tracking-application-response-times
Track and optimize application response times across API endpoints, database queries, and service calls. Use when monitoring performance or identifying bottlenecks. Trigger with phrases like "track response times", "monitor API performance", or "analyze latency".
57
50%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/performance/response-time-tracker/skills/tracking-application-response-times/SKILL.mdQuality
Discovery
100%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 well-crafted skill description that clearly specifies concrete capabilities (tracking response times across API endpoints, database queries, and service calls), provides explicit 'Use when' guidance, and includes natural trigger phrases. It uses proper third-person voice and is concise without being vague. The description is distinctive enough to avoid conflicts with other performance-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Track and optimize application response times across API endpoints, database queries, and service calls.' This clearly names the domain and enumerates specific areas of focus. | 3 / 3 |
Completeness | Clearly answers both 'what' (track and optimize response times across API endpoints, database queries, and service calls) and 'when' (explicit 'Use when monitoring performance or identifying bottlenecks' plus trigger phrases). | 3 / 3 |
Trigger Term Quality | Includes natural trigger phrases users would say: 'track response times', 'monitor API performance', 'analyze latency', plus domain terms like 'API endpoints', 'database queries', 'service calls', 'bottlenecks', and 'performance'. | 3 / 3 |
Distinctiveness Conflict Risk | Focuses on a clear niche of response time tracking and latency analysis across specific infrastructure components. The trigger terms are distinct and unlikely to conflict with general coding or deployment skills. | 3 / 3 |
Total | 12 / 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 almost entirely descriptive and abstract, reading more like a product marketing page than actionable instructions for Claude. It lacks any concrete code, commands, data formats, or specific implementation details. The content is highly redundant across sections and explains concepts Claude already understands while failing to provide the actual technical guidance needed to perform response time tracking.
Suggestions
Replace abstract descriptions with concrete, executable code examples showing how to collect, parse, and analyze response time data (e.g., Python scripts for calculating percentiles, parsing APM output, or querying metrics endpoints).
Consolidate redundant sections (Overview, How It Works, When to Use, Examples) into a single concise overview, and use the freed space for actual implementation details like data formats, specific tool commands, and calculation methods.
Add explicit validation steps to the workflow, such as verifying metric collection is working, validating data format before analysis, and confirming SLO threshold definitions before compliance checking.
Define concrete input/output formats (e.g., expected CSV/JSON schema for response time data, exact format of the percentile report output) so Claude knows exactly what to produce.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive padding. The 'Overview' section explains what the skill does in vague marketing language ('empowers Claude to proactively monitor'). 'When to Use This Skill', 'How It Works', 'Best Practices', 'Integration', and 'Resources' sections all contain generic information Claude already knows. Most of the content describes rather than instructs. | 1 / 3 |
Actionability | No concrete code, commands, or executable guidance anywhere. The entire skill is abstract descriptions like 'Configure monitoring for API endpoints' and 'Collect response time metrics' without any actual implementation. Examples describe what the skill 'will do' rather than showing how to do it. No code snippets, no specific tool commands, no data formats. | 1 / 3 |
Workflow Clarity | The 'Instructions' section lists 6 high-level steps but they are vague directives ('Configure monitoring', 'Collect response time metrics', 'Analyze trends') with no specifics on how to accomplish any of them. No validation checkpoints, no feedback loops, no error recovery steps within the workflow. The error handling section is also just a generic checklist. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files despite mentioning a metrics directory path. Content is poorly organized with redundant sections (Overview, How It Works, When to Use, Examples, Instructions all overlap). No bundle files exist to support the content, and no clear navigation structure. | 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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