Name: agent-topology-optimizer
Rating: 52.8 (1 reviews)
Author: ruvnet

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agent-topology-optimizer

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

1.58x

Quality

30%

Does it follow best practices?

Impact

92%

1.58x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.agents/skills/agent-topology-optimizer/SKILL.md

The canonical home for this skill is agent-topology-optimizer in ruvnet/claude-flow

Quality

Content

32%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 content is a verbose, monolithic catalog of aspirational JavaScript classes that mostly cannot run as written, with no sequenced workflow or validation for its destructive topology-migration operations. It lacks any progressive disclosure structure, inlining material that belongs in referenced files.

Suggestions

Collapse the body to a concise overview and move the algorithm implementations, MCP integration code, and metrics into separate files under references/, linking to them one level deep.

Provide executable, self-contained examples (real package/CLI calls with expected output) instead of classes that instantiate undefined dependencies.

Add an explicit sequenced workflow with validation checkpoints for destructive operations (e.g. analyze -> propose -> validate migration plan -> apply -> verify), including error-recovery feedback loops.

Dimension	Reasoning	Score
Conciseness	The ~810-line body is padded with elaborate speculative scaffolding (AI-generated topologies, neural integration, multiple optimizer classes) that far exceeds what a skill overview needs.	2 / 5
Actionability	Code is structurally detailed but references undefined classes (HierarchicalTopology, NetworkOptimizer, GeneticAlgorithm, mcp.*) so it functions as pseudocode; the bash commands give some executable surface but key dependencies are missing.	3 / 5
Workflow Clarity	The body is a catalog of capability classes, not a sequenced process; risky operations like topology migration and swarm scaling have no validation checkpoints or error-recovery feedback loops.	2 / 5
Progressive Disclosure	No bundle files exist and all algorithm implementations, MCP integrations, and metrics are inlined into one monolithic SKILL.md with no references to separate files, despite section headers providing minimal structure.	2 / 5
	Total	9 / 20 Passed

Description

28%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 a templated, meta-level label that names the domain without stating concrete capabilities or any usage trigger. It tells Claude how to invoke the skill but not what it does or when to use it.

Suggestions

Replace the generic 'Agent skill for topology-optimizer' framing with the concrete actions the skill performs, e.g. 'Reconfigures swarm topology, optimizes communication latency, and places agents across nodes'.

Add an explicit 'Use when...' trigger clause listing natural user phrases such as 'optimize swarm topology', 'reduce agent communication latency', or 'rebalance agent placement'.

Drop the '$agent-topology-optimizer' invocation token from the description; keep natural-language triggers instead of CLI/identifier jargon.

Dimension	Reasoning	Score
Specificity	The phrase 'Agent skill for topology-optimizer' names the domain but states no concrete actions; it describes the skill's existence rather than what it does.	2 / 5
Completeness	It offers only a vague 'what' (an 'agent skill for topology-optimizer') and no 'when'/Use-when clause at all, matching the vague-what-and-no-when anchor.	2 / 5
Trigger Term Quality	'topology-optimizer' and the invocation token '$agent-topology-optimizer' are the skill's own identifiers/CLI jargon, not natural phrases a user would say when they need the skill.	2 / 5
Distinctiveness Conflict Risk	'topology-optimizer' points at a fairly specific niche, but the templated 'Agent skill for X' framing gives no distinct triggers, leaving overlap risk with related optimization skills.	3 / 5
	Total	9 / 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
skill_md_line_count	SKILL.md is long (813 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: ruvnet/ruflo
Path: .agents/skills/agent-topology-optimizer/SKILL.md
Commit: fa13ee4

Reviewed: about 8 hours ago

Table of Contents

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