agent-evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

65%

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

The content is highly actionable with rich executable code, but it suffers from monolithic verbosity and lacks explicit validation checkpoints and file-based progressive disclosure for its large reference material.

Suggestions

Move the long TypeScript class implementations into separate reference files (e.g., references/statistical-evaluator.ts) and keep SKILL.md a concise overview with one-level-deep links.

Add explicit validation/feedback-loop checkpoints (validate -> fix -> retry) to the risky-operation workflows such as adversarial testing and production-readiness evaluation.

Tighten or trim the code blocks to the essential executable fragments rather than full class bodies to improve token efficiency.

Dimension	Reasoning	Score
Conciseness	The prose is lean and assumes competence, but the body is a ~1100-line monolith padded with long full-class TypeScript implementations that could be tightened significantly.	2 / 3
Actionability	It provides extensive, executable TypeScript code with complete interfaces, classes, and detector functions across every pattern, giving copy-paste-ready concrete guidance.	3 / 3
Workflow Clarity	Numbered workflows and 'When to use' labels exist, but risky operations (adversarial testing, production readiness) lack explicit validation checkpoints and feedback loops.	2 / 3
Progressive Disclosure	Sections are reasonably organized, but no bundle files exist and the entire content is inlined in one monolithic SKILL.md rather than split into one-level-deep references.	2 / 3
	Total	9 / 12 Passed

Description

67%

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 is specific and distinctive, clearly conveying the domain and concrete actions, but omits an explicit trigger/usage clause, which caps completeness and slightly weakens trigger-term naturalness.

Suggestions

Add an explicit 'Use when...' clause naming natural user phrases (e.g., 'Use when the user mentions agent testing, benchmarking agents, or agent reliability').

Soften technical phrasing like 'capability assessment' and 'reliability metrics' with more common trigger terms users would actually say.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete actions — 'behavioral testing, capability assessment, reliability metrics, and production monitoring' — rather than vaguely naming a domain.	3 / 3
Completeness	It clearly states what the skill does but lacks an explicit 'Use when...' trigger clause, so the 'when' is only implied; per guidelines this caps completeness at 2.	2 / 3
Trigger Term Quality	It includes relevant keywords like 'testing', 'benchmarking', and 'reliability', but phrasing leans technical ('capability assessment', 'reliability metrics') and misses some natural variations a user would say.	2 / 3
Distinctiveness Conflict Risk	It targets a clear niche (LLM agent evaluation/benchmarking) with distinct triggers, making it unlikely to fire for unrelated skills.	3 / 3
	Total	10 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1136 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: boisenoise/skills-collections
Commit: f36337d

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.