prediction-monitor
Prediction Monitor - Auto-activating skill for ML Deployment. Triggers on: prediction monitor, prediction monitor Part of the ML Deployment skill category.
33
0%
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
0%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 with no substantive content. It names a category and repeats the skill name as a trigger term but provides zero information about what the skill does, what actions it performs, or when it should be selected. It would be nearly impossible for Claude to correctly choose this skill from a pool of ML-related skills.
Suggestions
Add concrete actions describing what the skill does, e.g., 'Monitors ML model prediction quality, detects data drift, tracks prediction accuracy metrics, and alerts on anomalies in inference pipelines.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user mentions prediction monitoring, model drift, inference quality, prediction accuracy, or deployed model performance.'
Diversify trigger terms beyond just 'prediction monitor' to include natural variations like 'model predictions', 'prediction drift', 'inference monitoring', 'model performance tracking', 'prediction alerts'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('ML Deployment') and a label ('Prediction Monitor') but describes no concrete actions whatsoever. There is no indication of what the skill actually does—no verbs like 'monitors', 'alerts', 'tracks', 'analyzes', etc. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause or equivalent guidance. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'prediction monitor' repeated twice. There are no natural variations a user might say, such as 'model predictions', 'inference monitoring', 'prediction drift', 'model performance', or 'prediction accuracy'. | 1 / 3 |
Distinctiveness Conflict Risk | The description is too vague to be distinguishable. 'ML Deployment' is broad, and without specific actions or triggers, it could easily conflict with other ML-related skills covering deployment, monitoring, or predictions. | 1 / 3 |
Total | 4 / 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 template with no substantive content. It contains only generic boilerplate descriptions that provide no actionable guidance on prediction monitoring—no metrics to track (data drift, prediction latency, accuracy degradation), no code examples, no tool recommendations, and no workflows. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable code examples for setting up prediction monitoring (e.g., tracking data drift with evidently, logging prediction latency with Prometheus, setting up alerting thresholds).
Define a clear multi-step workflow for implementing prediction monitoring: instrument model serving → define metrics/thresholds → set up dashboards → configure alerts → validate with test predictions.
Replace all generic placeholder text ('Provides step-by-step guidance') with specific, actionable instructions that assume Claude's competence—focus only on domain-specific knowledge Claude wouldn't already have.
Add references to supporting files or inline sections covering specific monitoring patterns (e.g., drift detection, performance degradation, A/B test monitoring) with concrete configuration examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, provides no domain-specific information about prediction monitoring, and every section is generic placeholder text that could apply to any skill. | 1 / 3 |
Actionability | There is zero concrete guidance—no code, no commands, no specific tools, no monitoring metrics, no configuration examples. Every bullet point is vague and abstract (e.g., 'Provides step-by-step guidance' without any actual steps). | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequence, no validation checkpoints. For a monitoring skill that could involve destructive or batch operations, this is a critical gap. | 1 / 3 |
Progressive Disclosure | No bundle files exist, no references to external documents, and the content itself is a flat, shallow placeholder with no meaningful structure or navigation to deeper content. | 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
13d35b8
- Reviewed
Table of Contents
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