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. It is essentially a label rather than a description.
Suggestions
Add concrete actions describing what safla-neural actually does (e.g., 'Performs neural network analysis, trains models, processes embeddings').
Add an explicit 'Use when...' clause with natural trigger terms that describe scenarios where this skill should be selected (e.g., 'Use when the user asks about neural processing, vector embeddings, or model training').
Replace the invocation instruction ('invoke with $agent-safla-neural') with functional information — invocation syntax is operational detail, not a description of capability.
| 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 states it's an 'agent skill' and how to invoke it, with no functional or contextual information. | 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 'Agent skill' is completely generic and provides no distinguishing information about its purpose, making it impossible to differentiate from other agent skills. | 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 marketing description or persona prompt rather than an actionable skill document. It explains abstract concepts Claude already understands, provides non-executable pseudo-code examples, lacks any concrete workflow or validation steps, and has no progressive disclosure structure. The content would need a complete rewrite to be useful as a skill.
Suggestions
Replace the conceptual architecture description with concrete, executable MCP tool call examples showing exact syntax and expected responses
Add a clear step-by-step workflow for common tasks (e.g., 'Setting up persistent memory', 'Creating a feedback loop') with validation checkpoints after each step
Remove the capability bullet list and four-tier memory model explanation—these are abstract descriptions that don't help Claude perform any task
If the skill involves multiple complex topics, split into a concise SKILL.md overview with references to separate files for memory architecture, training workflows, and safety constraints
| 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 reads like marketing copy rather than actionable instructions. Claims like '172,000+ operations per second' and '60% compression' are meaningless without context. The four-tier memory model description is purely conceptual padding. | 1 / 3 |
Actionability | The MCP integration examples use non-standard syntax (not valid JavaScript, not valid JSON, not valid MCP tool calls) and are not executable. The skill describes what it can do rather than instructing how to do it. There are no concrete, copy-paste-ready commands or workflows—just abstract architecture descriptions and pseudo-code. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequence of steps. The content presents a conceptual architecture and two disconnected code snippets with no sequencing, no validation checkpoints, no error handling, and no feedback loops despite claiming to be about feedback loop engineering. For a skill involving neural training and memory persistence, the absence of any workflow is a critical gap. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block with no references to external files, no bundle files to support it, and no clear navigation structure. The four-tier memory model and capability list could be split into reference documents, but instead everything is dumped inline with no organization beyond a single heading. | 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
619b263
- Reviewed
Table of Contents
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