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 strong skill description that clearly articulates specific 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 would effectively differentiate this skill from others in a large skill library.
| 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 capability. | 3 / 3 |
Completeness | Clearly answers both what ('Track and optimize application response times across API endpoints, database queries, and service calls') and when ('Use when monitoring performance or identifying bottlenecks. Trigger with phrases like...'). | 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', and 'bottlenecks'. | 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, with no executable code, concrete commands, or specific implementation details. It reads like a marketing overview rather than an actionable skill file. Every section restates the same high-level concepts without providing Claude with anything it can actually execute or follow.
Suggestions
Replace abstract descriptions with concrete, executable code examples — e.g., actual Python/shell scripts for collecting response time metrics, computing percentiles, and generating reports.
Remove redundant sections (Overview, How It Works, When to Use, Integration) that explain concepts Claude already knows, and consolidate into a lean quick-start with specific commands.
Add explicit validation checkpoints to the workflow — e.g., 'Verify metrics are being collected by running X command and checking for Y output' before proceeding to analysis.
Define concrete data formats (e.g., JSON schema for response time records, expected file structure in metrics/response-times/) so Claude knows exactly what to produce and consume.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive padding. Sections like 'Overview', 'How It Works', 'When to Use This Skill', 'Integration', and 'Best Practices' explain concepts Claude already knows and add no actionable value. The content is full of generic filler like 'This skill empowers Claude to proactively monitor and improve application performance.' | 1 / 3 |
Actionability | No concrete code, commands, or executable guidance anywhere. Everything is abstract and descriptive — 'Configure monitoring for API endpoints', 'Collect response time metrics' — without showing how. No actual implementation, no scripts, no tool invocations, no data formats. The examples describe what the skill 'will do' rather than showing how to do it. | 1 / 3 |
Workflow Clarity | The 'Instructions' section lists 6 vague steps with no specifics on how to execute any of them, no validation checkpoints, and no feedback loops. Steps like 'Configure monitoring' and 'Analyze trends' are not actionable sequences. Error handling is a generic checklist with no concrete commands or verification steps. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with many sections that repeat similar information. No references to external files for detailed content. The skill mentions '${CLAUDE_SKILL_DIR}/metrics/response-times/' but never explains the structure or contents. Multiple sections (Overview, How It Works, When to Use, Examples) all say roughly the same thing at different levels of vagueness. | 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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