groq-observability
Set up observability for Groq integrations: latency histograms, token throughput, rate limit gauges, cost tracking, and Prometheus alerts. Trigger with phrases like "groq monitoring", "groq metrics", "groq observability", "monitor groq", "groq alerts", "groq dashboard".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
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 defines specific capabilities (latency histograms, token throughput, rate limit gauges, cost tracking, Prometheus alerts) within a well-scoped domain (Groq observability). It includes explicit trigger phrases covering natural user language variations, and the narrow focus on Groq monitoring makes it highly distinctive and unlikely to conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: latency histograms, token throughput, rate limit gauges, cost tracking, and Prometheus alerts. These are clearly defined, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (set up observability with specific metrics and alerts) and 'when' (explicit trigger phrases provided). The 'Trigger with phrases like...' clause serves as an explicit 'Use when' equivalent. | 3 / 3 |
Trigger Term Quality | Includes natural trigger phrases users would say: 'groq monitoring', 'groq metrics', 'groq observability', 'monitor groq', 'groq alerts', 'groq dashboard'. Good coverage of natural variations combining the domain (Groq) with common monitoring-related terms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific combination of Groq + observability/monitoring. The Groq-specific focus and Prometheus tooling make it unlikely to conflict with generic monitoring or other LLM provider skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a highly actionable skill with complete, executable code examples covering the full observability stack for Groq. Its main weaknesses are the monolithic structure (all code inline rather than split into referenced files) and the lack of validation checkpoints to verify the monitoring pipeline is working correctly. The content could be tightened by removing explanatory comments Claude doesn't need and splitting detailed configurations into separate files.
Suggestions
Add a validation step after setup (e.g., 'Step 7: Verify - make a test request and confirm metrics appear at /metrics endpoint') to improve workflow clarity.
Split the Prometheus alert rules and Grafana panel definitions into separate referenced files (e.g., groq-alerts.yml, DASHBOARD.md) to improve progressive disclosure.
Remove the 'Why' column from the metrics table and trim inline comments that restate obvious purpose — Claude can infer why you'd track error rates or token usage.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good use of tables and code, but includes some unnecessary context (e.g., explaining Groq's speed advantage, 'Groq's main value prop' comments, the overview paragraph). The metrics table's 'Why' column adds marginal value. The pricing data is time-sensitive and could become stale. | 2 / 3 |
Actionability | Fully executable TypeScript code with complete type definitions, Prometheus metric declarations, YAML alert rules, and structured logging. Code is copy-paste ready with real metric names, bucket values, and pricing data. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced (instrumented client → metrics → rate limits → alerts → logging → dashboard), but there are no validation checkpoints. There's no step to verify metrics are actually being emitted, no test request to confirm the pipeline works, and no feedback loop for debugging misconfigured alerts or missing headers. | 2 / 3 |
Progressive Disclosure | The content is quite long with all code inline. The dashboard panel descriptions, error handling table, and alert rules could be split into separate reference files. The 'Next Steps' reference to groq-incident-runbook is good but the main content is monolithic. | 2 / 3 |
Total | 9 / 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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