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.
72
58%
Does it follow best practices?
Impact
94%
1.22xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/observability-monitoring/skills/prometheus-configuration/SKILL.mdQuality
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.
The description has a solid structure with an explicit 'Use when' clause, which is its strongest aspect. However, it lacks specificity in the concrete actions Prometheus can perform and could benefit from more natural trigger terms that users would use. The broad monitoring/alerting language creates some overlap risk with other monitoring tool skills.
Suggestions
Add more specific concrete actions like 'configure scrape targets, write PromQL queries, set up Alertmanager rules, define recording rules, configure exporters'.
Expand trigger terms to include common variations users would say: 'PromQL', 'scrape config', 'exporters', 'node_exporter', 'prometheus.yml', 'time series database'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Prometheus, monitoring) and some actions (metric collection, storage, monitoring, alerting), but doesn't list multiple concrete specific actions like configuring scrape targets, writing PromQL queries, setting up Alertmanager rules, or creating recording rules. | 2 / 3 |
Completeness | Clearly answers both 'what' (set up Prometheus for metric collection, storage, and monitoring) and 'when' with an explicit 'Use when' clause covering metrics collection, monitoring infrastructure, and alerting systems. | 3 / 3 |
Trigger Term Quality | Includes relevant keywords like 'Prometheus', 'metrics collection', 'monitoring infrastructure', and 'alerting systems', but misses common user variations like 'PromQL', 'Grafana', 'scrape config', 'exporters', 'node_exporter', 'prometheus.yml', or 'time series'. | 2 / 3 |
Distinctiveness Conflict Risk | Mentioning 'Prometheus' specifically helps distinguish it, but the broader terms 'monitoring infrastructure' and 'alerting systems' could overlap with skills for Grafana, Datadog, Nagios, or other monitoring tools. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides excellent, actionable Prometheus configuration examples that are copy-paste ready, which is its primary strength. However, it is significantly too verbose—including architecture diagrams, purpose sections, and best practices lists that Claude already knows—consuming far more tokens than necessary. The content would benefit from aggressive trimming and better leveraging the referenced external files rather than duplicating content inline.
Suggestions
Remove the architecture diagram, 'Purpose', 'When to Use', and 'Best Practices' sections—Claude already knows Prometheus architecture and general monitoring best practices. This alone would save ~40 lines.
Move the detailed scrape configuration patterns, recording rules, and alert rules into their referenced files (references/scrape-configs.md, references/recording-rules.md) and keep only a minimal example of each inline.
Add a clear numbered end-to-end workflow at the top: Install → Configure prometheus.yml → Add rules → Validate with promtool → Deploy → Verify targets, with validation as an explicit gate before deployment.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines. It explains Prometheus architecture (which Claude already knows), includes a 'When to Use' section that restates the description, provides an ASCII diagram, and lists 10 best practices that are common knowledge. The architecture overview, purpose section, and much of the explanatory text waste tokens. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready configurations: complete prometheus.yml, Helm install commands, Docker Compose files, recording rules, alert rules, and validation commands. All code blocks are concrete and specific rather than pseudocode. | 3 / 3 |
Workflow Clarity | While individual sections are clear, there's no overall sequenced workflow tying the pieces together (e.g., install → configure → validate → deploy). The validation section exists but isn't integrated into the workflow as explicit checkpoints. For a configuration-heavy skill involving infrastructure changes, the lack of a clear end-to-end sequence with validation gates is a gap. | 2 / 3 |
Progressive Disclosure | References to external files are present (assets/prometheus.yml.template, references/scrape-configs.md, etc.) and a Reference Files section exists, but the main file itself is monolithic with extensive inline content that could be split out. The scrape configuration patterns, recording rules, and alert rules sections are all fully inlined despite having referenced files, defeating the purpose of progressive disclosure. | 2 / 3 |
Total | 8 / 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.
- Repository
- Dicklesworthstone/pi_agent_rust
- Commit
47823e3
- Reviewed
Table of Contents
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