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 selected. Claude would have no meaningful basis for choosing this skill over any other.
Suggestions
Replace the generic label with specific concrete actions the skill performs, e.g., 'Allocates cloud compute resources across projects, balances workloads, and optimizes capacity utilization.'
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 balancing, or provisioning infrastructure.'
Clarify the domain of 'resources' (e.g., cloud infrastructure, team staffing, budget) to make the skill distinctly identifiable and reduce conflict risk with other skills.
| 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 | The description is so vague that 'resource-allocator' could refer to anything—memory allocation, cloud resource provisioning, 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 |
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 aspirational design document masquerading as actionable guidance. It consists entirely of non-executable pseudocode referencing fictional classes and APIs, with no clear workflow, no validation steps, and extreme verbosity. It provides no practical value for Claude to actually perform resource allocation tasks.
Suggestions
Replace fictional class hierarchies with actual executable code or concrete CLI commands that work with a real system (e.g., real MCP tool calls with documented parameters and expected outputs).
Add a clear step-by-step workflow: e.g., 1) Collect metrics, 2) Analyze bottlenecks, 3) Calculate allocation, 4) Validate plan, 5) Apply changes — with specific commands and validation checkpoints at each step.
Reduce content by 80%+ — remove all conceptual code (RL agents, genetic algorithms, LSTM models) that Claude already understands and focus only on project-specific configuration, tool invocations, and decision criteria.
Add concrete examples with real input/output: show what a resource allocation request looks like, what the response contains, and how to interpret and act on it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~500+ lines. The content is filled with elaborate class definitions, method stubs, and conceptual code that Claude already understands (genetic algorithms, LSTM models, circuit breakers, reinforcement learning). Most code is non-executable pseudocode dressed up as real implementations with placeholder classes that don't exist. | 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. Nothing is copy-paste ready or practically usable. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequence of steps for performing resource allocation. The content is organized as class definitions and method signatures without any guidance on when or how to use them. 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 blocks with no references to external files and no meaningful organization hierarchy. All content is dumped inline with no separation of overview from detail. The sections are just increasingly long code blocks with no navigation structure. | 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
0f7c750
- Reviewed
Table of Contents
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