ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
61
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
92%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 a strong skill description that clearly articulates specific capabilities (RAG, vector search, agent orchestration) and includes an explicit 'Use PROACTIVELY for...' clause with natural trigger terms. The main weakness is potential overlap with other AI-related skills due to some broader terms like 'AI-powered applications' and 'enterprise AI integrations'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Build production-ready LLM applications', 'advanced RAG systems', 'intelligent agents', 'vector search', 'multimodal AI', 'agent orchestration', and 'enterprise AI integrations'. | 3 / 3 |
Completeness | Clearly answers both what (build LLM apps, RAG systems, agents, vector search, etc.) AND when ('Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications'). The explicit 'Use PROACTIVELY for...' clause provides clear trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'LLM', 'RAG', 'chatbots', 'AI agents', 'AI-powered applications', 'vector search', 'multimodal AI'. These cover common variations of how users describe AI/ML application development. | 3 / 3 |
Distinctiveness Conflict Risk | While specific to AI/LLM development, terms like 'AI-powered applications' and 'enterprise AI integrations' are broad enough to potentially overlap with other AI-related skills. The RAG, vector search, and agent orchestration terms help distinguish it, but 'LLM features' could conflict with simpler AI integration skills. | 2 / 3 |
Total | 11 / 12 Passed |
Implementation
12%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 comprehensive capability catalog or persona description rather than actionable instructions. It extensively lists technologies and concepts Claude already knows (LLM providers, vector databases, frameworks) without providing concrete implementation guidance, executable code, or clear workflows. The content would benefit from dramatic reduction and replacement with specific, executable examples.
Suggestions
Replace the extensive capability lists with 2-3 concrete, executable code examples showing common patterns (e.g., a basic RAG implementation, an agent workflow)
Move detailed technology references to a separate REFERENCE.md file and keep SKILL.md focused on quick-start guidance
Add validation checkpoints to workflows, especially for production deployments (e.g., 'Validate embeddings before indexing', 'Test retrieval quality before deployment')
Remove sections that describe Claude's knowledge (Capabilities, Knowledge Base, Behavioral Traits) and focus on project-specific instructions and constraints
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive lists of technologies, capabilities, and knowledge areas that Claude already knows. The document reads like a resume or capability catalog rather than actionable instructions, with massive sections like 'Capabilities' and 'Knowledge Base' that add little instructional value. | 1 / 3 |
Actionability | No executable code, no concrete commands, no specific examples with inputs/outputs. The 'Instructions' section is just 4 vague bullet points. 'Example Interactions' lists prompts but provides no actual implementation guidance or copy-paste ready solutions. | 1 / 3 |
Workflow Clarity | The 4-step Instructions section and 8-step Response Approach provide a basic sequence, but lack validation checkpoints, feedback loops, or concrete verification steps. For complex AI systems involving data pipelines and production deployments, this is insufficient. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files for detailed content. All information is dumped inline with no clear navigation structure. The massive capability lists should be in separate reference files with the SKILL.md providing a concise overview. | 1 / 3 |
Total | 5 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Reviewed
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