agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural
36
0%
Does it follow best practices?
Impact
100%
3.03xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-safla-neural/SKILL.mdQuality
Discovery
0%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an extremely weak description that provides virtually no useful information for skill selection. It only states it is an 'agent skill' with a name and invocation command, but fails to describe any capabilities, actions, use cases, or trigger conditions. This description would be essentially useless when choosing among multiple available skills.
Suggestions
Add concrete actions describing what safla-neural actually does (e.g., 'Performs neural network analysis...', 'Processes neural embeddings...').
Add an explicit 'Use when...' clause with natural trigger terms that describe scenarios where this skill should be selected.
Replace the generic 'Agent skill for safla-neural' with a substantive description of the skill's domain, capabilities, and distinguishing features.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for safla-neural' is entirely vague and abstract, providing no information about what the skill actually does. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command ('$agent-safla-neural') with no explanation of purpose or trigger conditions. | 1 / 3 |
Trigger Term Quality | The only keyword is 'safla-neural', which is a technical/internal name that users would not naturally say. There are no natural language trigger terms that a user would use when needing this skill. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it provides no distinguishing characteristics. 'Agent skill' is completely generic and could apply to any agent-based skill, making it impossible to differentiate from others. | 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 marketing copy rather than actionable technical guidance. It describes abstract capabilities and architectural concepts without providing concrete, executable instructions for any specific task. The code examples are non-functional pseudocode with undefined variables, and the entire four-tier memory model section explains concepts Claude already understands without adding any project-specific or tool-specific value.
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 clear step-by-step workflow for at least one complete task (e.g., 'Setting up a persistent memory agent') with validation checkpoints after each step.
Remove conceptual explanations of memory types (vector, episodic, semantic, working) that Claude already knows, and instead document the specific API calls, parameters, and expected responses.
Provide concrete examples with actual input/output pairs showing what a successful invocation looks like versus a failed one, including error 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 padded marketing copy ('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 handling, 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 bundle files to support it, yet the content is long enough to warrant splitting. | 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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