agent-matrix-optimizer

Agent skill for matrix-optimizer - invoke with $agent-matrix-optimizer

1.40x

Quality

13%

Does it follow best practices?

Impact

87%

1.40x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-matrix-optimizer/SKILL.md

Quality

Discovery

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This description is critically deficient across all dimensions. It functions only as an invocation reference rather than a skill description, providing no information about capabilities, use cases, or trigger conditions. Claude would have no basis for selecting this skill appropriately from a skill library.

Suggestions

Add specific concrete actions describing what matrix optimization this skill performs (e.g., 'Performs matrix multiplication, inversion, eigenvalue decomposition, and linear system solving').

Include a 'Use when...' clause with natural trigger terms users would say (e.g., 'Use when working with matrices, linear algebra, array operations, or numerical optimization').

Remove the invocation syntax from the description and replace with functional content that explains the skill's purpose and domain.

Dimension	Reasoning	Score
Specificity	The description contains no concrete actions whatsoever. 'Agent skill for matrix-optimizer' is completely abstract with no indication of what the skill actually does.	1 / 3
Completeness	Missing both 'what does this do' and 'when should Claude use it'. The description only provides invocation syntax ('invoke with $agent-matrix-optimizer') but no functional information.	1 / 3
Trigger Term Quality	The only term present is 'matrix-optimizer' which is technical jargon. No natural keywords a user would say like 'matrix', 'optimize', 'linear algebra', 'calculations', etc.	1 / 3
Distinctiveness Conflict Risk	Extremely generic phrasing 'Agent skill for X' could apply to any agent skill. Without knowing what matrix optimization means in this context, it could conflict with math, data processing, or optimization skills.	1 / 3
	Total	4 / 12 Passed

Implementation

27%

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

This skill is excessively verbose, explaining concepts Claude already understands while burying actionable content in walls of text. The code examples are partially useful but incomplete, and the document lacks proper structure with no external references despite its length. The skill would benefit significantly from aggressive trimming and restructuring into a concise overview with linked reference materials.

Suggestions

Reduce content by 70%+ by removing explanations of basic concepts (what matrix analysis is, what diagonal dominance means) and keeping only the MCP tool signatures with minimal usage examples

Split into SKILL.md (quick start with 2-3 essential tool calls) plus separate REFERENCE.md (full API details) and EXAMPLES.md (complete workflows)

Make code examples complete and executable - either provide full working Python OR JavaScript, not mixed incomplete snippets with undefined helper functions

Add explicit validation steps to workflows: what to check after analyzeMatrix, how to handle non-diagonally-dominant matrices with specific tool calls

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive explanations Claude already knows (what matrix analysis is, what diagonal dominance means). Contains redundant sections like 'Core Capabilities' that just list concepts, and excessive boilerplate descriptions throughout.	1 / 3
Actionability	Provides JavaScript code examples that appear executable, but they reference undefined functions (create_diagonally_dominant_matrix, analyze_matrix_properties) and mix JavaScript/Python inconsistently. The MCP tool calls are concrete but lack complete context for actual use.	2 / 3
Workflow Clarity	The 'Complete Matrix Optimization Pipeline' lists phases but lacks validation checkpoints and error recovery steps. The numbered best practices are helpful but don't form a clear executable workflow with feedback loops for when matrix analysis fails.	2 / 3
Progressive Disclosure	Monolithic wall of text with no references to external files. All content is inline including advanced features, integration guidelines, and example workflows that could be split into separate reference documents. No clear navigation structure.	1 / 3
	Total	6 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/claude-flow
Commit: b2618f9

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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