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observability-engineer

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

35

Quality

31%

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SecuritybySnyk

Passed

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

10%Scale 1-5

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

This skill reads like a job description or capability matrix rather than actionable instructions for Claude. It is severely bloated with lists of tools and concepts Claude already knows, contains zero executable code or concrete examples, and provides only the vaguest workflow guidance. The content needs a complete rewrite focused on specific, actionable procedures with code examples, validation steps, and proper content organization across multiple files.

Suggestions

Replace the extensive capability bullet lists with concrete, executable examples: e.g., a complete Prometheus recording rule, a sample SLO definition in YAML, or an OpenTelemetry instrumentation snippet.

Add explicit validation checkpoints to the workflow, such as 'Verify SLI measurement accuracy by comparing against raw data' or 'Test alert thresholds against historical incident data before enabling.'

Move detailed tool-specific guidance (Prometheus, Grafana, ELK, etc.) into separate reference files and keep SKILL.md as a concise overview with clear links to each.

Remove or drastically condense sections like 'Capabilities', 'Knowledge Base', 'Behavioral Traits', and 'Example Interactions' — these describe what Claude already knows and waste token budget.

DimensionReasoningScore

Conciseness

Extremely verbose and padded. The vast majority of content is long bullet-point lists of tools, concepts, and capabilities that Claude already knows. Sections like 'Capabilities', 'Knowledge Base', 'Behavioral Traits', and 'Example Interactions' are essentially resume-style enumerations that add no actionable value and consume enormous token budget. The skill is ~300+ lines of content that could be condensed to under 50 lines.

1 / 5

Actionability

There is zero executable code, no concrete commands, no specific configuration examples, no copy-paste ready snippets. The entire skill is abstract descriptions and lists of tools/concepts. The 'Instructions' section has only 4 vague high-level steps like 'Identify critical services' and 'Build dashboards' with no specifics on how to do any of them.

1 / 5

Workflow Clarity

The 'Instructions' section provides a rough 4-step sequence but steps are extremely vague with no validation checkpoints, no error recovery, and no concrete commands. The 'Response Approach' section lists 8 steps but they are equally abstract. For a skill involving production systems and potentially destructive operations (alerting changes, chaos engineering), the complete absence of validation steps is a significant gap.

2 / 5

Progressive Disclosure

The content is a monolithic wall of bullet points with no references to external files, no linked resources, and no structured navigation. Massive sections like the 13 capability subsections should be in separate reference files. There are section headers but they organize a flat dump of information rather than providing meaningful progressive disclosure.

2 / 5

Total

6

/

20

Passed

Description

53%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 identifies a clear domain (observability/monitoring) and lists several relevant sub-areas, but remains at a high categorical level rather than specifying concrete actions. The absence of an explicit 'Use when...' clause significantly weakens its utility for skill selection, and the trigger terms, while relevant, miss common synonyms and tool names users would naturally mention.

Suggestions

Add an explicit 'Use when...' clause with trigger phrases like 'Use when the user asks about setting up monitoring, configuring alerts, creating dashboards, defining SLOs, or building incident response runbooks.'

Include more specific concrete actions such as 'configure alerting rules, create Grafana dashboards, set up distributed tracing, define error budgets' to improve specificity.

Add common synonyms and tool names as trigger terms: 'alerts', 'dashboards', 'metrics', 'Prometheus', 'Grafana', 'OpenTelemetry', 'uptime', 'on-call'.

DimensionReasoningScore

Specificity

Names the domain (observability) and lists a few concrete areas — monitoring, logging, tracing, SLI/SLO management, incident response workflows — but these are still fairly high-level categories rather than specific concrete actions like 'configure Prometheus alerts' or 'set up distributed tracing with OpenTelemetry'.

3 / 5

Completeness

The 'what' is reasonably clear (build monitoring/logging/tracing systems, implement observability strategies, SLI/SLO management, incident response), but there is no explicit 'when' clause or trigger guidance telling Claude when to select this skill. Per rubric guidelines, missing 'Use when...' caps completeness at 3.

3 / 5

Trigger Term Quality

Includes relevant terms like 'monitoring', 'logging', 'tracing', 'SLI/SLO', 'incident response', and 'observability', but misses common natural user phrases and synonyms such as 'alerts', 'dashboards', 'metrics', 'Grafana', 'Prometheus', 'OpenTelemetry', 'on-call', or 'uptime'.

3 / 5

Distinctiveness Conflict Risk

The combination of observability, SLI/SLO management, and incident response workflows creates a fairly distinct niche. There's minor overlap risk with general DevOps or infrastructure skills, but the specific focus on observability and incident response differentiates it reasonably well.

4 / 5

Total

13

/

20

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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