Name: multi-ai-research
Rating: 56.00000000000001 (1 reviews)
Author: fernandezbaptiste

multi-ai-research

Comprehensive research and analysis using Claude (subagents), Gemini CLI, and Codex CLI. Multi-perspective research with cross-verification, iterative refinement, and 100% citation coverage. Use for security analysis, architecture research, code quality assessment, performance analysis, or any research requiring rigorous verification and multiple AI perspectives.

Quality

64%

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./.claude/skills/multi-ai-research/SKILL.md

The canonical home for this skill is multi-ai-research in fernandezbaptiste/Skrillz

Quality

Content

45%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 lays out a clear, well-sequenced multi-phase workflow but is badly overlong and redundant, with marketing claims and placeholder-driven implementation undercutting actionability. Progressive disclosure is essentially absent given the single monolithic file.

Suggestions

Collapse the three repeated analysis-category sections (When to Use, Analysis Types, Examples) into one, and remove unverifiable marketing claims like per-example 'Quality: 97/100' and the 'Quality Guarantees ✓' list.

Split detailed material into references/ files (e.g. analysis-types.md, quality-rubric.md, examples.md) and have SKILL.md point to them one level deep, instead of inlining everything.

Replace the placeholder Claude orchestration in scripts/analyze.sh (the 'In actual implementation, this would use the Task tool' block) with a real, executable invocation, or clearly document that the script is a scaffold to be driven by Claude Code.

Dimension	Reasoning	Score
Conciseness	The body is ~730 lines with heavy redundancy: analysis categories appear three times (When to Use, Analysis Types, Examples), marketing-style 'Quality Guarantees ✓' bullets and fabricated per-example quality scores (97/100, 98/100) pad the content, and phases are restated repeatedly.	2 / 5
Actionability	Install/test commands for Gemini and Codex CLIs and the analyze.sh invocation are concrete and executable, but the central orchestration is explicitly a placeholder ('In actual implementation, this would use the Task tool') and the bundled script simulates Claude research rather than implementing it.	3 / 5
Workflow Clarity	The 5/6-phase pipeline is clearly sequenced with per-phase outputs, durations, and an explicit verification-then-iterate loop, which provides good checkpoints; minor gaps come from the placeholder-driven implementation not matching the documented flow.	4 / 5
Progressive Disclosure	The skill is a monolithic ~730-line SKILL.md with no references/ files; material that belongs in separate references (full analysis-type specs, the quality rubric, worked examples) is all inlined, and the only external pointer is to the bundled scripts/analyze.sh.	2 / 5
	Total	11 / 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.

The description is well-constructed: third person, concrete systems and actions, explicit 'Use for' trigger guidance, and a clear niche. It is strong on completeness with only minor gaps in trigger-term breadth and distinctiveness at the edges.

Dimension	Reasoning	Score
Specificity	Names concrete systems (Claude subagents, Gemini CLI, Codex CLI) and several concrete actions (research, analysis, cross-verification, iterative refinement, 100% citation coverage), but the actions are somewhat high-level rather than fully enumerated.	4 / 5
Completeness	Clearly states what it does ('Comprehensive research and analysis... Multi-perspective research with cross-verification, iterative refinement, and 100% citation coverage') and explicitly answers when via 'Use for security analysis, architecture research... or any research requiring rigorous verification'.	5 / 5
Trigger Term Quality	Includes natural phrases users would say ('security analysis', 'architecture research', 'code quality assessment', 'performance analysis', 'research'), though it lacks synonyms and file-extension variants that would push it to 5.	4 / 5
Distinctiveness Conflict Risk	The multi-AI / cross-verification niche is fairly distinct from typical single-purpose skills, but the catch-all 'any research requiring rigorous verification and multiple AI perspectives' leaves some overlap with general analysis skills.	4 / 5
	Total	17 / 20 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 (736 lines); consider splitting into references/ and linking	Warning
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning

	Total	14 / 16 Passed

Repository: fernandezbaptiste/Skrillz
Path: .claude/skills/multi-ai-research/SKILL.md
Commit: 93ed392

Reviewed: 1 day ago

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