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 skill 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 alternatives.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Monitors ML model prediction quality, detects data drift, tracks accuracy metrics, and alerts on prediction anomalies in deployed models.'
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 tracking, or deployed model performance.'
Expand trigger terms to include natural variations users would say, such as 'model monitoring', 'prediction drift', 'inference tracking', 'model performance', 'prediction quality', and 'deployed model 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 'Prediction Monitor' without further detail could overlap with any monitoring, ML, or deployment-related skill. | 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 actual content about prediction monitoring. It contains no executable code, no specific ML monitoring concepts (e.g., data drift detection, model performance tracking, alerting thresholds), and no actionable guidance. It reads as auto-generated boilerplate that would provide zero value to Claude when handling prediction monitoring tasks.
Suggestions
Add concrete, executable code examples for prediction monitoring tasks (e.g., setting up drift detection with evidently, configuring Prometheus metrics for model latency/accuracy, implementing alerting thresholds).
Define a clear workflow for setting up prediction monitoring: instrument model serving → define metrics → configure alerts → validate monitoring pipeline → handle degradation scenarios.
Replace the generic 'Capabilities' and 'Example Triggers' sections with actual technical content: specific metrics to track (prediction latency, feature drift, accuracy decay), tools to use, and configuration examples.
Add validation checkpoints such as verifying that monitoring endpoints are reachable, alerts fire correctly on test data, and dashboards display expected metrics.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know and provides zero domain-specific information about prediction monitoring. Every section restates the same vague concept without adding value. | 1 / 3 |
Actionability | There are no concrete steps, code examples, commands, configurations, or specific guidance. The skill describes what it claims to do ('provides step-by-step guidance') without actually providing any guidance whatsoever. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps, no sequence, no validation checkpoints. The skill merely lists abstract capabilities like 'validates outputs against common standards' without specifying what standards or how to validate. | 1 / 3 |
Progressive Disclosure | There is no meaningful content to organize, no references to detailed materials, and no navigation structure. The sections are just repetitive restatements of the skill name with no substance to disclose progressively. | 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
c8a915c
- Reviewed
Table of Contents
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