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.
41
Quality
11%
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
22%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 severely underdeveloped, essentially just restating the skill name without explaining capabilities or providing meaningful trigger guidance. It lacks concrete actions, comprehensive trigger terms, and explicit 'when to use' instructions. The duplicate trigger term suggests a template that wasn't properly filled out.
Suggestions
Add specific actions the skill performs, e.g., 'Configures TensorFlow Serving containers, sets up model versioning, creates serving endpoints, and manages model deployment pipelines.'
Add a 'Use when...' clause with natural trigger terms: 'Use when deploying TensorFlow models to production, setting up TF Serving, configuring model endpoints, or serving ML models via REST/gRPC.'
Include common variations of terminology users might say: 'TF Serving', 'model serving', 'SavedModel deployment', 'inference server', 'production ML model'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only mentions 'Tensorflow Serving Setup' without describing any concrete actions. It doesn't explain what the skill actually does - no verbs like 'deploy', 'configure', 'manage', or specific capabilities are listed. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself. There is no 'Use when...' clause or equivalent explicit trigger guidance. The 'Triggers on' section is redundant and doesn't provide meaningful context. | 1 / 3 |
Trigger Term Quality | Includes 'tensorflow serving setup' as a trigger term (duplicated), which is a natural phrase users might say. However, it's missing common variations like 'TF Serving', 'model serving', 'deploy tensorflow model', 'serve ML model', or 'production deployment'. | 2 / 3 |
Distinctiveness Conflict Risk | The mention of 'Tensorflow Serving' provides some specificity that distinguishes it from generic ML skills, but 'ML Deployment' is broad and could overlap with other deployment-related skills. The lack of specific use cases increases conflict risk. | 2 / 3 |
Total | 6 / 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 entirely meta-content describing what the skill should do rather than providing any actual guidance for TensorFlow Serving setup. It contains zero actionable information—no Docker commands, no model export steps, no serving configuration examples, no gRPC/REST endpoint setup. The content is essentially a placeholder template.
Suggestions
Add concrete TensorFlow Serving setup steps: model export with SavedModel format, Docker container commands, and serving configuration examples
Include executable code snippets for common operations like `docker run -p 8501:8501 --mount type=bind,source=/path/to/model,target=/models/model -e MODEL_NAME=model tensorflow/serving`
Add a validation workflow: how to test the serving endpoint is working (curl commands, health checks, prediction requests)
Remove all meta-description content ('This skill provides...', 'When to Use...') and replace with actual technical instructions
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about TensorFlow Serving. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude already understands. | 1 / 3 |
Actionability | No concrete code, commands, or specific instructions are provided. The skill describes what it does abstractly ('provides step-by-step guidance') but never actually provides any guidance, configurations, or executable examples. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps for setting up TensorFlow Serving, no validation checkpoints, and no sequence of operations. The content only describes trigger phrases. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of meta-description with no actual technical content to organize. There are no references to detailed documentation, examples, or related files. | 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
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