Name: eval
Rating: 64 (1 reviews)
Author: alirezarezvani

eval

Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.

Quality

76%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./.gemini/skills/eval/SKILL.md

The canonical home for this skill is eval in alirezarezvani/claude-skills

Quality

Content

68%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.

A compact, action-oriented skill body with concrete commands and a clear mode-based structure. Its main weakness is workflow clarity: a batch evaluation across multiple agents lacks explicit validation/verification checkpoints and error-recovery feedback loops.

Suggestions

Add a validation step after running the eval command — e.g. check the ranker exit code and handle failures before declaring a winner — to introduce a feedback loop and lift workflow_clarity above the batch-operation cap of 3.

Confirm the referenced scripts exist as bundle files (scripts/result_ranker.py, scripts/session_manager.py) or note that they are provided by the AgentHub runtime, so the in-body references are not dangling.

Resolve or document the template placeholders ({session-id}, {eval_cmd}, {metric}, {direction}) so the example commands read as copy-paste ready rather than schematic.

Dimension	Reasoning	Score
Conciseness	The body is efficient and assumes Claude's competence, using compact command blocks and example output tables rather than explanatory prose; a few illustrative output blocks could be trimmed, so it falls just short of a 5.	4 / 5
Actionability	Provides concrete, mostly executable commands ('python {skill_path}/scripts/result_ranker.py ...', 'git diff {base_branch}...{agent_branch}') and explicit ranking criteria; minor gaps come from unresolved template placeholders like {session-id} and {eval_cmd}.	4 / 5
Workflow Clarity	The three modes and 'After Eval' steps are sequenced, but this is a batch operation over multiple agents with no validation that the eval command succeeded and no error-recovery loop; per the rubric cap, a destructive/batch workflow without validation cannot exceed 3.	3 / 5
Progressive Disclosure	Content is organized into clearly labeled sections (Usage, What It Does with sub-modes, After Eval) in a single well-structured file with no nested references; at >50 lines it does not qualify for the simple-skill 5, and the referenced script paths are not present as bundle files, so it stays at 4.	4 / 5
	Total	15 / 20 Passed

Description

83%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.

A well-crafted description that clearly states both capability and trigger conditions in third-person imperative voice, with concrete actions and natural trigger terms. It is held back from perfection only by slightly less-than-exhaustive action and keyword enumeration.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — 'Evaluate and rank agent results by metric or LLM judge' plus 'score, compare, or pick a winner' — with only minor gaps in coverage; not a full 5 because it stops short of enumerating the ranking modes themselves.	4 / 5
Completeness	Explicitly answers both what ('Evaluate and rank agent results by metric or LLM judge for an AgentHub session') and when ('Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents') with concrete trigger phrases.	5 / 5
Trigger Term Quality	'asks to score, compare, or pick a winner among completed AgentHub agents' captures natural user phrasings well; a few synonyms (e.g. 'grade', 'review results') are missing, keeping it below 5.	4 / 5
Distinctiveness Conflict Risk	The 'for an AgentHub session' and '/hub:eval' framing carves a clear niche with distinct triggers; minor overlap risk with generic evaluation skills keeps it just below 5.	4 / 5
	Total	17 / 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: alirezarezvani/claude-skills
Path: .gemini/skills/eval/SKILL.md
Commit: 19392f7

Reviewed: ~6 hours ago

Table of Contents

Discovery Implementation Validation

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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.