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ai-agent-development

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

39

Quality

37%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/antigravity-awesome-skills-claude/skills/ai-agent-development/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

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 a high-level workflow outline that delegates all actual work to other skills without providing any concrete, actionable guidance itself. It is extremely verbose and repetitive, with each phase following an identical template of vague action items. The content describes rather than instructs, offering no executable code, specific commands, or meaningful examples that would help Claude build AI agents.

Suggestions

Replace vague action items (e.g., 'Choose agent framework', 'Implement agent logic') with concrete, executable code examples for at least one framework (e.g., a minimal CrewAI or LangGraph agent implementation).

Condense the seven nearly identical phase sections into a compact workflow table or numbered list, eliminating the repetitive structure of Skills/Actions/Prompts per phase.

Add validation checkpoints with specific commands or checks between phases (e.g., 'Run agent with test input and verify tool calls are logged before proceeding to multi-agent setup').

Remove explanations of concepts Claude already knows (e.g., what autonomous agents are, what memory systems do) and focus on project-specific patterns, gotchas, and concrete implementation details.

DimensionReasoningScore

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 just single-line references to other skills. The entire document 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 guidance. The 'Copy-Paste Prompts' are just skill invocation references, not actionable instructions.

1 / 3

Workflow Clarity

The phases are clearly sequenced and logically ordered from design through evaluation, and there's a quality gates checklist. However, there are no validation checkpoints between phases, no error recovery guidance, and no feedback loops for when things go wrong.

2 / 3

Progressive Disclosure

References to other skills are present throughout (e.g., @crewai, @langgraph, @agent-tool-builder), providing some progressive disclosure structure. However, no bundle files exist to support these references, and the main document itself is bloated with repetitive content that doesn't serve as a useful overview.

2 / 3

Total

6

/

12

Passed

Description

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 identifies a clear niche in AI agent development and includes good framework-specific trigger terms (CrewAI, LangGraph). However, it lacks explicit 'Use when...' guidance and the capability descriptions remain at a category level rather than listing concrete actions. Adding trigger conditions and more specific tasks 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 communication.'

List more concrete actions such as 'define agent roles and goals, configure agent tools, set up inter-agent communication, design state graphs, implement agent memory and context sharing.'

Include common user phrasings and file/concept references like 'agentic workflows', 'tool-calling agents', 'agent pipelines', 'crew tasks' to improve trigger term coverage.

DimensionReasoningScore

Specificity

Names the domain (AI agent development) and some actions (building autonomous agents, multi-agent systems, agent orchestration), but doesn't list concrete specific actions like 'define agent roles', 'configure tool usage', 'set up agent communication pipelines'. The actions are more category-level than task-level.

2 / 3

Completeness

Describes 'what' at a high level but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per the rubric, a missing 'Use when...' clause should cap completeness at 2, and since the 'what' is also somewhat vague, this scores a 1.

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 a developer would naturally use when seeking help with agent development.

3 / 3

Distinctiveness Conflict Risk

The combination of AI agent development with specific frameworks (CrewAI, LangGraph) and concepts (multi-agent systems, agent orchestration) creates a clear niche that is unlikely to conflict with general coding skills or other AI-related skills.

3 / 3

Total

9

/

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
sickn33/antigravity-awesome-skills
Reviewed

Table of Contents

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