agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural
36
0%
Does it follow best practices?
Impact
100%
3.03xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-safla-neural/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely weak description that provides virtually no useful information for skill selection. It only names the skill and its invocation command without describing any capabilities, use cases, or trigger conditions. This description would be essentially unusable in a multi-skill environment.
Suggestions
Add concrete actions describing what safla-neural actually does (e.g., 'Performs neural network analysis on...', 'Generates embeddings for...').
Add an explicit 'Use when...' clause with natural trigger terms that describe the situations and user requests where this skill should be selected.
Include domain-specific keywords and common variations that users would naturally use when they need this skill's capabilities.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for safla-neural' is entirely vague and abstract, providing no information about what the skill actually does. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command ('$agent-safla-neural') with no explanation of purpose or trigger conditions. | 1 / 3 |
Trigger Term Quality | The only keyword is 'safla-neural', which is technical jargon that no user would naturally say when requesting a task. There are no natural language trigger terms present. | 1 / 3 |
Distinctiveness Conflict Risk | While 'safla-neural' is a unique name, the description is so vague that Claude would have no basis to distinguish when to select this skill over any other. The lack of any functional description makes proper selection impossible. | 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 reads as a persona description and marketing document rather than actionable instructions. It makes impressive-sounding claims (172K ops/sec, 60% compression, quantum neural patterns) without any substantiation or executable guidance. The code examples are non-functional pseudocode using undefined variables and non-standard syntax, and the entire skill lacks any concrete workflow for accomplishing tasks.
Suggestions
Replace the persona description and capability bullet list with concrete, executable steps for the primary use case (e.g., 'When asked to create a persistent memory system, do X, Y, Z').
Provide real, copy-paste-ready MCP tool invocation examples with proper syntax and realistic parameter values instead of pseudocode with undefined variables.
Define a clear workflow with numbered steps and validation checkpoints for at least one core task (e.g., setting up a feedback loop or initializing memory persistence).
Remove unsubstantiated performance claims (172K ops/sec, 60% compression) and marketing language; focus on what Claude should actually do when this skill is invoked.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanations of concepts Claude already knows (memory tiers, semantic understanding, episodic memory). The bullet-pointed capability list reads like marketing copy with specific but unsubstantiated performance claims (172,000+ ops/sec, 60% compression). Most of the content describes rather than instructs. | 1 / 3 |
Actionability | The MCP integration examples use non-standard JavaScript-like syntax that isn't executable (no proper function call syntax, uses undefined variables like `interaction_context`, `result_metrics`). The four-tier memory model is purely descriptive with no concrete implementation steps. There's no guidance on what to actually do when invoked. | 1 / 3 |
Workflow Clarity | There is no workflow, sequence, or process defined. The skill describes what the agent supposedly is and can do, but never explains how to accomplish any task. No validation steps, no error handling, no feedback loops despite claiming 'Feedback Loop Engineering' as a core capability. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files, no clear navigation structure, and no separation of overview from detailed content. The four-tier memory model description is inlined despite being lengthy, and there's no indication of where to find more detailed guidance. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- ruvnet/ruflo
- Commit
398f7c2
- Reviewed
Table of Contents
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