aggregating-performance-metrics
Aggregate and centralize performance metrics from applications, systems, databases, caches, and services. Use when consolidating monitoring data from multiple sources. Trigger with phrases like "aggregate metrics", "centralize monitoring", or "collect performance data".
43
44%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/performance/metrics-aggregator/skills/aggregating-performance-metrics/SKILL.mdQuality
Discovery
89%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 is a solid description that excels in completeness and trigger term quality by providing explicit 'Use when' and 'Trigger with' clauses. The main weakness is that the specific capabilities could be more detailed—'aggregate and centralize' are somewhat high-level actions that don't fully convey what concrete operations the skill performs. Overall, it would perform well in a multi-skill selection scenario.
Suggestions
Expand the capability list with more specific actions, e.g., 'normalize metric formats, correlate cross-service data, generate unified dashboards' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (performance metrics) and lists sources (applications, systems, databases, caches, services), but the actions are limited to 'aggregate and centralize' without detailing specific concrete operations like creating dashboards, setting alerts, or transforming data formats. | 2 / 3 |
Completeness | Clearly answers both 'what' (aggregate and centralize performance metrics from multiple source types) and 'when' (explicit 'Use when' clause plus 'Trigger with phrases like' providing concrete trigger terms). | 3 / 3 |
Trigger Term Quality | Includes explicit trigger phrases ('aggregate metrics', 'centralize monitoring', 'collect performance data') and natural keywords like 'performance metrics', 'monitoring data', 'databases', 'caches', and 'services' that users would naturally use when requesting this capability. | 3 / 3 |
Distinctiveness Conflict Risk | The focus on aggregating/centralizing performance metrics from multiple monitoring sources is a clear niche. The specific trigger phrases and domain (monitoring, metrics consolidation) make it unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 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 reads like a marketing description or README rather than actionable instructions for Claude. It contains no executable code, no concrete configuration examples, and no specific commands—just abstract descriptions of what the skill 'will do.' The content is repetitive across sections (Overview, How It Works, When to Use, Examples all say roughly the same thing) and wastes tokens on explanations Claude doesn't need.
Suggestions
Replace abstract descriptions with concrete, executable examples: include actual Prometheus scrape configs, StatsD client code snippets, and CloudWatch API calls that Claude can directly use or adapt.
Consolidate the redundant sections (Overview, How It Works, When to Use, Examples) into a concise quick-start section with a specific end-to-end example showing metrics aggregation configuration.
Add validation checkpoints to the workflow, such as 'verify Prometheus targets are up with `curl localhost:9090/api/v1/targets`' or 'confirm metrics are being scraped by checking the /metrics endpoint'.
Provide a concrete metrics naming convention template (e.g., `{service}_{subsystem}_{metric}_{unit}`) with specific examples rather than just advising to 'use a consistent naming convention'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is highly verbose, explaining what Claude will do in abstract terms rather than providing actionable information. Sections like 'How It Works', 'When to Use This Skill', and 'Overview' repeat the same information in different ways and explain things Claude already knows. The 'Integration' section adds no value. | 1 / 3 |
Actionability | There is no concrete code, no executable commands, no configuration snippets, and no specific examples. Everything is described at a high level ('Guide the user in configuring Prometheus') without any actual Prometheus config, StatsD setup commands, or CloudWatch API calls. The 'Instructions' section is a vague 6-step list with no specifics. | 1 / 3 |
Workflow Clarity | The workflow steps are vague and lack any validation checkpoints. Steps like 'Configure metric collection from all sources' and 'Set up centralized storage and retention policies' provide no concrete guidance on sequencing, verification, or error recovery. The error handling section is a generic checklist with no actionable steps. | 1 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files, no bundle structure, and no layered organization. All sections are at the same level of abstraction (vague), and there's no separation of quick-start content from advanced material. The referenced path '${CLAUDE_SKILL_DIR}/metrics/' has no supporting bundle 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 | |
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