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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.

70

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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

SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

The body is concise and highly actionable, with concrete commands, clear mode selection, and a well-defined post-eval workflow. The main gaps are unspecified configuration sources for the eval command placeholders and no guidance for failure cases such as empty or failed evaluations.

Suggestions

Specify where the "configured criteria" ({eval_cmd}, {metric}, {direction}) come from — e.g., name the config file and its location — so metric mode can be run without guessing.

Add a brief failure path: what to do when a session has no results yet or the eval command fails, so the workflow includes a validation checkpoint.

Clarify how to determine {base_branch} and {agent_branch} in LLM judge mode, since the git diff command is currently the only place they appear and no source for these values is given.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence: no concept explanations, no padding, and every section (usage, three eval modes with example output, after-eval state update and next steps) earns its place. The example output blocks are illustrative rather than explanatory filler.

5 / 5

Actionability

Mostly executable guidance: concrete commands ("python {skill_path}/scripts/result_ranker.py --session {session-id} ...", "git diff {base_branch}...{agent_branch}"), specific file paths (".agenthub/board/results/agent-{i}-result.md"), and copy-paste usage examples. Minor gaps: "{eval_cmd}"/"{metric}"/"{direction}" are said to come from "configured criteria" without saying where that configuration is found or what its defaults are.

4 / 5

Workflow Clarity

The sequence is clear: mode selection (metric / LLM judge / --judge flag), evaluation, an explicit hybrid tie-break checkpoint ("If top agents are within 10% of each other"), then state update and user-facing next steps. It lacks validation for failure cases (no results, eval command fails), keeping it below the explicit-validation 5 anchor.

4 / 5

Progressive Disclosure

A short (~80 line), single-purpose skill with well-organized sections and no content that belongs in separate files; the only external references are one-level-deep script invocations via {skill_path}. Per the simple-skill note, this matches the well-organized 5 anchor.

5 / 5

Total

18

/

20

Passed

Description

87%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 strong description: it states concrete capabilities, an explicit use-when clause with natural trigger phrasing, and tight scoping to AgentHub sessions. The only weakness is slightly incomplete capability coverage (hybrid mode and follow-on actions are absent).

DimensionReasoningScore

Specificity

"Evaluate and rank agent results by metric or LLM judge for an AgentHub session" names the domain and several concrete actions with an explicit mode distinction. It falls short of a 5 because coverage has minor gaps (hybrid mode and the merge handoff are not mentioned).

4 / 5

Completeness

It 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, matching the 5 anchor exactly.

5 / 5

Trigger Term Quality

"asks to score, compare, or pick a winner" plus "/hub:eval" are natural user phrasings with good coverage. A few natural variants (e.g., "rank agents", "benchmark") are missing, keeping it below the comprehensive 5 anchor.

4 / 5

Distinctiveness Conflict Risk

The description is scoped to AgentHub sessions and a specific command (/hub:eval), giving it a clear niche with minimal risk of triggering for unrelated evaluation or ranking skills.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

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

Reviewed

Table of Contents

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