hive-mind-advanced

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

0.99x

Quality

28%

Does it follow best practices?

Impact

99%

0.99x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./.claude/skills/hive-mind-advanced/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 relies heavily on abstract buzzwords and technical jargon without explaining concrete capabilities or when to use the skill. It fails to provide actionable information for skill selection, reading more like a marketing tagline than a functional description. Users would have no clear understanding of what tasks this skill actually performs.

Suggestions

Replace abstract terms with concrete actions (e.g., 'Coordinates multiple AI agents to solve complex tasks, distributes subtasks, aggregates results')

Add a 'Use when...' clause with natural trigger terms users would say (e.g., 'Use when the user needs to parallelize work, coordinate multiple agents, or solve problems requiring distributed reasoning')

Remove or explain jargon like 'queen-led' and 'Hive Mind' - describe the actual functionality in plain language

Dimension	Reasoning	Score
Specificity	Uses abstract buzzwords like 'collective intelligence system', 'queen-led multi-agent coordination', 'consensus mechanisms' without describing concrete actions. No specific verbs or operations are listed.	1 / 3
Completeness	Only vaguely addresses 'what' with abstract concepts, completely missing 'when' guidance. No 'Use when...' clause or explicit trigger conditions.	1 / 3
Trigger Term Quality	Contains technical jargon ('Hive Mind', 'queen-led', 'consensus mechanisms') that users would not naturally say. No common user-facing keywords or natural language triggers.	1 / 3
Distinctiveness Conflict Risk	The specific terminology ('Hive Mind', 'queen-led') is unusual enough to avoid conflicts with common skills, but the vague nature could still cause confusion with other multi-agent or coordination tools.	2 / 3
	Total	5 / 12 Passed

Implementation

50%

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

The skill provides excellent actionable content with executable CLI commands and code examples, but is severely bloated with unnecessary explanations and content that should be split into separate reference files. The workflow guidance lacks explicit validation checkpoints for complex multi-step operations, and the document tries to be a comprehensive reference rather than a focused skill guide.

Suggestions

Reduce content by 60-70% by removing explanations of concepts Claude knows (LRU cache, WAL mode, consensus algorithm definitions) and keeping only the actionable commands and code

Move API Reference, Configuration, and Troubleshooting sections to separate linked files (API.md, CONFIG.md, TROUBLESHOOTING.md)

Add explicit validation steps to workflows, e.g., 'After spawning, verify with `npx claude-flow hive-mind status` before proceeding'

Consolidate the 'Best Practices' and 'Examples' sections into the main workflow sections rather than repeating similar content

Dimension	Reasoning	Score
Conciseness	Extremely verbose at 600+ lines with extensive explanations Claude doesn't need (e.g., explaining what LRU cache is, what WAL mode does, basic concepts like 'simple democratic voting'). Contains redundant sections and could be reduced by 70%+ while preserving actionable content.	1 / 3
Actionability	Provides fully executable CLI commands and JavaScript code examples throughout. Commands are copy-paste ready with clear flags and options, and code snippets are complete and runnable.	3 / 3
Workflow Clarity	Steps are listed for initialization and spawning, but lacks explicit validation checkpoints. No feedback loops for error recovery in multi-step processes. Troubleshooting section exists but isn't integrated into workflows as validation steps.	2 / 3
Progressive Disclosure	Has section structure but includes massive inline content that should be in separate files (full API reference, extensive configuration examples, complete troubleshooting guide). References to related skills exist but the main file is monolithic.	2 / 3
	Total	8 / 12 Passed

Validation

81%

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 — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (730 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	9 / 11 Passed

Repository: ruvnet/ruvector
Commit: 462536e

Reviewed: about 16 hours ago

Table of Contents

Discovery Implementation Validation

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