agent-safla-neural
Agent skill for safla-neural - invoke with $agent-safla-neural
Install with Tessl CLI
npx tessl i github:ruvnet/claude-flow --skill agent-safla-neural43
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillEvaluation — 100%
↑ 3.03xAgent success when using this skill
Validation for skill structure
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 critically deficient across all dimensions. It provides only a name and invocation command with zero information about what the skill does, when to use it, or what problems it solves. Claude would have no basis for selecting this skill appropriately.
Suggestions
Add concrete actions describing what this skill does (e.g., 'Performs neural network analysis', 'Processes machine learning models', etc.)
Include a 'Use when...' clause with natural trigger terms that users would actually say when they need this functionality
Explain what 'safla-neural' means and what domain or problem space this skill addresses to distinguish it from other skills
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. 'Agent skill for safla-neural' is completely abstract with no indication of 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 a name and invocation syntax with no functional information. | 1 / 3 |
Trigger Term Quality | The only terms present are 'agent skill', 'safla-neural', and the invocation command. These are technical/internal terms, not natural keywords a user would say when needing this functionality. | 1 / 3 |
Distinctiveness Conflict Risk | Without any description of capabilities or use cases, this skill cannot be distinguished from any other skill. The term 'safla-neural' is meaningless without context. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
22%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like marketing documentation than actionable guidance. It spends significant tokens describing capabilities and concepts Claude already understands while providing minimal concrete workflow for actually implementing SAFLA patterns. The two code examples are a start but lack context, sequencing, and validation steps.
Suggestions
Remove the capability bullet lists and four-tier memory model explanation - replace with a concise workflow showing how to initialize, train, store, and retrieve SAFLA patterns in sequence
Add validation steps after neural_train and memory_usage calls - what should Claude check to confirm success? What errors might occur?
Provide complete, contextual examples showing input data and expected outputs for the MCP calls
Extract reference material (memory tier details, architecture specs) to a separate REFERENCE.md file if needed, keeping SKILL.md focused on actionable steps
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanations of concepts Claude already knows (memory tiers, neural architectures). The marketing-style language ('truly intelligent', 'excels at') and capability lists add no actionable value. Most content describes rather than instructs. | 1 / 3 |
Actionability | Provides two code examples with MCP tool calls, but they are incomplete - missing context on when to use them, what the expected outputs are, and how to handle responses. The four-tier memory model is descriptive rather than instructive. | 2 / 3 |
Workflow Clarity | No clear workflow or sequence for using SAFLA capabilities. The two code snippets are isolated examples without context on how they fit together, what order to execute them, or any validation/verification steps for these neural training operations. | 1 / 3 |
Progressive Disclosure | Content is organized with headers and code blocks, but everything is inline in one file with no references to additional documentation. The four-tier memory model explanation could be extracted to a reference file, keeping the main skill leaner. | 2 / 3 |
Total | 6 / 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.
- Reviewed
Table of Contents
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