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.

1.33x

Quality

58%

Does it follow best practices?

Impact

100%

1.33x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/observability-monitoring/skills/prometheus-configuration/SKILL.md

Quality

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 is structurally sound with a clear 'Use when' clause and identifies the core domain (Prometheus monitoring). However, it lacks specific concrete actions beyond high-level categories and could better differentiate itself from other monitoring tool skills. Adding more specific capabilities and natural trigger terms would strengthen it.

Suggestions

Add more specific concrete actions such as 'configure scrape targets, write PromQL queries, set up Alertmanager rules, define recording rules, configure exporters'.

Include additional natural trigger terms users might say, such as 'PromQL', 'scrape config', 'node_exporter', 'time series database', or 'prometheus.yml'.

Clarify distinctiveness by specifying this is specifically for Prometheus (not Grafana, Datadog, etc.) in the 'Use when' clause, e.g., 'Use when the user mentions Prometheus, PromQL, or needs a pull-based metrics system'.

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' (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', 'scrape config', 'Grafana', 'exporters', 'node_exporter', 'time series', or '.yml configuration'.	2 / 3
Distinctiveness Conflict Risk	Mentioning 'Prometheus' specifically helps distinguish it, but the broad 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 highly actionable, production-ready Prometheus configurations with concrete examples, which is its main strength. However, it is far too verbose—inlining hundreds of lines of YAML that should live in referenced files, explaining architecture Claude already knows, and including generic best practices. The workflow could be improved by integrating validation steps into a clear sequential process.

Suggestions

Move the large YAML configuration blocks (prometheus.yml, recording rules, alert rules) into the referenced files and keep only minimal examples inline in SKILL.md

Remove the architecture diagram, 'Purpose' section, and 'When to Use' list—these duplicate the frontmatter description and explain concepts Claude already knows

Add a clear numbered workflow: 1. Install → 2. Configure → 3. Validate with promtool (fix if errors) → 4. Deploy → 5. Verify targets are scraped

Trim the 10 best practices to only non-obvious, project-specific conventions rather than generic Prometheus advice

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~300+ lines. It explains Prometheus architecture (which Claude already knows), includes a full ASCII diagram, lists 10 best practices that are generic knowledge, and provides exhaustive YAML configs that could be referenced externally. The 'When to Use' and 'Purpose' sections are redundant with each other and with the frontmatter description.	1 / 3
Actionability	The content 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 examples are concrete and specific.	3 / 3
Workflow Clarity	While there is a validation section with promtool commands, there's no clear sequenced workflow tying installation → configuration → validation → deployment together. The validation steps exist but aren't integrated into a feedback loop (e.g., 'validate before deploying, fix errors, re-validate'). For a configuration-heavy skill involving infrastructure changes, this gap is notable.	2 / 3
Progressive Disclosure	There are references to external files (assets/prometheus.yml.template, references/scrape-configs.md, references/recording-rules.md, scripts/validate-prometheus.sh), which is good. However, the SKILL.md itself is monolithic with massive inline YAML blocks that should be in those referenced files instead. The overview doesn't stay concise—it duplicates what the references should contain.	2 / 3
	Total	8 / 12 Passed

Validation

100%

Warnings & errors only

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: wshobson/agents
Commit: 91fe43e

Reviewed: 8 days ago

Table of Contents

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