agent-sona-learning-optimizer
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer
18
3%
Does it follow best practices?
Impact
—
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 zero information about what the skill does, when it should be used, or what domain it operates in. It consists solely of a generic 'agent skill' label and an invocation command, making it impossible for Claude to make an informed selection decision.
Suggestions
Add a clear description of what the skill does with concrete actions (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 learning optimization, study plans, spaced repetition, or improving retention').
Remove or relocate the invocation command from the description and replace it with functional content that distinguishes this skill from other agent skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. It only states it is an 'agent skill' with an invocation command, providing no information about what the skill actually does. | 1 / 3 |
Completeness | Neither 'what does this do' nor 'when should Claude use it' is answered. The description provides only an invocation command with no functional or contextual information. | 1 / 3 |
Trigger Term Quality | There are no natural keywords a user would say. 'sona-learning-optimizer' is a technical identifier, not a term users would naturally use in conversation. No domain-relevant trigger terms are present. | 1 / 3 |
Distinctiveness Conflict Risk | The description is so vague that it could conflict with any learning, optimization, or agent-related skill. The only distinguishing element is the specific agent name, which provides no semantic clarity. | 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 product marketing page rather than an actionable skill for Claude. It is dominated by benchmark statistics, capability descriptions, and performance claims, but provides almost no concrete instructions on how to actually perform tasks using SONA. The two hook commands are the only actionable content in the entire file, and even those lack context about when and how to use them.
Suggestions
Replace the capability descriptions and benchmark tables with concrete, step-by-step workflows showing how Claude should use SONA for specific tasks (e.g., how to trigger LoRA fine-tuning, how to use pattern discovery).
Add executable code examples or commands for each core capability, not just the pre/post hooks—show how to actually invoke adaptive learning, pattern retrieval, and LLM routing.
Remove marketing-style performance claims and benchmark statistics that don't help Claude execute tasks; if benchmarks are needed for routing decisions, put them in a referenced file.
Define a clear workflow with validation steps: what Claude should do before, during, and after using SONA, including how to verify that learning was successful and how to handle failures.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with marketing-style claims (+55% quality improvement, 761 decisions/sec, 2211 ops/sec) and benchmark statistics that don't help Claude execute any task. It explains what SONA is and its capabilities at length without providing actionable instructions. Much of the content reads like a product README rather than a skill. | 1 / 3 |
Actionability | The skill provides almost no executable guidance. The only concrete commands are two hook invocations (pre-task and post-task), but there's no explanation of how to actually use SONA for learning, fine-tuning, pattern discovery, or LLM routing. The bulk of the content describes capabilities and benchmarks rather than instructing Claude on what to do. | 1 / 3 |
Workflow Clarity | There is no clear workflow or sequenced steps for any of the described capabilities. The hooks section shows a pre-task and post-task command but doesn't explain when or how to integrate them into a workflow, what happens between them, or how to validate outcomes. No validation checkpoints or error recovery are mentioned. | 1 / 3 |
Progressive Disclosure | The content references an integration guide (docs/RUVECTOR_SONA_INTEGRATION.md) and a package, which is appropriate progressive disclosure. However, no bundle files are provided to verify these references exist, and the main content itself is poorly organized—mixing marketing claims with the few actionable elements rather than structuring content for discovery. | 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/claude-flow
- Commit
ca77f83
- Reviewed
Table of Contents
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