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 known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-resource-allocator/SKILL.md

Quality

Content

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 sketch masquerading as an actionable skill. It consists entirely of non-executable pseudocode referencing fictional classes, libraries, and MCP methods, providing no concrete guidance Claude could act on. The extreme verbosity (~500+ lines) wastes token budget on conceptual code that teaches Claude nothing it doesn't already understand about resource allocation patterns.

Suggestions

Replace fictional pseudocode with actual executable commands or real MCP tool invocations that Claude can use for resource allocation tasks.

Reduce content to under 100 lines focusing on a clear workflow: when to allocate resources, what commands/tools to use, how to validate the allocation succeeded, and how to handle failures.

Add explicit step-by-step workflows with validation checkpoints, e.g., '1. Check current usage → 2. Calculate needed resources → 3. Apply allocation → 4. Verify allocation succeeded → 5. If failed, rollback and retry'.

If detailed reference material is needed, split it into separate bundle files and reference them from a concise SKILL.md overview.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~500+ lines of pseudocode-style JavaScript that Claude cannot execute. The code defines classes like `AdaptiveResourceAllocator`, `PredictiveScaler`, `AdaptiveCircuitBreaker`, and `PerformanceProfiler` with methods that reference non-existent libraries and constructors. It explains concepts Claude already knows (circuit breaker patterns, LSTM, genetic algorithms) and pads extensively with boilerplate class structures.	1 / 3
Actionability	None of the code is executable — it references fictional classes (`CPUAllocator`, `LSTMTimeSeriesModel`, `IsolationForestModel`, `DeepQNetworkAgent`), fictional MCP methods (`mcp.neural_train`, `mcp.model_save`, `mcp.daa_resource_alloc`), and fictional CLI commands (`npx claude-flow metrics-collect`). There is no concrete, copy-paste-ready guidance that Claude could actually use to accomplish anything.	1 / 3
Workflow Clarity	There is no clear multi-step workflow with sequencing or validation checkpoints. The content describes conceptual systems and class architectures but never provides a step-by-step process for Claude to follow when performing resource allocation. No validation steps, no error recovery loops, no decision points are explicitly sequenced.	1 / 3
Progressive Disclosure	The content is a monolithic wall of code with no references to external files and no bundle files to support it. All content is inline with no clear navigation structure. The sections are just increasingly long code blocks with no meaningful progressive disclosure or separation of overview from detail.	1 / 3
	Total	4 / 12 Passed

Description

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 essentially only provides the skill's name and invocation command. It fails on every dimension: no concrete actions, no natural trigger terms, no explanation of what it does or when to use it, and no distinguishing characteristics. It would be nearly impossible for Claude to correctly select this skill from a pool of available skills.

Suggestions

Add specific concrete actions describing what the skill does, e.g., 'Allocates compute resources across clusters, balances workload distribution, and manages capacity planning.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about resource allocation, capacity planning, workload balancing, or distributing resources across services.'

Specify the domain clearly to reduce conflict risk—clarify whether this is about cloud infrastructure, team staffing, budget allocation, or another type of resource management.

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 technical jargon and not something a user would naturally say. There are no natural keywords like 'allocate resources,' 'assign capacity,' 'distribute,' or any domain-specific terms.	1 / 3
Distinctiveness Conflict Risk	The description is so vague that 'resource-allocator' could refer to anything—cloud infrastructure, memory management, team staffing, budget allocation, etc. It provides no clear niche or distinct triggers to differentiate 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/ruflo
Commit: 28c81c0

Reviewed: about 19 hours ago

Table of Contents

Discovery Implementation Validation

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