exa-observability
Set up monitoring, metrics, and alerting for Exa search integrations. Use when implementing monitoring for Exa operations, building dashboards, or configuring alerting for search quality and latency. Trigger with phrases like "exa monitoring", "exa metrics", "exa observability", "monitor exa", "exa alerts", "exa dashboard".
80
77%
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/saas-packs/exa-pack/skills/exa-observability/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 well-structured skill description with excellent trigger term coverage and clear completeness. Its main weakness is that the capability descriptions are somewhat generic monitoring concepts rather than highly specific actions. The explicit 'Use when' and 'Trigger with' clauses make it very effective for skill selection.
Suggestions
Add more specific concrete actions beyond generic monitoring terms, e.g., 'track search latency percentiles, monitor API error rates, set up SLO-based alerts, create real-time search quality dashboards'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (monitoring/metrics/alerting for Exa search integrations) and some actions (implementing monitoring, building dashboards, configuring alerting), but the actions are somewhat generic monitoring concepts rather than highly specific concrete capabilities like 'track search latency percentiles, set up SLO-based alerts, create Grafana dashboards'. | 2 / 3 |
Completeness | Clearly answers both 'what' (set up monitoring, metrics, and alerting for Exa search integrations) and 'when' (explicit 'Use when' clause plus 'Trigger with phrases like' section). Both components are well-articulated. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'exa monitoring', 'exa metrics', 'exa observability', 'monitor exa', 'exa alerts', 'exa dashboard'. These are terms users would naturally use, and the explicit listing of trigger phrases is very helpful for skill selection. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific combination of 'Exa' (a specific product) with monitoring/observability. The 'exa' prefix on all trigger terms makes it very unlikely to conflict with generic monitoring skills or other Exa-related skills. | 3 / 3 |
Total | 11 / 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 solid, actionable skill with concrete TypeScript code and Prometheus alert rules that can be directly used. Its main weaknesses are verbosity in the code examples (particularly the cache monitoring class) and the lack of validation checkpoints between steps to verify the monitoring pipeline is working correctly before proceeding. The dashboard panel table and error handling table are well-structured additions.
Suggestions
Add validation checkpoints between steps, e.g., 'After Step 1, verify metrics appear in your backend before proceeding' and 'After Step 4, trigger a test alert to confirm routing works'.
Consider moving the full MonitoredCache class and result usage tracking to a referenced file, keeping only the key metric names and a minimal example inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and provides useful, specific content, but some sections like the cache monitoring class and result usage tracking are quite verbose. The overview section is lean, but the code blocks could be tightened—e.g., the MonitoredCache class includes boilerplate that Claude could generate from a shorter specification. | 2 / 3 |
Actionability | The skill provides fully executable TypeScript code for instrumentation, caching, and health checks, plus copy-paste-ready Prometheus alert rules in YAML. The generic emitMetric pattern with inline comments showing Prometheus/Datadog/OTel equivalents is practical and concrete. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced (instrument → track quality → cache → alerts → health check), but there are no validation checkpoints or feedback loops. For a monitoring setup involving production systems, there should be explicit verification steps (e.g., 'verify metrics are flowing before proceeding to alert rules'). | 2 / 3 |
Progressive Disclosure | The skill has good structural organization with clear sections, tables for dashboard panels and error handling, and references to external resources and next steps. However, the inline code is quite long (~120 lines of code blocks) and some sections like the full cache class could be split into a referenced file to keep the main skill leaner. | 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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