ai-agent-development

Name: ai-agent-development
Author: tomevault-io

AI Community

Repo stars 0
Author updated Live
Author repo skills-registry

Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network

Domain: AI
Compatible agents: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
Trust score: 88 / 100 · community maintained
Author / version / license: @tomevault-io · no license declared
Token usage: Lean
Setup complexity: Plug-and-play
External API key: Not required
Operating systems: Unspecified (assume cross-platform)
Runtime requirements: No special requirements
Permissions: Read-only
Write / modify
Network behavior: Local-only
Install commands: 26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview ai-agent-development.preview

---
name: ai-agent-development
description: AI agent development workflow for building autonomous agents, multi-agent systems, and agent orc…
category: ai
runtime: no special runtime
---

# ai-agent-development output preview

## PART A: Task fit
- Use case: AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / When to Use This Workflow / Workflow Phases” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Overview / When to Use This Workflow / Workflow Phases” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.

SKILL.md SKILL.md

---
name: ai-agent-development
description: AI agent development workflow for building autonomous agents, multi-agent systems, and agent orc…
category: ai
source: tomevault-io/skills-registry
---

# ai-agent-development

## When to use
- AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.

## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.

## Execution rules
- Organize steps around “Overview / When to Use This Workflow / Workflow Phases” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.

## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.

Evidence boundary and execution chain

The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.

skill "ai-agent-development" {
  input      -> user goal + target files + boundaries + acceptance criteria
  context    -> Overview / When to Use This Workflow / Workflow Phases
  rules      -> SKILL.md triggers / order / output contract
  runtime    -> no special runtime | read files, write/modify files | mostly runs locally
  guardrails -> usually needs no extra API key + small-sample validation + diff/log review
  output     -> copyable result + checklist + next iteration
}

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGrap…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “When to Use This Workflow”, “Workflow Phases”, “Phase 1: Agent Design”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name ai-agent-development directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / When to Use This Workflow / Workflow Phases” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

AI Agent Development Workflow

Overview

Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.

When to Use This Workflow

Use this workflow when:

Building autonomous AI agents
Creating multi-agent systems
Implementing agent orchestration
Adding tool integration to agents
Setting up agent memory

Workflow Phases

Phase 1: Agent Design

Skills to Invoke

ai-agents-architect - Agent architecture
autonomous-agents - Autonomous patterns

Actions

Define agent purpose
Design agent capabilities
Plan tool integration
Design memory system
Define success metrics

Copy-Paste Prompts

Use @ai-agents-architect to design AI agent architecture

Phase 2: Single Agent Implementation

Skills to Invoke

autonomous-agent-patterns - Agent patterns
autonomous-agents - Autonomous agents

Actions

Choose agent framework
Implement agent logic
Add tool integration
Configure memory
Test agent behavior

Copy-Paste Prompts

Use @autonomous-agent-patterns to implement single agent

Phase 3: Multi-Agent System

Skills to Invoke

crewai - CrewAI framework
multi-agent-patterns - Multi-agent patterns

Actions

Define agent roles
Set up agent communication
Configure orchestration
Implement task delegation
Test coordination

Copy-Paste Prompts

Use @crewai to build multi-agent system with roles

Phase 4: Agent Orchestration

Skills to Invoke

langgraph - LangGraph orchestration
workflow-orchestration-patterns - Orchestration

Actions

Design workflow graph
Implement state management
Add conditional branches
Configure persistence
Test workflows

Copy-Paste Prompts

Use @langgraph to create stateful agent workflows

Phase 5: Tool Integration

Skills to Invoke

agent-tool-builder - Tool building
tool-design - Tool design

Actions

Identify tool needs
Design tool interfaces
Implement tools
Add error handling
Test tool usage

Copy-Paste Prompts

Use @agent-tool-builder to create agent tools

Phase 6: Memory Systems

Skills to Invoke

agent-memory-systems - Memory architecture
conversation-memory - Conversation memory

Actions

Design memory structure
Implement short-term memory
Set up long-term memory
Add entity memory
Test memory retrieval

Copy-Paste Prompts

Use @agent-memory-systems to implement agent memory

Phase 7: Evaluation

Skills to Invoke

agent-evaluation - Agent evaluation
evaluation - AI evaluation

Actions

Define evaluation criteria
Create test scenarios
Measure agent performance
Test edge cases
Iterate improvements

Copy-Paste Prompts

Use @agent-evaluation to evaluate agent performance

Agent Architecture

User Input -> Planner -> Agent -> Tools -> Memory -> Response
              |          |        |        |
         Decompose   LLM Core  Actions  Short/Long-term

Quality Gates

Agent logic working
Tools integrated
Memory functional
Orchestration tested
Evaluation passing

ai-ml - AI/ML development
rag-implementation - RAG systems
workflow-automation - Workflow patterns

Source: Andruia/antigravity-awesome-skills — distributed by TomeVault.

Install command

One-click installPick a runner, click Copy, paste into your terminal, press enter

npx skills add tomevault-io/skills-registry/andruia--antigravity-awesome-skills--ai-agent-development -y

Open general install guide

View skill source

Works with

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

Security audit

88 / 100

Community

未声明 license/许可。
未声明 version。
未限定 allowed-tools，默认拥有全部工具权限。

Metadata

Source: github
tomevault-io/skills-registry
中文

More by @tomevault-io

Category & tags

Category AI

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

AI Agent Development Workflow

Overview

When to Use This Workflow

Workflow Phases

Phase 1: Agent Design

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 2: Single Agent Implementation

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 3: Multi-Agent System

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 4: Agent Orchestration

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 5: Tool Integration

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 6: Memory Systems

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 7: Evaluation

Skills to Invoke

Actions

Copy-Paste Prompts

Agent Architecture

Quality Gates

Related Workflow Bundles

Discussion