'Example Triggers', 'Capabilities') and replace with actionable content that teaches Claude something it doesn't already know about latency profiling.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats the phrase 'inference latency profiler' excessively, and provides zero domain-specific information. Every section is generic padding. | 1 / 3 |
Actionability | There is no concrete guidance whatsoever—no code, no commands, no specific tools, no profiling methodology, no metrics definitions. It only describes what the skill claims to do in vague terms without actually doing any of it. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The skill claims to provide 'step-by-step guidance' but contains none. There are no validation checkpoints or any sequenced instructions. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of generic text with no references to detailed materials, no links to supporting files, and no meaningful structural organization beyond boilerplate headings. | 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
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- Reviewed
Table of Contents
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