ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
32
26%
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 ./plugins/antigravity-awesome-skills-claude/skills/ai-ml/SKILL.mdQuality
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description identifies a broad AI/ML domain and lists several sub-areas but reads more like a category label than an actionable skill description. It lacks concrete actions (verbs), explicit trigger guidance ('Use when...'), and is too broad to be clearly distinguishable from more specialized AI-related skills.
Suggestions
Add an explicit 'Use when...' clause with trigger terms like 'build a chatbot', 'implement RAG', 'create an AI agent', 'set up an ML pipeline', 'integrate LLM APIs'.
Replace the broad category listing with specific concrete actions using verbs, e.g., 'Builds LLM-powered applications, implements retrieval-augmented generation with vector databases, designs multi-agent architectures, configures ML training pipelines.'
Narrow the scope or add more natural user-facing keywords like 'embeddings', 'vector database', 'prompt engineering', 'OpenAI', 'langchain', 'chatbot' to improve trigger term coverage and reduce conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI/ML) and lists several areas like 'LLM application development, RAG implementation, agent architecture, ML pipelines,' but these are broad categories rather than concrete actions. No specific verbs describing what the skill actually does (e.g., 'builds', 'configures', 'deploys'). | 2 / 3 |
Completeness | Describes 'what' at a high level (AI/ML workflow covering several areas) but completely lacks any 'when' clause or explicit trigger guidance. The absence of a 'Use when...' clause caps this at 2 per the rubric, and the 'what' is also quite vague, bringing it down to 1. | 1 / 3 |
Trigger Term Quality | Includes some relevant keywords users might say like 'RAG', 'LLM', 'agent', 'ML pipelines', and 'AI-powered features', but misses many common variations and natural phrases users would use such as 'chatbot', 'embeddings', 'vector database', 'fine-tuning', 'prompt engineering', 'model training', etc. | 2 / 3 |
Distinctiveness Conflict Risk | The scope is extremely broad—covering LLM apps, RAG, agents, ML pipelines, and AI features—which could easily overlap with more specialized skills in any of those individual areas. It's somewhat specific to AI/ML but the breadth creates significant conflict risk. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
20%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a table of contents that lists other skills and provides generic, non-actionable steps for each phase. It contains no concrete code, no specific architectural patterns, no configuration examples, and no real decision-making guidance. The content is highly verbose relative to the information density, with repetitive structures (skills list → vague actions → trivial prompts) repeated seven times.
Suggestions
Replace vague action items with concrete, actionable guidance — e.g., instead of 'Choose embedding model', provide a decision matrix or specific recommendations with tradeoffs (OpenAI ada-002 for cost, Cohere for multilingual, etc.)
Add at least one executable code example per phase showing a key integration pattern (e.g., a minimal RAG pipeline with actual Python code, an agent setup with LangGraph)
Add validation checkpoints and decision criteria between phases — e.g., 'Before moving to Phase 3, verify: LLM responses return in <2s, error rate <1%, prompt templates produce expected output format'
Dramatically reduce the content by removing the repetitive 'Copy-Paste Prompts' sections (which add no value) and consolidating the skill references into a single reference table with brief descriptions of when to use each
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. The skill is essentially a long list of skill names, generic action items (e.g., 'Define AI use cases', 'Choose appropriate models'), and copy-paste prompts that are trivially simple. Most content is organizational scaffolding with no substantive information Claude couldn't infer. The checklists are generic and add little value. | 1 / 3 |
Actionability | No concrete code, commands, or executable guidance anywhere. Every 'action' is a vague directive like 'Set up vector database' or 'Implement chunking strategy' with no specifics. The copy-paste prompts are just 'Use @skill-name to do X' which provide no real instruction. There are no examples, no configuration snippets, no architecture patterns. | 1 / 3 |
Workflow Clarity | The phases are clearly sequenced and logically ordered (design → integration → RAG → agents → ML → observability → security). However, there are no validation checkpoints, no feedback loops, no error recovery guidance, and no criteria for when to move between phases. The checklists at the end are generic and disconnected from the workflow steps. | 2 / 3 |
Progressive Disclosure | The skill references many sub-skills by name, providing some structure for navigation. However, there are no bundle files to support the references, the references are just skill names without clear file paths or descriptions of what each contains, and the main file itself is a wall of repetitive content that could be significantly condensed. | 2 / 3 |
Total | 6 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
22710e9
- Reviewed
Table of Contents
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