ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
51
26%
Does it follow best practices?
Impact
97%
0.97xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-ai-agent-development/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 clear domain (AI agent development) and names specific frameworks, which helps with identification. However, it lacks a 'Use when...' clause, lists category-level capabilities rather than concrete actions, and could benefit from more natural trigger terms that users would actually say when requesting help with agent development.
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 such as 'define agent roles and goals, configure agent tools, set up inter-agent communication, debug agent execution loops, implement ReAct patterns'.
Include additional natural trigger terms users might say, such as 'agentic workflow', 'LLM agent', 'tool-calling agent', 'agent framework', 'ReAct', or 'agent pipeline'.
| 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 it does (AI agent development workflow) 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 since the 'what' is also somewhat vague, this scores a 1. | 1 / 3 |
Trigger Term Quality | Includes relevant keywords like 'CrewAI', 'LangGraph', 'autonomous agents', 'multi-agent systems', and 'agent orchestration' which users might mention. However, it misses common variations like 'agentic workflow', 'tool-calling agents', 'ReAct pattern', 'agent loop', or 'LLM agents'. | 2 / 3 |
Distinctiveness Conflict Risk | The mention of specific frameworks (CrewAI, LangGraph) helps distinguish it, but 'AI agent development' and 'custom agents' are broad enough to potentially overlap with general Python development skills or other AI/ML skills. | 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 high-level project plan template with no actionable content. It delegates all real work to other skills via vague 'Copy-Paste Prompts' while providing no concrete code, commands, configuration examples, or specific technical guidance. The repetitive phase structure inflates token count without adding value, and the action items read like generic project management checklists rather than executable instructions.
Suggestions
Add concrete, executable code examples for at least one framework (e.g., a minimal CrewAI multi-agent setup or a LangGraph workflow) instead of just listing abstract action items.
Replace vague actions like 'Choose agent framework' and 'Implement agent logic' with specific decision criteria, code snippets, or configuration examples that Claude can directly use.
Consolidate the repetitive phase structure - each phase follows the same template with Skills/Actions/Prompts but none add substantive content. A condensed table or single-section overview would be far more token-efficient.
Add validation steps within phases, such as how to verify an agent is working correctly, specific test commands, or error patterns to watch for.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and repetitive. Each phase follows an identical template with vague action lists that add no real value. The 'Copy-Paste Prompts' sections are just one-liners that could be inferred. Much of the content is structural padding rather than substantive instruction. | 1 / 3 |
Actionability | No concrete code, commands, or executable examples anywhere. Every phase consists of abstract action items like 'Choose agent framework' and 'Implement agent logic' without any specific guidance on how to do these things. The 'Copy-Paste Prompts' are just vague directives to invoke other skills. | 1 / 3 |
Workflow Clarity | The phases are clearly sequenced and logically ordered, and 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 for what to do when things fail. | 2 / 3 |
Progressive Disclosure | References to other skills are present throughout each phase, and related workflow bundles are listed. However, the main content is a monolithic wall of repetitive phase templates that could be significantly condensed, and the references to invoked skills lack any description of what those skills actually contain. | 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
636b862
- Reviewed
Table of Contents
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