tensorflow-serving-setup
Tensorflow Serving Setup - Auto-activating skill for ML Deployment. Triggers on: tensorflow serving setup, tensorflow serving setup Part of the ML Deployment skill category.
36
3%
Does it follow best practices?
Impact
97%
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/tensorflow-serving-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 essentially a title restated with no substantive content. It fails to describe what the skill actually does (e.g., configure TensorFlow Serving, set up model endpoints, manage serving configs) and provides no meaningful trigger guidance. The duplicate trigger term and boilerplate category mention add no value.
Suggestions
Add specific concrete actions like 'Configures TensorFlow Serving instances, sets up model versioning, creates REST/gRPC endpoints for model inference, and manages serving configuration files.'
Add a 'Use when...' clause with natural trigger terms like 'Use when the user needs to deploy a TensorFlow model, set up TF Serving, configure model serving endpoints, or mentions tensorflow-model-server, SavedModel deployment, or serving config.'
Remove the duplicate trigger term and expand with varied natural language terms users would actually say, such as 'deploy ML model', 'serve predictions', 'model endpoint', 'TF serving docker', 'inference server'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only says 'Tensorflow Serving Setup' without listing any concrete actions. There are no specific capabilities described like configuring models, setting up endpoints, managing serving infrastructure, etc. | 1 / 3 |
Completeness | The 'what' is extremely vague (just the skill name restated) and there is no explicit 'when' clause. The 'Triggers on' line is just a duplicate of the title, not meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'tensorflow serving setup' repeated twice. Missing natural variations users would say like 'deploy model', 'serve TF model', 'model serving', 'TensorFlow inference', 'REST API for model', 'gRPC serving', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'TensorFlow Serving' is somewhat specific to a particular technology, which provides some distinctiveness. However, the vague 'ML Deployment' category and lack of concrete actions could cause overlap with other ML deployment 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 auto-generated boilerplate that describes what a skill *would* do without providing any actual instructions, code, commands, or configuration for TensorFlow Serving setup. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable steps for TensorFlow Serving setup: exporting a SavedModel, pulling the TF Serving Docker image, running the container with correct volume mounts and port mappings, and verifying with a curl request.
Include a complete working example with code snippets, e.g., `docker run -p 8501:8501 --mount type=bind,source=/path/to/model,target=/models/my_model -e MODEL_NAME=my_model -t tensorflow/serving`.
Add validation checkpoints such as checking the model status endpoint (`/v1/models/my_model`) and sending a test prediction request to confirm the serving setup works.
Remove all meta-description sections ('Purpose', 'When to Use', 'Example Triggers') that describe the skill itself rather than teaching how to perform TensorFlow Serving setup.
| 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 about TensorFlow Serving setup. Every section restates the same vague information. | 1 / 3 |
Actionability | There is zero concrete guidance—no commands, no code, no configuration examples, no Docker instructions, no model export steps. Phrases like 'provides step-by-step guidance' and 'generates production-ready code' describe capabilities without demonstrating them. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. TensorFlow Serving setup is inherently a multi-step process (export model, configure serving, deploy, validate) but none of these steps are mentioned, let alone sequenced with validation checkpoints. | 1 / 3 |
Progressive Disclosure | There is no meaningful content to organize, no references to detailed guides, no links to configuration examples or advanced topics. The sections are purely boilerplate with no navigational value. | 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
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.