ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
36
33%
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 ./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 keyword coverage including specific framework names (CrewAI, LangGraph) and relevant domain terms. However, it lacks a 'Use when...' clause entirely, and the capabilities listed are more categorical than concretely actionable. Adding explicit trigger conditions and more specific actions would significantly improve skill selection accuracy.
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 concrete actions instead of categories, e.g., 'Define agent roles and goals, configure agent tools, set up crew task pipelines, build LangGraph state machines, implement agent memory and communication.'
Add file type or pattern triggers if applicable, e.g., 'when working with crew.py, agents.yaml, or LangGraph graph definitions.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI agent development) and some actions (building autonomous agents, multi-agent systems, agent orchestration), but these are more like categories than concrete specific actions. It doesn't list granular tasks like 'define agent roles', 'configure tool usage', or '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 the rubric, 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 terms and specific framework names users would mention. | 3 / 3 |
Distinctiveness Conflict Risk | The mention of specific frameworks (CrewAI, LangGraph) helps distinguish it, but 'AI agent development' is broad enough to potentially overlap with general Python development skills, LLM integration skills, or other AI-related skills. The lack of explicit boundaries increases conflict risk. | 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 high-level project management template with no concrete, actionable content. It lists seven phases of agent development but provides zero executable code, no specific framework usage examples, and no real implementation guidance — just abstract action items like 'Implement agent logic' and trivial copy-paste prompts. The repetitive phase structure inflates token usage without adding value.
Suggestions
Replace abstract action items with concrete, executable code examples for at least the core phases (e.g., a minimal CrewAI multi-agent setup, a LangGraph workflow definition, a tool implementation pattern).
Remove the repetitive phase template structure and consolidate into a concise decision tree: given the user's goal (single agent vs. multi-agent vs. orchestration), point directly to the relevant skill with a concrete starter example.
Add validation checkpoints with specific commands or checks within each phase (e.g., 'Run agent with test input X and verify output matches Y') rather than just a final checklist.
Eliminate the 'Copy-Paste Prompts' sections entirely — they add no value — and replace with actual code snippets or configuration examples that demonstrate framework usage.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. Each phase follows an identical template with vague action lists that add no value Claude doesn't already know. The 'Copy-Paste Prompts' are trivial one-liners that don't provide real guidance. The entire skill could be condensed to a fraction of its size. | 1 / 3 |
Actionability | No executable code, no concrete commands, no specific examples. Every phase consists of abstract action items like 'Choose agent framework' and 'Implement agent logic' without any actual implementation details. The 'Copy-Paste Prompts' just say 'Use @skill-name to do X' which is not actionable guidance. | 1 / 3 |
Workflow Clarity | The phases are clearly sequenced and the overall flow from design through evaluation is logical. There's a quality gates checklist at the end. However, there are no validation checkpoints within phases, no error recovery steps, and no feedback loops — just linear lists of vague actions. | 2 / 3 |
Progressive Disclosure | The skill references many other skills/bundles by name, which is a form of progressive disclosure. However, no bundle files are provided, the references are just skill names without links or paths, and the main file itself is a wall of repetitive content that could benefit from better organization. | 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
cfced3a
- Reviewed
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