performance-engineer

Expert performance engineer specializing in modern observability,

Quality

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/performance-engineer/SKILL.md

Quality

Content

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

This skill reads as a persona description or resume rather than actionable instructions. It is overwhelmingly verbose, listing hundreds of technologies and concepts Claude already knows, while providing zero concrete code examples, commands, or executable guidance. The workflow is abstract and lacks the validation checkpoints needed for performance engineering tasks that can impact production systems.

Suggestions

Replace the extensive capability listings with a concise summary and provide concrete, executable examples (e.g., a k6 load test script, a Prometheus query for latency percentiles, an OpenTelemetry tracing setup snippet).

Add explicit validation checkpoints to the workflow, such as 'Run baseline load test and record p50/p95/p99 latencies before making changes' and 'Compare post-optimization metrics against baseline before declaring success'.

Remove the 'Behavioral Traits', 'Knowledge Base', 'Purpose', and 'Example Interactions' sections—these describe what Claude already knows or are persona-style content that wastes tokens without adding actionable value.

If detailed tool-specific guidance is needed, create separate reference files (e.g., LOAD_TESTING.md, OBSERVABILITY.md) and link to them from a concise SKILL.md overview.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose, listing exhaustive catalogs of tools, platforms, and concepts that Claude already knows. The 'Capabilities' section alone is a massive enumeration of technologies with no actionable depth—just bullet-point lists of things Claude is already familiar with. The 'Behavioral Traits', 'Knowledge Base', 'Example Interactions', and 'Purpose' sections largely repeat information already conveyed or that Claude inherently understands.	1 / 3
Actionability	There is no concrete, executable code, no specific commands, no example inputs/outputs, and no copy-paste-ready guidance. The entire skill is abstract descriptions and tool name-dropping. The 'Instructions' section has four vague steps like 'Collect traces, profiles, and load tests to isolate bottlenecks' with no specifics on how to do any of it.	1 / 3
Workflow Clarity	The four-step 'Instructions' workflow is extremely high-level with no validation checkpoints, no feedback loops, no error recovery steps, and no concrete sequencing. The 'Response Approach' section lists 9 steps but they are equally abstract. For a skill involving potentially destructive operations like load testing production, the lack of concrete validation steps is a significant gap.	1 / 3
Progressive Disclosure	The content is a monolithic wall of text with no references to external files, no layered structure, and no navigation aids. Hundreds of lines of capability listings are inlined that could be split into focused reference documents. There are no bundle files to support progressive disclosure.	1 / 3
	Total	4 / 12 Passed

Description

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

This description is critically underdeveloped — it reads as a fragment of a role title rather than a functional skill description. It lacks concrete actions, natural trigger terms, explicit 'when to use' guidance, and any distinguishing characteristics that would help Claude select it appropriately from a pool of skills.

Suggestions

List specific concrete actions the skill performs, e.g., 'Analyzes application traces, configures monitoring dashboards, diagnoses latency bottlenecks, sets up alerting rules.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about observability, monitoring, APM, tracing, metrics, logs, latency issues, or performance profiling.'

Remove the role-based framing ('Expert performance engineer') and use third-person action verbs to describe capabilities directly, which also avoids the first/second person penalty.

Dimension	Reasoning	Score
Specificity	The description uses vague, abstract language ('expert performance engineer') without listing any concrete actions. It describes a role rather than specific capabilities like 'analyze traces', 'set up dashboards', or 'configure alerts'.	1 / 3
Completeness	The description barely addresses 'what' (vague reference to performance engineering and observability) and completely omits 'when' — there is no 'Use when...' clause or any explicit trigger guidance.	1 / 3
Trigger Term Quality	The only potentially relevant keywords are 'performance' and 'observability', which are broad technical jargon. It lacks natural user terms like 'monitoring', 'metrics', 'tracing', 'logs', 'APM', 'latency', or 'dashboards'.	1 / 3
Distinctiveness Conflict Risk	The description is extremely generic and could overlap with any skill related to performance, monitoring, DevOps, or infrastructure. There are no distinct triggers to differentiate it from similar skills.	1 / 3
	Total	4 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: sickn33/antigravity-awesome-skills
Commit: 9431b67

Reviewed: 3 days ago

Table of Contents

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