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, domain, 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').
Replace the invocation-focused description with capability-focused language in third person voice that distinguishes 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 seeking 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 datasheet rather than an actionable instruction set. It is dominated by performance claims, benchmark numbers, and capability bullet points, but provides almost no concrete guidance on how Claude should actually use the SONA learning optimizer in practice. The only executable content is two bash commands with no surrounding workflow context.
Suggestions
Replace the capability descriptions and benchmark tables with a concrete step-by-step workflow showing how to use SONA learning in a real task (e.g., when to invoke pre-task hooks, how to interpret results, when to invoke post-task hooks, and what to do on failure).
Remove marketing-style claims and benchmark numbers that don't help Claude execute tasks — move them to a separate reference file if needed.
Add concrete examples showing a complete task lifecycle: initialization, execution with pattern retrieval, recording outcomes, and handling errors.
Provide explicit validation steps and decision points (e.g., what happens if post-task reports failure, how to verify learning was applied correctly).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is heavily padded with marketing-style claims ('+55% quality improvement', '761 decisions/sec'), benchmark tables, and capability descriptions that read like a product brochure rather than actionable instructions. Much of this is self-promotional fluff that doesn't help Claude perform any task. | 1 / 3 |
Actionability | The skill provides almost no actionable guidance. The only concrete commands are two hook invocations (pre-task/post-task), but there's no explanation of when to use them, what the outputs mean, or how to actually integrate SONA learning into a workflow. The bulk of the content describes capabilities and benchmarks rather than instructing Claude what to do. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequenced steps. The skill lists capabilities and performance numbers but never describes a process for how to actually use the learning optimizer. The pre/post task hooks hint at a workflow but lack any sequencing, validation, error handling, or decision points. | 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 itself is poorly organized — benchmark data and capability lists dominate instead of a concise overview pointing to detailed resources. | 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
398f7c2
- Reviewed
Table of Contents
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