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.

Quality

82%

Does it follow best practices?

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Securityby

Medium

Suggest reviewing before use

Quality

Content

65%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable, code-rich guide with solid validation commands, but it is long and monolithic with some redundant examples and only an implicit workflow sequence. Splitting reference material into bundled files and tightening repetition would improve it.

Suggestions

Move recording rules, alert rules, and scrape-config examples into reference files under ./references/ and link to them from SKILL.md to apply progressive disclosure.

Replace the generic 'Instructions' list with an explicit numbered setup workflow (install -> configure -> define rules -> validate) with a 'fix and re-validate' feedback loop.

Remove the duplicated Kubernetes scrape/relabel block and the explanatory filler phrases to tighten token efficiency.

Dimension	Reasoning	Score
Conciseness	Mostly efficient concrete config, but the ~390-line body repeats material (e.g., Kubernetes scrape/relabel examples appear in both prometheus.yml and a separate Service Discovery section) and includes light filler like 'Create pre-computed metrics for frequently queried expressions' that could be trimmed.	2 / 3
Actionability	Provides fully executable, copy-paste-ready artifacts — helm install commands, docker-compose, prometheus.yml, recording/alert rule YAML, promtool checks, and curl troubleshooting — with specific examples throughout.	3 / 3
Workflow Clarity	Validation steps exist (promtool check config / check rules), but the setup flow is organized by topic rather than as an explicit sequenced process with error-recovery checkpoints, and the 'Instructions' section is generic boilerplate.	2 / 3
Progressive Disclosure	Well-sectioned but monolithic: all rule examples, scrape configs, and troubleshooting live inline in one file with no bundle references or signaled navigation to deeper material, and the size would benefit from splitting into reference files.	2 / 3
	Total	9 / 12 Passed

Description

100%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that explicitly covers both capability and trigger conditions with concrete, domain-specific language. It is concise, distinct, and free of fluff or over-claims.

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions — 'comprehensive metric collection, storage, and monitoring' plus 'configuring alerting systems' — rather than vague language, matching the anchor that lists several specific actions.	3 / 3
Completeness	Clearly states what it does ('Set up Prometheus for...') and gives an explicit 'Use when...' clause naming the triggering situations, answering both what and when.	3 / 3
Trigger Term Quality	Includes natural terms a user would say — 'Prometheus', 'metrics collection', 'monitoring infrastructure', 'alerting systems' — giving good coverage of likely request phrasing.	3 / 3
Distinctiveness Conflict Risk	Prometheus-specific niche with distinct triggers ('metrics collection', 'monitoring infrastructure', 'alerting systems') makes it unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: rmyndharis/antigravity-skills
Path: skills/prometheus-configuration/SKILL.md
Commit: e63f7dd

Reviewed: 13 days ago

Table of Contents

Discovery Implementation Validation

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