customerio-observability
Set up Customer.io monitoring and observability. Use when implementing metrics, structured logging, alerting, or Grafana dashboards for Customer.io integrations. Trigger: "customer.io monitoring", "customer.io metrics", "customer.io dashboard", "customer.io alerts", "customer.io observability".
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/customerio-pack/skills/customerio-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 strong completeness and distinctiveness. It clearly identifies both what the skill does and when to use it, with explicit trigger terms. The main weakness is that the capability descriptions could be more concrete—listing specific actions rather than broad categories like 'metrics' and 'alerting'.
Suggestions
Make capabilities more concrete by specifying actions, e.g., 'Configure Prometheus metrics exporters, set up Grafana dashboards, create alerting rules, and implement structured logging for Customer.io webhook integrations.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Customer.io monitoring/observability) and lists some actions (metrics, structured logging, alerting, Grafana dashboards), but these are more categories than concrete specific actions like 'create alert rules' or 'configure Prometheus exporters'. | 2 / 3 |
Completeness | Clearly answers both 'what' (set up Customer.io monitoring and observability) and 'when' (explicit 'Use when' clause with specific scenarios plus a 'Trigger' list of terms). Both components are explicitly stated. | 3 / 3 |
Trigger Term Quality | Includes explicit trigger terms that users would naturally say: 'customer.io monitoring', 'customer.io metrics', 'customer.io dashboard', 'customer.io alerts', 'customer.io observability'. These cover the main natural variations well. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific combination of Customer.io + monitoring/observability. The niche is narrow enough (Customer.io-specific monitoring, not general monitoring or general Customer.io usage) that it's unlikely to conflict with other 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 skill provides highly actionable, production-ready code for Customer.io observability with good coverage of metrics, logging, dashboards, and alerting. Its main weaknesses are the monolithic structure (all code inline rather than split into referenced files) and the lack of validation checkpoints between steps to verify the setup works correctly. The instrumented client section could be more concise by showing the pattern once rather than repeating it three times.
Suggestions
Add validation checkpoints after key steps, e.g., 'Verify metrics endpoint returns data: curl localhost:3000/metrics | grep cio_api' and 'Test an alert fires: temporarily lower threshold and trigger'
Extract the Grafana dashboard JSON and Prometheus alert rules into separate referenced files (e.g., grafana-dashboard.json, customerio-alerts.yml) to reduce the main file length
Condense the instrumented client by showing one fully implemented method, then noting 'Apply the same pattern (startTimer → try/catch → inc error counter) to identify, trackEvent, sendEmail'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is quite long (~250 lines of code) with some repetitive patterns across the instrumented client methods (identify, trackEvent, sendEmail all follow the same try/catch pattern). The metrics table and error handling table are efficient, but the full instrumented client code could be condensed by showing one method and noting the pattern applies to others. | 2 / 3 |
Actionability | Every step provides fully executable TypeScript code with correct imports, proper types, and real library APIs (prom-client, pino, customerio-node, express). The Grafana dashboard JSON and Prometheus alerting YAML are copy-paste ready with real PromQL expressions. | 3 / 3 |
Workflow Clarity | The 7 steps are logically sequenced (define metrics → instrument client → add logging → collect webhook metrics → expose endpoint → dashboard → alerts), but there are no validation checkpoints. No step verifies that metrics are being scraped correctly, that the dashboard loads, or that alerts fire as expected. For an observability setup involving multiple interconnected systems, verification steps are important. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of code that could benefit from splitting the Grafana dashboard JSON and Prometheus alert rules into separate referenced files. The reference to 'customerio-webhooks-events skill' and 'customerio-advanced-troubleshooting' shows some cross-referencing, but the main file is very long with inline content that could be externalized. | 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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