ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
46
33%
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 ./skills/antigravity-ai-agent-development/SKILL.mdQuality
Discovery
47%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 benefits from good trigger terms including specific framework names (CrewAI, LangGraph) and relevant domain terminology. However, it lacks a 'Use when...' clause, which is critical for Claude to know when to select this skill, and the capability description remains at a high level without listing concrete actions the skill enables.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about building AI agents, setting up CrewAI crews, designing LangGraph workflows, or orchestrating multi-agent systems.'
List more specific concrete actions, e.g., 'Define agent roles and goals, configure agent tools, set up inter-agent communication, build LangGraph state machines, create CrewAI crews and tasks.'
Add file type or project structure triggers if applicable, e.g., 'when working with crew.py, agent configuration files, or LangGraph graph definitions.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI agent development) and mentions some actions/concepts like 'building autonomous agents, multi-agent systems, and agent orchestration,' but doesn't list specific concrete actions (e.g., 'define agent roles, configure tool usage, set up agent communication pipelines'). | 2 / 3 |
Completeness | Describes 'what' at a high level but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, a missing 'Use when...' clause caps completeness at 2, and the 'what' is also somewhat vague, bringing this to 1. | 1 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'AI agent', 'autonomous agents', 'multi-agent systems', 'agent orchestration', 'CrewAI', 'LangGraph', 'custom agents'. These cover both conceptual and framework-specific terms users would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | The mention of specific frameworks (CrewAI, LangGraph) adds distinctiveness, but the broad terms 'AI agent development workflow' and 'custom agents' could overlap with general coding skills or other AI/ML skills. Somewhat specific but not fully distinct. | 2 / 3 |
Total | 8 / 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 hollow template that repeats the same structure seven times with no substantive technical content. It provides no executable code, no concrete examples, no specific API usage, and no real implementation guidance—just abstract action items and references to other skills. The workflow structure is logical but the complete lack of actionable content makes this skill of very limited practical value.
Suggestions
Replace vague action items with concrete, executable code examples for at least one framework (e.g., a minimal CrewAI multi-agent setup or a LangGraph workflow with actual Python code).
Remove the repetitive phase template structure and consolidate into a concise decision tree or table that maps use cases to frameworks and patterns.
Add specific validation steps with concrete commands or code snippets (e.g., how to test agent behavior, how to verify memory retrieval works correctly).
Eliminate the 'Copy-Paste Prompts' sections entirely—they add no value—and replace with actual copy-paste-ready code snippets or configuration examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose and repetitive. Each phase follows an identical template with vague action items that Claude already knows (e.g., 'Define agent purpose', 'Add error handling'). The 'Copy-Paste Prompts' sections are trivial one-liners that add no real value. Most of the content is padding with no substantive information. | 1 / 3 |
Actionability | There is no executable code, no concrete commands, no specific API examples, and no real implementation guidance. Every action item is abstract and vague (e.g., 'Choose agent framework', 'Implement agent logic', 'Configure memory'). The 'copy-paste prompts' just say 'Use @skill-name to do X' which provides no actual technical guidance. | 1 / 3 |
Workflow Clarity | The phases are clearly sequenced and logically ordered from design through evaluation, and there is a quality gates checklist. However, there are no validation checkpoints within phases, no feedback loops for error recovery, and no guidance on what to do when things fail. The steps within each phase are too vague to constitute a real workflow. | 2 / 3 |
Progressive Disclosure | The skill references many other skills/files (e.g., @crewai, @langgraph, @agent-tool-builder) which suggests progressive disclosure, and it has a related workflows section. However, the references are not clearly signaled as links, and the main file itself is bloated with repetitive structure that could be condensed significantly. The ASCII architecture diagram is a nice touch but insufficient to elevate the score. | 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
- boisenoise/skills-collections
- Commit
431bfad
- Reviewed
Table of Contents
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