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consult

Fans out a question across all ready AI provider CLIs in parallel and synthesizes a single recommendation. Use when comparing AI answers, asking multiple models (Claude, Codex, Antigravity / agy, Copilot, Qwen, OpenCode), seeking multi-model consensus, or aggregating opinions on read-only questions.

74

Quality

93%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

96%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.

An exemplary lean, fully actionable skill body: copy-paste-ready commands, an explicit validation and fallback stage, and a strict output template. The sole defect is the reference link, whose ../../ path escapes the skill bundle and does not resolve to an existing file.

Suggestions

Fix the reference link to point inside the skill bundle (e.g., [references/CLI_REFERENCE.md](references/CLI_REFERENCE.md)) instead of ../../references/CLI_REFERENCE.md, and ensure the references/ directory with that file actually ships with the skill.

Make the consult command's question argument an obvious placeholder (e.g., "<question>") or add a one-line note that the sample Rust question is a stand-in, so the command cannot be copy-pasted with the example question verbatim.

DimensionReasoningScore

Conciseness

The ~40-line body is lean throughout: steps are one-line directives ("Verify provider availability using `quintet doctor` if pool status is unknown") with no explanation of concepts Claude already knows and no padding. Every token earns its place, matching the lean-and-efficient anchor.

5 / 5

Actionability

The skill provides a fully executable copy-paste bash one-liner including binary-path fallback resolution, a concrete readiness command (`quintet doctor`), and a strict verbatim output template. This matches the fully-executable anchor covering the common case; the embedded sample question ("Best approach to dedupe a 10M-row stream in Rust?") serves as a concrete example.

5 / 5

Workflow Clarity

The four-step workflow has an explicit validation/fallback stage ("Verify responses were collected from ready models", "If a provider times out or errors, log the failure and synthesize from available responses", "If zero providers respond, prompt user to check CLI auth") plus a strict deliverable format. Clear sequence with explicit validation and error-recovery feedback loops matches the top anchor; the operation is read-only so no destructive-operation cap applies.

5 / 5

Progressive Disclosure

Structure is good: a concise overview, a Reference Materials section with a single clearly-signaled one-level reference for the bulk CLI detail. It falls short of 5 because the link path "../../references/CLI_REFERENCE.md" points two directory levels up — outside the skill bundle where a relative "references/CLI_REFERENCE.md" would be expected — and no references/ bundle directory is present alongside the skill, so navigation to the referenced file is broken.

4 / 5

Total

19

/

20

Passed

Description

90%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: third-person, concise, with an explicit and richly synonymed "Use when" trigger clause naming the specific provider CLIs. The only gap is that it lists just two capabilities, leaving supporting behaviors (readiness check, fallback synthesis) to the body.

Suggestions

Consider adding one or two supporting capabilities to the description (e.g., readiness checking via `quintet doctor`, or graceful synthesis when a provider fails) so capability coverage is comprehensive, not just the core fan-out and synthesis actions.

DimensionReasoningScore

Specificity

"Fans out a question across all ready AI provider CLIs in parallel and synthesizes a single recommendation" names the domain plus exactly two concrete actions (parallel fan-out, synthesis), matching the anchor for 1-2 concrete actions without comprehensive coverage. It is not a 4 because supporting capabilities like readiness checking and per-provider fallback are absent from the description.

3 / 5

Completeness

Both questions are explicitly answered: the what ("Fans out a question across all ready AI provider CLIs in parallel and synthesizes a single recommendation") and the when ("Use when comparing AI answers, asking multiple models...") with concrete trigger phrases. This is a direct match for the top anchor; not below it since neither half is vague or implied.

5 / 5

Trigger Term Quality

The "Use when" clause covers natural phrases and synonyms comprehensively: "comparing AI answers", "asking multiple models", "seeking multi-model consensus", "aggregating opinions", plus the concrete tool names users would actually say (Claude, Codex, Antigravity / agy, Copilot, Qwen, OpenCode). This matches the comprehensive-synonyms anchor; no common variation is obviously missing.

5 / 5

Distinctiveness Conflict Risk

The multi-model consultation niche is distinct ("fans out a question across AI provider CLIs", "multi-model consensus") and its triggers name specific CLIs, so it would not naturally fire for a generic single-model or orchestration skill. Minimal conflict risk matches the clear-niche anchor.

5 / 5

Total

18

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

relative_links

Relative link issues: 1 suspicious

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

14

/

16

Passed

Repository
salemaziel/omc-octo-quintet
Reviewed

Table of Contents

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