agent-sona-learning-optimizer
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer
23
3%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/agent-sona-learning-optimizer/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 description is essentially non-functional as a skill selector. It provides only the skill's internal name and invocation syntax, with zero information about capabilities, use cases, or trigger conditions. Claude would have no basis for selecting this skill appropriately from a list of available skills.
Suggestions
Add a clear 'what' clause describing the specific actions this skill performs (e.g., 'Optimizes learning schedules, recommends study strategies, tracks knowledge retention').
Add an explicit 'Use when...' clause with natural trigger terms users would say (e.g., 'Use when the user asks about study plans, learning optimization, spaced repetition, or improving retention').
Remove the invocation syntax from the description and replace it with domain-specific keywords that distinguish this skill from other potentially similar skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. It only names itself ('sona-learning-optimizer') and provides an invocation command, with no indication of what the skill actually does. | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is answered. The description only provides an invocation command, leaving both questions entirely unaddressed. | 1 / 3 |
Trigger Term Quality | There are no natural keywords a user would say. 'sona-learning-optimizer' is an internal tool name, not a term users would naturally use when requesting help. No domain-relevant trigger terms are present. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it provides no distinguishing characteristics. Without knowing what the skill does, it could conflict with any learning, optimization, or general-purpose skill. | 1 / 3 |
Total | 4 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads like a marketing brochure or README rather than an actionable skill file. It is dominated by capability descriptions and benchmark numbers but provides almost no concrete instructions on how to actually use the SONA learning optimizer. The only actionable content is two bash commands for hooks, buried at the bottom with insufficient context.
Suggestions
Replace the capability descriptions and benchmark tables with a concrete step-by-step workflow showing how to initialize, execute a task with SONA learning, and verify results.
Add executable code examples for the core operations (pattern discovery, LoRA fine-tuning invocation, LLM routing configuration) instead of just describing them.
Remove marketing-style claims and performance numbers that don't help Claude execute tasks—move benchmarks to a separate reference file if needed.
Add validation checkpoints to the workflow (e.g., how to verify learning was applied, how to check for quality regression).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is heavily padded with marketing-style claims ('+55% quality improvement', '761 decisions/sec', '99% parameter reduction') and explanations of concepts Claude already knows (what LoRA is, what EWC does). Most of the content describes capabilities rather than instructing on how to use them. The performance benchmarks table adds bulk without actionable value. | 1 / 3 |
Actionability | The skill provides almost no executable guidance. The only concrete commands are the two hook invocations at the bottom, but there's no explanation of how to actually use the SONA learning optimizer in practice—no workflow for fine-tuning, no code for pattern discovery, no examples of LLM routing configuration. It describes rather than instructs. | 1 / 3 |
Workflow Clarity | There is no clear multi-step workflow. The skill lists capabilities and performance numbers but never sequences them into a usable process. The pre-task/post-task hooks hint at a workflow but lack context on when/how to use them, what happens between them, and there are no validation or error-handling steps. | 1 / 3 |
Progressive Disclosure | There is a reference to an integration guide (docs/RUVECTOR_SONA_INTEGRATION.md) and a package reference, which is appropriate. However, the main content is a monolithic description of capabilities and benchmarks that could be better organized, and the references are minimal and not clearly signaled for different use cases. | 2 / 3 |
Total | 5 / 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
ccb062f
- Reviewed
Table of Contents
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