observability-engineer

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows.

Quality

50%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

The canonical home for this skill is observability-engineer in rmyndharis/antigravity-skills

Quality

Content

35%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 skill body is a verbose capability catalog with no executable guidance or external references. It communicates domain breadth but fails to instruct concretely or stay lean.

Suggestions

Replace the capability/trait/knowledge bullet walls with a concise overview and move detailed tool catalogs into reference files under references/, linking to them one level deep.

Add concrete, executable artifacts — example PromQL queries, an OpenTelemetry collector config, or an SLO/error-budget template — instead of abstract 'Define signals' instructions.

Insert explicit validation checkpoints into the Response Approach workflow (e.g., 'validate alert fires correctly before enabling routing', 'confirm error-budget burn-rate query returns data').

Dimension	Reasoning	Score
Conciseness	The body is a long enumeration of capability bullets, behavioral traits, and a 'Knowledge Base' restating well-known concepts (Prometheus, Grafana, SOC2/HIPAA, OpenTelemetry) that Claude already knows; this is noticeably verbose padding rather than lean, anchoring at 2.	2 / 5
Actionability	Instructions like 'Define signals, instrumentation, and data retention' and 'Build dashboards and alerts aligned to SLOs' are high-level hints with no executable code, commands, or concrete config — minimal concrete guidance matching anchor 2.	2 / 5
Workflow Clarity	A sequenced process exists ('Response Approach' steps 1–8 and Instructions 1–4), but validation checkpoints are only vaguely implied ('Validate signal quality and reduce alert noise'); the missing explicit validation for alerting/incident workflows caps this at 3.	3 / 5
Progressive Disclosure	Section headers provide some structure, but all capability catalogs, knowledge base, and examples are inlined into SKILL.md with no bundle files or one-level-deep references, so content that belongs in separate files is not split out.	3 / 5
	Total	10 / 20 Passed

Description

66%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 clearly states capabilities and occupies a distinct niche, but it lacks an explicit 'when to use' trigger clause, capping completeness. It is solid but not exemplary.

Suggestions

Add an explicit 'Use when ...' clause naming the natural trigger phrases users would say (e.g., 'Use when designing monitoring/tracing systems, defining SLIs/SLOs, or investigating production reliability regressions').

Include a few more synonyms and concrete nouns users actually say (e.g., 'dashboards', 'alerts', 'metrics') to lift trigger-term coverage toward comprehensive.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — 'Build production-ready monitoring, logging, and tracing systems' and 'SLI/SLO management, and incident response workflows' — with only minor coverage gaps, matching the 'several specific actions' anchor rather than the fully comprehensive anchor 5.	4 / 5
Completeness	The 'what' is clear ('Build production-ready monitoring, logging, and tracing systems'), but there is no explicit 'Use when...' clause or equivalent trigger guidance, which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Natural terms like 'monitoring', 'logging', 'tracing', 'SLI/SLO', and 'incident response' appear, giving good keyword coverage; it stops short of anchor 5 because synonyms and common phrasings users actually say are not exhaustively covered.	4 / 5
Distinctiveness Conflict Risk	The observability/SLO/incident-response niche is mostly distinct with only minor overlap risk against general SRE or monitoring skills; it does not reach the minimal-conflict clarity of anchor 5.	4 / 5
	Total	15 / 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-engineer/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

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