prediction-monitor
Prediction Monitor - Auto-activating skill for ML Deployment. Triggers on: prediction monitor, prediction monitor Part of the ML Deployment skill category.
35
Quality
3%
Does it follow best practices?
Impact
93%
1.03xAverage 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/prediction-monitor/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 severely underdeveloped, functioning more as a label than a useful skill description. It provides no information about what capabilities the skill offers, what actions it can perform, or specific scenarios when it should be activated. The duplicate trigger term suggests a template that wasn't properly filled out.
Suggestions
Add specific capabilities describing what the skill does, e.g., 'Tracks prediction accuracy, detects model drift, monitors inference latency, and alerts on anomalies in deployed ML models.'
Include a 'Use when...' clause with explicit triggers like 'Use when monitoring deployed models, checking prediction quality, detecting drift, or analyzing inference performance.'
Expand trigger terms to include natural variations: 'model monitoring', 'prediction drift', 'inference metrics', 'model performance tracking', 'ML observability'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Prediction Monitor') and its category ('ML Deployment') without describing any concrete actions. There are no verbs indicating what the skill actually does. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' (no capabilities listed) and 'when should Claude use it' (no explicit use-case guidance beyond the generic trigger phrase). Both components are missing or very weak. | 1 / 3 |
Trigger Term Quality | The trigger terms are redundant ('prediction monitor, prediction monitor' - duplicated) and overly narrow. Missing natural variations users might say like 'model monitoring', 'inference tracking', 'prediction drift', or 'ML observability'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'prediction monitor' is somewhat specific to ML monitoring, the lack of detail about what distinguishes this from other ML-related skills (model training, deployment, evaluation) creates potential overlap risk within the ML Deployment category. | 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 content is essentially a placeholder template with no actual instructional value. It describes what a prediction monitoring skill would do without providing any concrete guidance, code examples, monitoring metrics, alerting patterns, or implementation details. The content fails all dimensions because it contains only meta-descriptions rather than actionable knowledge.
Suggestions
Add concrete code examples for setting up prediction monitoring (e.g., tracking prediction latency, throughput, data drift detection)
Include specific metrics to monitor (prediction confidence distributions, feature value ranges, model staleness indicators) with threshold examples
Provide a clear workflow for implementing monitoring: instrument model -> define alerts -> set up dashboards -> establish feedback loops
Replace generic capability claims with actual implementation patterns, tool configurations (Prometheus, Grafana, custom solutions), or integration code
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude doesn't need and add no actionable value. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms ('provides step-by-step guidance') but never actually provides any guidance, examples, or executable content. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The content claims to provide 'step-by-step guidance' but contains zero actual steps for prediction monitoring tasks. | 1 / 3 |
Progressive Disclosure | No structure for discovery or navigation. The content is a flat list of vague claims with no references to detailed materials, examples, or related documentation that would help Claude learn the actual skill. | 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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