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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.

Quality

70%

Does it follow best practices?

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tessl review fix ./skills/antigravity-ai-agent-development/SKILL.md

The canonical home for this skill is ai-agent-development in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

67%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, lean workflow index with a clear phase sequence and per-phase invocation prompts. Its main weakness is actionability: the per-phase actions are abstract directives rather than executable guidance, and validation exists only as an end-of-document checklist without feedback loops.

Suggestions

Make each phase's actions concrete — add example commands, code snippets, or acceptance criteria (e.g. what 'Test agent behavior' means and how to verify it).

Tie the Quality Gates to specific phases and add a feedback loop, e.g. 'If a gate fails, return to the corresponding phase and fix before proceeding.'

Remove duplication: merge 'When to Use This Workflow' with the description content, and drop the per-phase 'Copy-Paste Prompts' that merely restate the primary 'Skills to Invoke' entry.

DimensionReasoningScore

Conciseness

The body is list-based and lean with no explanation of concepts Claude already knows, but 'When to Use This Workflow' duplicates the description and each phase pairs 'Skills to Invoke' with a copy-paste prompt that restates the primary skill. Minor trims would tighten it.

4 / 5

Actionability

Copy-paste prompts per phase ('Use @langgraph to create stateful agent workflows') and named skills to invoke are concrete, but the 'Actions' lists are high-level directives ('Implement agent logic', 'Configure memory', 'Test agent behavior') with no code, commands, or acceptance criteria.

3 / 5

Workflow Clarity

The 7-phase sequence (Design → Single Agent → Multi-Agent → Orchestration → Tools → Memory → Evaluation) is clear, with a test step in each phase and a Quality Gates checklist. However, the gates sit at the end detached from the phases and there is no feedback loop for what to do when a gate fails.

4 / 5

Progressive Disclosure

No bundle files exist; the body is a well-organized index with one-level-deep, clearly signaled sibling-skill references and clean per-phase headers. It exceeds the under-50-line simple-skill exception and carries minor duplication between 'When to Use', 'Skills to Invoke', and the prompts.

4 / 5

Total

15

/

20

Passed

Description

61%Weight 40%Scale 1-5

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 names a clear domain with recognizable trigger terms and named frameworks, but it is missing any 'when to use' guidance and relies on generic action verbs. It is serviceable but below the quality of the rubric's good examples, which pair concrete actions with explicit trigger clauses.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks to build AI agents, multi-agent systems, or agent workflows with CrewAI or LangGraph.'

Replace generic verbs with concrete actions such as 'design agent roles', 'implement tool integration', or 'set up agent memory and evaluation'.

Include common user synonyms like 'AI agents', 'agent framework', or 'agent teams' to broaden natural trigger coverage.

DimensionReasoningScore

Specificity

Names the domain and concrete deliverables ('building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph') but the action verbs are generic ('building', 'development workflow') with no specific actions like 'design agent roles' or 'implement tool integration'.

3 / 5

Completeness

Has a clear 'what' (a workflow for building autonomous agents, multi-agent systems, and orchestration) but no 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the rubric guidelines.

3 / 5

Trigger Term Quality

Includes natural phrases users would say — 'autonomous agents', 'multi-agent systems', 'agent orchestration', 'CrewAI', 'LangGraph' — but misses common variations such as 'AI agents', 'agent framework', or 'agent teams'.

4 / 5

Distinctiveness Conflict Risk

Named frameworks (CrewAI, LangGraph) create a distinct niche, but the broad 'AI agent development' umbrella overlaps with the sub-skills it indexes (crewai, langgraph, autonomous-agents) and adjacent bundles (ai-ml, rag-implementation).

4 / 5

Total

14

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

boisenoise/skills-collections

Path

skills/antigravity-ai-agent-development/SKILL.md

Reviewed

Table of Contents

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