Name: observability-monitoring-monitor-setup
Rating: 46.400000000000006 (1 reviews)
Author: administrakt0r

observability-monitoring-monitor-setup

You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da

Quality

48%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/observability-monitoring-monitor-setup/SKILL.md

The canonical home for this skill is observability-monitoring-monitor-setup in administrakt0r/AI-Agents-Safe-Coding-Skills

Quality

Content

36%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 a generic, templated skill shell: organized into sections but almost entirely abstract, with no executable guidance and a dangling reference to a missing playbook file. It reads as boilerplate rather than a domain skill.

Suggestions

Replace abstract instructions with concrete, executable guidance — example instrumentation snippets, dashboard-as-code (Grafana JSON), or alert/SLO templates — ideally moved into the referenced playbook.

Add explicit validation checkpoints to the workflow (e.g., verify metrics are scraping, confirm traces propagate end-to-end, validate dashboards render) so the sequence has feedback loops.

Create the referenced `resources/implementation-playbook.md` (or point to an existing `references/` file) so the progressive-disclosure reference resolves, and align the path consistently.

Dimension	Reasoning	Score
Conciseness	The body is short (~45 lines) and does not heavily over-explain known concepts, but it repeats the frontmatter opening paragraph verbatim and leans on generic boilerplate ('Apply relevant best practices', 'Provide actionable steps') that could be trimmed.	3 / 5
Actionability	Guidance is high-level and abstract ('Clarify goals, constraints, and required inputs', 'Apply relevant best practices and validate outcomes') with no concrete code, commands, or specific steps; the Output Format lists deliverable names without executable substance.	2 / 5
Workflow Clarity	A rough three-step sequence exists (clarify -> apply best practices -> provide steps and verification) but steps are poorly defined and validation is absent, with no explicit checkpoints for the monitoring-setup process.	2 / 5
Progressive Disclosure	Section headers provide some structure and a single reference is signaled, but the referenced file `resources/implementation-playbook.md` does not exist (no resources/ or references/ directory is present), making the navigation promise unfulfillable.	3 / 5
	Total	10 / 20 Passed

Description

61%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.

The description conveys a clear domain and several concrete capabilities but is hampered by second-person voice, a truncated final clause ('insightful da'), and the complete absence of an explicit 'Use when' trigger. It lands as a competent but unfinished description.

Suggestions

Rewrite in third person and add an explicit 'Use when...' clause with concrete trigger phrases (e.g., 'Use when setting up monitoring, dashboards, alerting, or tracing for a service').

Complete the truncated phrase ('insightful da') to 'dashboards' and consider naming specific tools (Prometheus, Grafana, OpenTelemetry) to boost trigger-term coverage.

Tighten the opening so it leads with capabilities rather than role identity ('You are... expert').

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('Set up metrics collection, distributed tracing, log aggregation, and create insightful da...'), which would warrant a 4, but the description uses second-person voice ('You are a monitoring and observability expert'), which per the judging guidelines reduces the specificity score by 1.	3 / 5
Completeness	The 'what' is clear (set up metrics, tracing, logs, dashboards) but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the guidelines caps completeness at 3.	3 / 5
Trigger Term Quality	Natural user-facing terms are present ('metrics collection', 'distributed tracing', 'log aggregation', 'monitoring', 'observability'), giving good keyword coverage, though a few common synonyms or tool names (e.g. Prometheus, Grafana, dashboards) are absent.	4 / 5
Distinctiveness Conflict Risk	The monitoring/observability niche is mostly distinct with only minor overlap risk against closely related DevOps/SRE skills; trigger terms are specific enough to avoid most conflicts.	4 / 5
	Total	14 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/observability-monitoring-monitor-setup/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

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