prometheus-configuration
Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.
Install with Tessl CLI
npx tessl i github:wshobson/agents --skill prometheus-configuration76
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
67%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 description has good structure with an explicit 'Use when' clause that clearly communicates both purpose and triggers. However, it lacks specific concrete actions beyond high-level concepts and could benefit from more natural trigger terms that users might actually say. The Prometheus-specific focus helps with distinctiveness but generic monitoring terms create potential overlap.
Suggestions
Add specific concrete actions like 'configure scrape targets, write PromQL queries, define alerting rules, set up service discovery'
Include additional natural trigger terms users might say: 'observability', 'time-series metrics', 'scraping endpoints', 'PromQL', '.yml config'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Prometheus, monitoring) and some actions (metric collection, storage, monitoring, alerting), but lacks specific concrete actions like 'configure scrape targets', 'write PromQL queries', or 'set up alert rules'. | 2 / 3 |
Completeness | Clearly answers both what (set up Prometheus for metric collection, storage, and monitoring) and when (implementing metrics collection, setting up monitoring infrastructure, configuring alerting systems) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes relevant keywords like 'Prometheus', 'metrics collection', 'monitoring infrastructure', and 'alerting systems', but misses common variations users might say like 'observability', 'scraping', 'PromQL', 'Grafana integration', or 'time-series database'. | 2 / 3 |
Distinctiveness Conflict Risk | The mention of 'Prometheus' provides some distinctiveness, but 'monitoring infrastructure' and 'alerting systems' could overlap with other monitoring tools like Datadog, Grafana, or generic observability skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
72%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 excellent executable examples and good progressive disclosure through external references. The main weaknesses are some verbosity in introductory sections and lack of an explicit end-to-end workflow with validation checkpoints integrated into the process flow.
Suggestions
Add an explicit numbered workflow at the top showing the complete setup sequence: install -> configure -> validate -> deploy -> verify, with validation as a required checkpoint before deployment.
Remove or significantly trim the 'Purpose', 'When to Use', and architecture diagram sections - Claude can infer these from context and the description field.
Consolidate the 10 best practices into the most critical 3-4 that are non-obvious and specific to Prometheus.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary sections like the architecture diagram and extensive 'When to Use' bullets that Claude can infer. The 'Best Practices' section lists 10 items that are somewhat generic and could be trimmed. | 2 / 3 |
Actionability | Provides fully executable code examples throughout - Helm commands, Docker Compose files, complete YAML configurations, and validation commands are all copy-paste ready with specific values and proper syntax. | 3 / 3 |
Workflow Clarity | While individual sections are clear, there's no explicit end-to-end workflow with validation checkpoints. The validation section exists but isn't integrated into a 'configure -> validate -> deploy -> verify' sequence with feedback loops. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections, appropriate references to external files (assets/prometheus.yml.template, references/scrape-configs.md, etc.), and related skills linked at the end. References are one level deep and clearly signaled. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
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