agent-performance-benchmarker

Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker

2.89x

Quality

Does it follow best practices?

Impact

81%

2.89x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.agents/skills/agent-performance-benchmarker/SKILL.md

Quality

Content

0%Scale 1-3

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

This skill is an extremely verbose, non-executable pseudocode dump that provides no actionable guidance for performing performance benchmarking. It explains concepts Claude already knows through hundreds of lines of fictional class implementations that reference non-existent dependencies. The content would need to be completely rewritten to be useful as a skill.

Suggestions

Replace the fictional class implementations with actual executable code or concrete CLI commands that can be run to benchmark consensus protocols, using real libraries and tools.

Add a clear workflow section with numbered steps, validation checkpoints, and error recovery guidance (e.g., '1. Configure benchmark parameters → 2. Run benchmark → 3. Validate results → 4. Generate report').

Reduce content to under 100 lines by removing all explanatory pseudocode and focusing on the specific commands, configurations, and decision points that Claude needs to know.

If detailed implementations are needed, split them into separate referenced files (e.g., THROUGHPUT.md, LATENCY.md, RESOURCES.md) and keep SKILL.md as a concise overview with clear navigation.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~600+ lines of non-executable pseudocode. The code references fictional classes (SystemMonitor, PerformanceModel, TimeSeriesDatabase, etc.) that don't exist, and explains concepts Claude already understands like calculating percentiles, standard deviations, and basic monitoring patterns. Most of this content is padding that provides no actionable value.	1 / 3
Actionability	Despite the massive amount of code, none of it is executable. Every class depends on undefined imports and fictional APIs (SystemMonitor, PerformanceModel, LoadGenerator, LatencyHistogram, etc.). The MCP integration hooks reference non-existent tools like 'neural_patterns' and 'neural_predict'. This is elaborate pseudocode dressed up as real implementation.	1 / 3
Workflow Clarity	There is no clear workflow or sequence of steps for actually performing benchmarking. The skill lists 'Core Responsibilities' abstractly and then dumps class implementations without explaining when or how to use them. No validation checkpoints, no error recovery guidance, and no clear entry point for the agent to follow.	1 / 3
Progressive Disclosure	Monolithic wall of code with no references to external files and no meaningful structure beyond class-level organization. Everything is inlined in a single massive document with no navigation aids, no summary/overview section that points to details, and no bundle files to support progressive disclosure.	1 / 3
	Total	4 / 12 Passed

Description

0%Scale 1-3

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 is an extremely weak description that essentially only provides an invocation command and a tool name. It fails across all dimensions: no concrete actions, no natural trigger terms, no 'when to use' guidance, and no distinguishing characteristics. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.

Suggestions

Add specific concrete actions the skill performs, e.g., 'Runs performance benchmarks, measures response latency, compares throughput across configurations, and generates benchmark reports.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about performance testing, benchmarking, load testing, measuring speed, or comparing performance metrics.'

Specify the domain or technology scope to improve distinctiveness, e.g., 'Benchmarks API endpoints, database queries, or application response times using standardized test suites.'

Dimension	Reasoning	Score
Specificity	The description provides no concrete actions whatsoever. It only says 'Agent skill for performance-benchmarker' which is entirely vague — there's no indication of what specific tasks it performs (e.g., run benchmarks, measure latency, compare results).	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no 'Use when...' clause and no meaningful explanation of capabilities — it merely states it's an agent skill and how to invoke it.	1 / 3
Trigger Term Quality	The only potentially relevant term is 'performance-benchmarker', which is a tool name rather than a natural keyword a user would say. Users might say 'benchmark', 'performance test', 'load test', 'measure speed', etc., none of which are present.	1 / 3
Distinctiveness Conflict Risk	The description is so generic that it could overlap with any performance-related or benchmarking skill. Without specific actions, file types, or domains, there's nothing to distinguish it from other potential 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
skill_md_line_count	SKILL.md is long (856 lines); consider splitting into references/ and linking	Warning

	Total	10 / 11 Passed

Repository: ruvnet/claude-flow
Path: .agents/skills/agent-performance-benchmarker/SKILL.md
Commit: 913f9ea

Reviewed: 3 days ago

Table of Contents

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