agent-resource-allocator
Agent skill for resource-allocator - invoke with $agent-resource-allocator
31
0%
Does it follow best practices?
Impact
80%
5.00xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-resource-allocator/SKILL.mdQuality
Discovery
0%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 is essentially a label with an invocation command rather than a functional description. It provides zero information about what the skill does, what domain it operates in, or when it should be selected. It is among the weakest possible descriptions for skill selection purposes.
Suggestions
Add concrete actions describing what the skill does, e.g., 'Allocates cloud computing resources, scales infrastructure, and manages capacity across services.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about resource allocation, scaling, capacity planning, or infrastructure provisioning.'
Specify the domain clearly to distinguish from other potential 'resource' skills, e.g., clarify whether this is for cloud resources, project 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 'Use when...' clause, no explanation of functionality, and no trigger guidance whatsoever. | 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,' 'assign resources,' 'capacity planning,' or any domain-specific terms. | 1 / 3 |
Distinctiveness Conflict Risk | 'Resource-allocator' is extremely generic and could refer to cloud infrastructure, project management, memory allocation, budget allocation, or countless other domains. Without specificity, it would be impossible to distinguish from other skills. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a massive dump of non-executable pseudocode that defines fictional classes and methods for resource allocation, predictive scaling, circuit breaking, and performance profiling. None of the code is real or actionable—it references non-existent libraries, models, and MCP methods. The content fails on all dimensions: it's extremely verbose, provides no executable guidance, lacks any workflow structure, and has no progressive disclosure.
Suggestions
Replace fictional class definitions with actual executable code or concrete CLI commands that Claude can run, referencing real tools and APIs.
Add a clear multi-step workflow (e.g., 1. Collect metrics → 2. Analyze bottlenecks → 3. Apply allocation → 4. Validate results) with explicit validation checkpoints.
Reduce content by 80%+ by removing pseudocode class stubs and focusing only on the specific commands, configurations, and decision logic Claude needs to perform resource allocation.
Split detailed reference material (e.g., KPI definitions, ML model configurations) into separate bundle files and reference them from a concise overview in SKILL.md.
| 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 extensive method stubs that explain concepts Claude already understands (circuit breakers, genetic algorithms, LSTM models, memory profiling). Nearly every token is filler—none of these classes are real, importable, or executable. | 1 / 3 |
Actionability | None of the code is executable—it references non-existent classes (CPUAllocator, LSTMTimeSeriesModel, IsolationForestModel, DeepQNetworkAgent, etc.) and fictional MCP methods. The bash commands reference 'npx claude-flow' with undocumented flags and placeholder arguments. There is nothing copy-paste ready or concretely actionable. | 1 / 3 |
Workflow Clarity | There is no clear multi-step workflow or sequenced process. The content is organized as class definitions and method signatures without any guidance on when or how to invoke them, in what order, or with what validation checkpoints. The operational commands section lists commands but provides no workflow connecting them. | 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. Everything is dumped inline with no separation of overview from detail. There are no bundle files to support progressive disclosure either. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
90%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
- Commit
e6dc21f
- Reviewed
Table of Contents
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