agent-resource-allocator

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

5.00x

Quality

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

Quality

Content

0%Scale 1-3

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

This skill is essentially a design document or architectural fantasy rather than an actionable skill. It presents hundreds of lines of non-executable pseudocode defining imaginary classes and ML models with no real implementations, no clear workflows, and no concrete guidance Claude could follow. The content violates every dimension of the rubric: it's verbose, non-actionable, lacks workflow structure, and has no progressive disclosure.

Suggestions

Replace pseudocode class definitions with actual executable commands or real API calls that Claude can use to perform resource allocation tasks.

Define a clear step-by-step workflow: e.g., 1) Check current usage, 2) Identify bottlenecks, 3) Calculate allocation, 4) Apply changes, 5) Validate results — with specific commands at each step.

Remove all imaginary class definitions (LSTMTimeSeriesModel, DeepQNetworkAgent, etc.) and focus only on tools and commands that actually exist in the environment.

Reduce content to under 100 lines focusing on the essential commands and decision logic, moving any reference material to separate files with clear links.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~500+ lines of pseudocode-style JavaScript that Claude cannot execute. The code defines imaginary classes (LSTMTimeSeriesModel, DeepQNetworkAgent, IsolationForestModel) with no real implementations. Massive amounts of boilerplate that explain concepts Claude already understands (circuit breakers, genetic algorithms, reinforcement learning) without providing any actionable, executable content.	1 / 3
Actionability	None of the code is executable — it references non-existent classes, libraries, and MCP tools (mcp.neural_train, mcp.model_save, etc.) with no indication these actually exist. The bash commands reference 'npx claude-flow' subcommands that may not exist. Everything reads as aspirational architecture documentation rather than concrete, usable instructions.	1 / 3
Workflow Clarity	There is no clear workflow or step-by-step process for performing resource allocation tasks. The content presents class definitions and method signatures but never describes when or how to use them in sequence. No validation checkpoints, no error recovery steps, no decision points are articulated as a workflow.	1 / 3
Progressive Disclosure	The content is a monolithic wall of code with no references to external files, no layered structure, and no clear navigation. All content is dumped inline with no separation between overview and detailed reference material. The 'Integration Points' section lists connections to other agents but provides no links or file references.	1 / 3
	Total	4 / 12 Passed

Description

0%Scale 1-3

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 is an extremely weak description that provides virtually no useful information for skill selection. It reads as an auto-generated stub rather than a crafted description, containing only the skill's internal name and invocation command without any explanation of capabilities, domain, or usage triggers.

Suggestions

Add specific concrete actions describing what the skill does, e.g., 'Allocates compute resources across clusters, balances workloads, and manages capacity planning' or whatever the actual functionality is.

Add an explicit 'Use when...' clause with natural trigger terms users would say, e.g., 'Use when the user asks about resource allocation, capacity planning, workload distribution, or scaling infrastructure.'

Clarify the specific domain of 'resource allocation' to distinguish this skill from other potentially overlapping skills (e.g., is this about cloud infrastructure, team staffing, budget allocation, etc.).

Dimension	Reasoning	Score
Specificity	The description contains no concrete actions whatsoever. 'Agent skill for resource-allocator' is entirely vague — it doesn't describe what the skill does, what resources it allocates, or any specific capabilities.	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of functionality and no 'Use when...' clause or equivalent trigger guidance.	1 / 3
Trigger Term Quality	The only potentially relevant term is 'resource-allocator', which is a technical/internal name rather than a natural keyword a user would say. There are no natural trigger terms like 'allocate', 'assign resources', 'capacity planning', etc.	1 / 3
Distinctiveness Conflict Risk	'Resource-allocator' is extremely generic and could refer to memory allocation, team resource management, cloud infrastructure provisioning, budget allocation, or countless other domains. There is nothing to distinguish it from other skills.	1 / 3
	Total	4 / 12 Passed

Validation

90%

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 — 10 / 11 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	10 / 11 Passed

Repository: ruvnet/claude-flow
Path: .agents/skills/agent-resource-allocator/SKILL.md
Commit: 913f9ea

Reviewed: 3 days ago

Table of Contents

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