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 is an extremely weak description that provides virtually no useful information for skill selection. It reads as a placeholder or auto-generated label rather than a functional description. It fails on every dimension—no concrete actions, no trigger terms, no 'when to use' guidance, and no distinctiveness.
Suggestions
Describe the specific actions this skill performs (e.g., 'Allocates compute 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 tasks across infrastructure').
Specify the domain and resource types to make the skill distinctive and reduce conflict risk (e.g., cloud infrastructure resources, project team assignments, memory 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 actually does, what resources it allocates, or how. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities 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 it's impossible to distinguish this skill from any other. 'Resource-allocator' could overlap with infrastructure management, project planning, memory management, or countless other domains. | 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 an actionable skill. It consists entirely of non-executable pseudocode referencing fictional classes and APIs, with no concrete guidance Claude could actually follow. The massive volume of illustrative code wastes token budget while providing zero actionable instructions for resource allocation tasks.
Suggestions
Replace fictional class hierarchies with actual executable code or concrete CLI commands that Claude can run, including real tool names and realistic input/output examples.
Define a clear step-by-step workflow for common resource allocation tasks (e.g., 1. Collect metrics → 2. Analyze bottlenecks → 3. Apply allocation → 4. Validate results) with explicit validation checkpoints.
Reduce content by 80%+ by removing illustrative pseudocode for concepts Claude already understands (circuit breakers, RL training loops, memory profiling) and focus only on project-specific configuration and commands.
Add concrete examples with actual inputs and expected outputs for the CLI commands, and clarify which MCP tools are real vs. hypothetical.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~500+ lines of non-executable pseudocode. Explains concepts Claude already knows (circuit breakers, genetic algorithms, LSTM, reinforcement learning). The code is illustrative class definitions with placeholder methods that add no actionable value—every class references undefined dependencies and unimplemented helper methods. | 1 / 3 |
Actionability | None of the code is executable—it references fictional classes (CPUAllocator, LSTMTimeSeriesModel, IsolationForestModel, DeepQNetworkAgent, etc.) and fictional MCP methods (mcp.neural_train, mcp.daa_resource_alloc, mcp.swarm_scale). The CLI commands at the end use placeholder syntax with no concrete examples of actual inputs or outputs. Nothing is copy-paste ready. | 1 / 3 |
Workflow Clarity | There is no clear multi-step workflow or sequenced process. The content is organized as a catalog of class definitions without any guidance on when to use what, in what order, or how to validate results. 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 bundle files to support it, and no layered structure. Everything is dumped inline with no navigation aids. The 'Integration Points' section lists connections to other agents but provides no links or references. | 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
619b263
- Reviewed
Table of Contents
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