Name: agent-resource-allocator
Rating: 39.2 (1 reviews)
Author: ruvnet

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agent-resource-allocator

Agent skill for resource-allocator - invoke with $agent-resource-allocator

5.00x

Quality

12%

Does it follow best practices?

Impact

80%

5.00x

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-resource-allocator/SKILL.md

The canonical home for this skill is agent-resource-allocator in ruvnet/claude-flow

Quality

Content

7%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 monolithic dump of verbose, non-executable JavaScript class skeletons that over-explain concepts Claude already knows and provide no clear workflow or progressive disclosure. It offers little actionable guidance and no validation steps for risky resource operations.

Suggestions

Replace the class skeletons with a short overview plus a few executable commands/examples; move detailed API material into separate reference files linked one level deep.

Add a numbered operational workflow (analyze → predict → allocate → validate → scale) with explicit validation checkpoints and rollback guidance for the destructive scaling operations.

Cut generic explanations of ML/circuit-breaker/profiling concepts that Claude already knows, keeping only swarm-specific configuration values and constraints.

Dimension	Reasoning	Score
Conciseness	The ~670-line body is almost entirely long JavaScript class skeletons (e.g. AdaptiveResourceAllocator, PredictiveScaler, PerformanceProfiler) that restate general programming and ML concepts Claude already knows, with heavy padding and no token discipline.	1 / 5
Actionability	It provides high-level method skeletons and references undefined helpers (CPUAllocator, MultiObjectiveGeneticSolver, mcp.neural_train) plus shell stubs, but no executable, copy-paste-ready code that would actually run end-to-end.	2 / 5
Workflow Clarity	There is no sequenced operational workflow, and for destructive/batch operations like resource allocation and scaling there are no validation checkpoints or feedback loops — steps are absent or incoherent.	1 / 5
Progressive Disclosure	The body is a monolithic wall of inline code with no references or supporting files (no references/, scripts/, or assets/ exist), and no navigation structure splitting the detailed material into one-level-deep references.	1 / 5
	Total	5 / 20 Passed

Description

17%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 generic, template-generated label that names the skill but fails to describe concrete capabilities or specify when it should be invoked. It lacks trigger terms a user would naturally say and has high overlap risk with other optimization skills.

Suggestions

Replace the templated phrase with concrete actions, e.g. 'Allocates CPU, memory, storage and network resources across agent swarms, predicts scaling needs, and profiles performance bottlenecks.'

Add an explicit trigger clause such as 'Use when the user asks to scale agent swarms, rebalance resources, or troubleshoot performance bottlenecks.'

Include natural user terms and synonyms (e.g. 'scaling', 'capacity planning', 'bottleneck analysis') rather than only the internal '$agent-resource-allocator' invocation token.

Dimension	Reasoning	Score
Specificity	The description only names the domain ('resource-allocator') via a templated invocation phrase and lists no concrete actions — it states 'Agent skill for resource-allocator - invoke with $agent-resource-allocator', which is closer to a launcher label than a capability statement.	2 / 5
Completeness	It conveys only a vague 'what' (an agent skill for resource allocation) and provides no 'Use when...' trigger clause, so the 'when' guidance is entirely missing.	2 / 5
Trigger Term Quality	There are no natural user-facing keywords; 'resource-allocator' and the '$agent-resource-allocator' invocation syntax are technical/internal tokens a user would not naturally say, and no synonyms or scenario terms are present.	1 / 5
Distinctiveness Conflict Risk	'Resource allocation' is a very broad concept that could overlap with many optimization or scaling skills, and nothing in the description distinguishes it from neighboring performance agents.	2 / 5
	Total	7 / 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 (679 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: ruvnet/ruflo
Path: .agents/skills/agent-resource-allocator/SKILL.md
Commit: fa13ee4

Reviewed: about 8 hours ago

Table of Contents

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