railway-observability

Railway.com built-in metrics, monitoring dashboards, alerting (Pro plan), and external OTEL integration with Grafana. Use when setting up monitoring, creating dashboards, configuring alerts, integrating Prometheus/Loki/Tempo, deploying Grafana stack, or analyzing Railway service metrics.

Quality

68%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/railway-observability/SKILL.md

Quality

Content

50%Weight 40%Scale 1-5

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

The body is actionable and well-structured but significantly over-long, restating its core 5-step workflow three times and inlining detail that the reference files already cover. A broken templates/ reference and missing deploy-validation checkpoint further weaken it.

Suggestions

Collapse the Quick Start, Workflow, and Key Features/Grafana Stack Details sections into a single linear workflow; move the full Alloy river config, Python/Node examples, and metrics tables into the existing references/ files and link to them instead.

Add an explicit validation checkpoint after the Grafana stack deployment (e.g., verify each service is healthy and Grafana is reachable) so the batch deploy workflow can clear the workflow_clarity cap.

Fix or remove the templates/alloy-config.river reference — no templates/ directory exists in the bundle — by either adding the file or pointing the inline config to a real path.

Dimension	Reasoning	Score
Conciseness	The ~650-line body repeats the same 5-step workflow three times (Quick Start, Workflow, Key Features/Grafana Stack Details) and re-states retention, ports, and template info multiple times; parentheticals like '(visualization)', '(metrics)' explain tools Claude already knows.	2 / 5
Actionability	Provides mostly executable guidance — concrete OTEL env vars, a complete Python OTel meter example, an Alloy river config, and webhook JSON — with minor gaps where deploy steps are UI-only comments or use uncertain CLI commands ('railway metrics -s').	4 / 5
Workflow Clarity	The 5-step process is clearly sequenced, but the batch Grafana-stack deployment (Step 5) lacks an explicit validation checkpoint; the batch/deploy cap holds this at 3 despite a verify step existing for OTEL integration.	3 / 5
Progressive Disclosure	References are clearly signaled ('See references/otel-integration.md') and the bundle mostly matches, but large blocks that belong in references (full Alloy config, Python/Node examples, Key Features tables) are inlined, and the body references templates/alloy-config.river which does not exist in the bundle.	3 / 5
	Total	12 / 20 Passed

Description

87%Weight 40%Scale 1-5

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, well-scoped description that clearly answers both what the skill does and when to use it, with concrete Railway/Grafana-specific triggers. Minor gaps in keyword synonyms keep trigger quality just below the top.

Dimension	Reasoning	Score
Specificity	Lists several concrete capability areas ('built-in metrics, monitoring dashboards, alerting (Pro plan), and external OTEL integration with Grafana') and multiple task actions, but framing is task-oriented rather than a fully enumerated capability set.	4 / 5
Completeness	Explicitly states both what ('built-in metrics, monitoring dashboards, alerting (Pro plan), and external OTEL integration with Grafana') and when ('Use when setting up monitoring, creating dashboards, configuring alerts...') with concrete trigger phrases, matching the top anchor.	5 / 5
Trigger Term Quality	Strong natural triggers ('setting up monitoring, creating dashboards, configuring alerts') plus tool names (Prometheus/Loki/Tempo, Grafana stack), but omits common variations like 'logs', 'tracing', and the word 'observability' itself.	4 / 5
Distinctiveness Conflict Risk	Scoped tightly to Railway.com observability with tool-specific triggers (Grafana/OTEL/Prometheus), giving it a clear niche with minimal overlap risk against generic monitoring skills.	5 / 5
	Total	18 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (658 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: fernandezbaptiste/Skrillz
Path: skills/railway-observability/SKILL.md
Commit: 93ed392

Reviewed: 1 day ago

Table of Contents

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