agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural
36
0%
Does it follow best practices?
Impact
100%
3.03xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-safla-neural/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely weak description that provides virtually no useful information for skill selection. It only names the skill and its invocation command without describing any capabilities, use cases, or trigger conditions. This would be nearly impossible for Claude to correctly select from a pool of available skills.
Suggestions
Add concrete actions describing what safla-neural actually does (e.g., 'Performs neural network analysis', 'Generates embeddings', etc.).
Add an explicit 'Use when...' clause with natural trigger terms that users would say when they need this skill.
Replace the generic 'Agent skill for safla-neural' with a specific capability summary that distinguishes it from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for safla-neural' is entirely vague and abstract, providing no information about what the skill actually does. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command ('$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 | The description is so vague that Claude would have no basis for distinguishing this skill from any other. 'Agent skill' is completely generic and provides no niche or distinct triggers. | 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 essentially a marketing description of a 'SAFLA Neural Specialist' persona rather than actionable instructions. It explains abstract concepts Claude already understands, provides non-executable pseudo-code examples, and lacks any concrete workflow, validation steps, or progressive disclosure. The content reads like a product brochure rather than a skill that teaches Claude how to perform specific tasks.
Suggestions
Replace the abstract capability list and memory architecture description with concrete, executable MCP tool invocation examples showing exact syntax and real parameter values.
Add a step-by-step workflow for at least one core task (e.g., 'Setting up persistent memory for a new agent') with explicit validation checkpoints after each step.
Remove marketing-style claims ('172,000+ operations per second', '60% compression') and conceptual explanations of memory types that Claude already understands.
Provide concrete examples with actual input/output pairs showing what a successful SAFLA neural pattern setup looks like, including error cases and recovery steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanations of concepts Claude already knows (what vector memory is, what episodic memory is, what working memory is). The capability bullet list is marketing-style padding ('172,000+ operations per second', '60% compression') with no actionable value. The four-tier memory model description is purely conceptual explanation that doesn't teach Claude anything new. | 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 bulk of the content describes capabilities and architecture abstractly rather than providing concrete, copy-paste-ready instructions for performing specific tasks. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequenced process. The skill describes what the agent supposedly does but never explains how to actually accomplish any task step-by-step. No validation checkpoints, no error recovery, no feedback loops despite claiming 'Feedback Loop Engineering' as a core capability. | 1 / 3 |
Progressive Disclosure | Monolithic content with no references to supporting files, no clear navigation structure, and no separation of overview from detailed content. Everything is dumped into a single file with no organization beyond basic headers, and there are no bundle files to support it. | 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/claude-flow
- Commit
d29d87f
- Reviewed
Table of Contents
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