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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%

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

Quality

Content

20%Scale 1-3

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 delegates all real work to other skills via vague 'Copy-Paste Prompts' while providing no executable code, specific patterns, or implementation details of its own. The repetitive phase structure inflates token usage without adding value, and every action item is abstract enough to be unhelpful.

Suggestions

Replace abstract action items with concrete, executable code examples for at least one framework (e.g., a minimal CrewAI multi-agent setup or a LangGraph workflow graph).

Eliminate the repetitive phase template structure — consolidate into a concise workflow overview with a decision tree for choosing between frameworks (CrewAI vs LangGraph vs custom).

Add validation checkpoints with specific commands or checks between phases (e.g., 'Run agent with test input X and verify output matches Y').

Remove explanations Claude already knows (e.g., 'Use this workflow when building autonomous AI agents') and replace with specific patterns, anti-patterns, or gotchas unique to agent development.

DimensionReasoningScore

Conciseness

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' are just single-line references to other skills, repeated seven times in the same pattern. Most content is padding.

1 / 3

Actionability

No executable code, no concrete commands, no specific examples of agent implementation. Every action item is abstract ('Choose agent framework', 'Implement agent logic', 'Configure memory'). The 'Copy-Paste Prompts' just say 'Use @skill-name to do X' without any actual implementation guidance. This describes rather than instructs.

1 / 3

Workflow Clarity

The phases are sequenced logically (design → implement → multi-agent → orchestration → tools → memory → evaluation), and there's a quality gates checklist. However, there are no validation checkpoints between phases, no feedback loops for error recovery, and no guidance on when to skip or revisit phases. The checklist items are too vague to serve as real validation.

2 / 3

Progressive Disclosure

References to other skills are present and clearly signaled (e.g., @crewai, @langgraph, @agent-memory-systems), and related workflow bundles are listed. However, no bundle files are provided, so the references can't be verified. The SKILL.md itself is monolithic with repetitive content that could be condensed significantly, and the seven nearly identical phase sections create unnecessary bulk.

2 / 3

Total

6

/

12

Passed

Description

54%Scale 1-3

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 with good framework-specific trigger terms (CrewAI, LangGraph) that make it distinctive. However, it lacks a 'Use when...' clause, which is critical for Claude to know when to select this skill, and the capabilities listed are more categorical than concretely actionable.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about building AI agents, setting up multi-agent workflows, or working with CrewAI or LangGraph frameworks.'

List more specific concrete actions, e.g., 'Define agent roles and goals, configure tool integrations, set up inter-agent communication, debug agent execution flows, design crew/graph topologies.'

Include common user phrasing variations like 'agentic workflow', 'agent pipeline', 'crew setup', 'LangGraph state machine' 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 these are more like categories than concrete specific actions. It doesn't list granular tasks like '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 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 are terms users would naturally use when seeking help with agent development.

3 / 3

Distinctiveness Conflict Risk

The combination of specific frameworks (CrewAI, LangGraph) and the focused domain of AI agent development/orchestration creates a clear niche that is unlikely to conflict with other skills like general coding or generic AI/ML 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.

Validation — 10 / 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
popey/claude-code-skills
Reviewed

Table of Contents

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