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 rather than a functional description. It provides no information about what the skill does, what domain it operates in, or when it should be invoked. It fails on every dimension of the rubric.
Suggestions
Describe the specific actions the skill performs (e.g., 'Allocates cloud computing resources across clusters, balances workloads, and manages capacity planning').
Add an explicit 'Use when...' clause with natural trigger terms users would say (e.g., 'Use when the user needs to allocate resources, balance workloads, manage capacity, or distribute computing tasks').
Specify the domain clearly to distinguish this from other potential resource-related skills (e.g., cloud infrastructure, project staffing, budget allocation).
| 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 and no explanation of functionality beyond the tool name itself. | 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, memory management, project staffing, 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 an extremely verbose, non-actionable collection of conceptual JavaScript class definitions that describe an idealized resource allocation system. None of the code is executable, no real tools or APIs are concretely specified, and there is no workflow guidance. It reads like aspirational architecture documentation rather than a skill that teaches Claude how to perform specific tasks.
Suggestions
Replace conceptual class definitions with actual executable commands or code that Claude can run, such as specific CLI commands with real arguments and expected outputs.
Add a clear workflow section with numbered steps, validation checkpoints, and error recovery for common resource allocation tasks (e.g., scaling a swarm, diagnosing a bottleneck).
Reduce content by 80%+ — remove all pseudo-architectural code and focus on the 5-10 concrete actions Claude needs to perform, with specific examples of inputs and expected outputs.
Add a quick-start section that shows one complete, end-to-end example of resource allocation with actual tool calls and their responses.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~500+ lines with massive code blocks that are entirely non-executable pseudocode/conceptual class definitions. Explains architectural patterns (circuit breakers, genetic algorithms, LSTM models, reinforcement learning) that Claude already knows, and none of the code is actually runnable or tied to a real implementation. | 1 / 3 |
Actionability | Despite the volume of code, nothing is executable. All classes reference undefined dependencies (CPUAllocator, LSTMTimeSeriesModel, IsolationForestModel, etc.), MCP calls reference unverified APIs, and the CLI commands at the end use placeholder syntax without concrete examples of actual usage. This is architectural fiction, not actionable guidance. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequence of steps for performing resource allocation. The content presents disconnected class definitions and method signatures without explaining when or how to use them, in what order, or with what validation checkpoints. No error recovery or verification steps are defined. | 1 / 3 |
Progressive Disclosure | The entire content is a monolithic wall of code blocks with no references to external files, no layered structure, and no clear navigation. Everything is dumped inline with no separation between overview, quick start, and detailed reference material. | 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/ruflo
- Commit
398f7c2
- Reviewed
Table of Contents
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