triton-inference-config
Triton Inference Config - Auto-activating skill for ML Deployment. Triggers on: triton inference config, triton inference config Part of the ML Deployment skill category.
36
3%
Does it follow best practices?
Impact
98%
0.98xAverage 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/triton-inference-config/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 name without describing any concrete capabilities or meaningful trigger conditions. It follows a boilerplate template ('Auto-activating skill for...') that adds no useful information for skill selection. The trigger terms are redundant duplicates of the skill name, missing the many natural phrases users would employ when needing help with Triton Inference Server configuration.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates and validates Triton Inference Server config.pbtxt files, configures model repositories, sets up dynamic batching, and defines ensemble pipelines.'
Add a 'Use when...' clause with natural trigger terms like 'Triton server', 'config.pbtxt', 'model repository', 'NVIDIA Triton', 'inference server configuration', 'model serving setup', 'dynamic batching config'.
Replace the boilerplate template with a substantive description that explains what distinguishes this skill from general ML deployment skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names 'Triton Inference Config' and 'ML Deployment' but provides no concrete actions. There is no indication of what the skill actually does—no verbs describing capabilities like 'generates config files', 'validates model repositories', etc. | 1 / 3 |
Completeness | The 'what' is essentially absent—there are no described capabilities beyond the name. The 'when' is technically present via 'Triggers on' but is just the skill name repeated, providing no meaningful guidance on when to select this skill. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'triton inference config' repeated twice. Missing natural variations users might say like 'Triton server', 'model config', 'config.pbtxt', 'NVIDIA Triton', 'inference server setup', 'model repository', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Triton Inference Config' is fairly niche and specific to NVIDIA Triton Inference Server, which reduces conflict risk with other skills. However, the lack of detail means it could overlap with broader ML deployment or model serving 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 a hollow placeholder that provides no actual knowledge about Triton Inference Server configuration. It contains only meta-descriptions of what the skill would do without any concrete content—no config.pbtxt examples, no model repository structure, no deployment commands, and no validation steps. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable examples of Triton config.pbtxt files for common model types (e.g., TensorRT, ONNX, PyTorch) with specific fields like max_batch_size, input/output tensor definitions, and instance_group settings.
Include a clear multi-step workflow: 1) Create model repository structure, 2) Write config.pbtxt, 3) Launch tritonserver with specific flags, 4) Validate with curl health check and perf_analyzer, with explicit validation checkpoints.
Remove all the meta-content sections (Purpose, When to Use, Capabilities, Example Triggers) that describe the skill abstractly and replace with actual Triton configuration knowledge and commands.
Add references to advanced topics like dynamic batching configuration, model ensembles, and rate limiting in separate linked files if the content would be too long for a single skill file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler with no substantive information. It explains what the skill does in abstract terms without providing any actual Triton configuration knowledge, commands, or code. Every section restates the same vague idea. | 1 / 3 |
Actionability | There is zero concrete guidance—no config.pbtxt examples, no model repository structure, no CLI commands, no code snippets. The content describes rather than instructs, offering nothing executable or copy-paste ready. | 1 / 3 |
Workflow Clarity | No workflow steps are defined at all. Triton deployment involves multi-step processes (model repository setup, config writing, server launch, validation) but none are mentioned, let alone sequenced with validation checkpoints. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no meaningful structure. There are no references to detailed guides, no separation of quick-start vs advanced content, and no navigation to related resources. | 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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