ai-agent-development
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
63
Quality
44%
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
54%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 has strong trigger term coverage with specific framework names (CrewAI, LangGraph) and domain terminology that would help Claude identify when to use this skill. However, it critically lacks explicit 'Use when...' guidance and could benefit from more concrete action verbs describing what the skill actually does beyond the general 'workflow' framing.
Suggestions
Add a 'Use when...' clause with explicit triggers like 'Use when the user asks about building agents, creating multi-agent workflows, or mentions CrewAI, LangGraph, or agent orchestration'
Replace vague 'workflow' with specific concrete actions like 'Define agent roles and goals, configure agent communication patterns, build agent pipelines, debug agent behavior'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI agent development) and mentions some actions/concepts (building autonomous agents, multi-agent systems, agent orchestration), but doesn't list concrete specific actions like 'create agent workflows', 'define agent roles', or 'configure agent communication'. | 2 / 3 |
Completeness | Describes what (AI agent development workflow) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all. | 1 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'AI agent', 'autonomous agents', 'multi-agent systems', 'agent orchestration', 'CrewAI', 'LangGraph', 'custom agents' - these are terms developers naturally use when working in this space. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche with distinct triggers - the specific mention of CrewAI, LangGraph, and agent-specific terminology creates a well-defined scope that's unlikely to conflict with general coding or other AI-related skills. | 3 / 3 |
Total | 9 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill functions as a workflow orchestrator that delegates to other skills, but provides almost no standalone actionable content. The structure is clear but the lack of concrete code examples, specific implementation guidance, or validation steps makes it more of a table of contents than a useful skill. The repetitive phase format adds bulk without adding value.
Suggestions
Add at least one concrete, executable code example for a simple agent implementation (e.g., a basic LangGraph or CrewAI agent setup) to demonstrate the workflow in action
Replace generic action items like 'Define agent purpose' with specific, actionable guidance such as template questions to answer or concrete deliverables to produce
Add validation checkpoints with specific commands or tests to verify each phase is complete (e.g., 'Run `pytest tests/agent_tools.py` to verify tool integration')
Condense the repetitive phase structure - consider a table format for the skill invocations and consolidate similar phases
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is moderately efficient but includes repetitive structure across phases (each phase follows identical format with Skills/Actions/Prompts). The copy-paste prompts are minimal but the action lists are somewhat generic and could be condensed. | 2 / 3 |
Actionability | The skill provides only vague, abstract guidance with no executable code, concrete examples, or specific implementation details. Actions like 'Define agent purpose' and 'Design agent capabilities' describe rather than instruct, and the copy-paste prompts just reference other skills without providing actual implementation guidance. | 1 / 3 |
Workflow Clarity | The phases are clearly sequenced and the workflow structure is logical, but there are no validation checkpoints, feedback loops, or error recovery steps. The quality gates at the end are just checkboxes without guidance on how to verify each item. | 2 / 3 |
Progressive Disclosure | The skill references many other skills appropriately (one level deep), but the main content itself is somewhat monolithic with repetitive phase structures. The references are signaled but the organization could be tighter with the overview being more concise. | 2 / 3 |
Total | 7 / 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
5c5ae21
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.