Agentic Development

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha ea59a0c53d79 · 14 Oct 2026Report an issue or request a re-scan

• Publisheralinaqi GitHub userOwner of the repository this skill is read from
• Source folderalinaqi/claude-bootstrap/skills/agentic-developmentThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictea59a0c53d79A new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)

---
name: agentic-development
description: Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
when-to-use: When building AI agents, tool-using LLM systems, or agentic workflows
user-invocable: false
effort: high
---

# Agentic Development Skill


For building autonomous AI agents that perform multi-step tasks with tools.

**Sources:** [Claude Agent SDK](https://docs.anthropic.com/en/docs/agents-and-tools/claude-agent-sdk) | [Anthropic Claude Code Best Practices](https://www.anthropic.com/engineering/claude-code-best-practices) | [Pydantic AI](https://ai.pydantic.dev/) | [Google Gemini Agent Development](https://developers.googleblog.com/en/building-agents-google-gemini-open-source-frameworks/) | [OpenAI Building Agents](https://developers.openai.com/tracks/building-agents/)

---

## Framework Selection by Language

| Language/Framework | Default | Why |
|-------------------|---------|-----|
| **Python** | **Pydantic AI** | Type-safe, Pydantic validation, multi-model, production-ready |
| **Node.js / Next.js** | **Claude Agent SDK** | Official Anthropic SDK, tools, multi-agent, native streaming |

### Python: Pydantic AI (Default)
```python
from pydantic_ai import Agent
from pydantic import BaseModel

class SearchResult(BaseModel):
    title: str
    url: str
    summary: str

agent = Agent(
    'claude-sonnet-4-6',
    result_type=list[SearchResult],
    system_prompt='You are a research assistant.',
)

# Type-safe result
result = await agent.run('Find articles about AI agents')
for item in result.data:
    print(f"{item.title}: {item.url}")
```

### Node.js / Next.js: Claude Agent SDK (Default)
```typescript
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

// Define tools
const tools: Anthropic.Tool[] = [
  {
    name: "web_search",
    description: "Search the web for information",
    input_schema: {
      type: "object",
      properties: {
        query: { type: "string", description: "Search query" },
      },
      required: ["query"],
    },
  },
];

// Agentic loop
async function runAgent(prompt: string) {
  const messages: Anthropic.MessageParam[] = [
    { role: "user", content: prompt },
  ];

  while (true) {
    const response = await client.messages.create({
      model: "claude-sonnet-4-6",
      max_tokens: 4096,
      tools,
      messages,
    });

    // Check for tool use
    if (response.stop_reason === "tool_use") {
      const toolUse = response.content.find((b) => b.type === "tool_use");
      if (toolUse) {
        const result = await executeTool(toolUse.name, toolUse.input);
        messages.push({ role: "assistant", content: response.content });
        messages.push({
          role: "user",
          content: [{ type: "tool_result", tool_use_id: toolUse.id, content: result }],
        });
        continue;
      }
    }

    // Done - return final response
    return response.content.find((b) => b.type === "text")?.text;
  }
}
```

---

## Core Principle

**Plan first, act incrementally, verify always.**

Agents that research and plan before executing consistently outperform those that jump straight to action. Break complex tasks into verifiable steps, use tools judiciously, and maintain clear state throughout execution.

---

## Agent Architecture

### Three Components (OpenAI)
```
┌─────────────────────────────────────────────────┐
│                    AGENT                        │
├─────────────────────────────────────────────────┤
│  Model (Brain)      │ LLM for reasoning &       │
│                     │ decision-making           │
├─────────────────────┼───────────────────────────┤
│  Tools (Arms/Legs)  │ APIs, functions, external │
│                     │ systems for action        │
├─────────────────────┼───────────────────────────┤
│  Instructions       │ System prompts defining   │
│  (Rules)            │ behavior & boundaries     │
└─────────────────────┴───────────────────────────┘
```

### Project Structure
```
project/
├── src/
│   ├── agents/
│   │   ├── orchestrator.ts    # Main agent coordinator
│   │   ├── specialized/       # Task-specific agents
│   │   │   ├── researcher.ts
│   │   │   ├── coder.ts
│   │   │   └── reviewer.ts
│   │   └── base.ts            # Shared agent interface
│   ├── tools/
│   │   ├── definitions/       # Tool schemas
│   │   ├── implementations/   # Tool logic
│   │   └── registry.ts        # Tool discovery
│   ├── prompts/
│   │   ├── system/            # Agent instructions
│   │   └── templates/         # Task templates
│   └── memory/
│       ├── conversation.ts    # Short-term context
│       └── persistent.ts      # Long-term storage
├── tests/
│   ├── agents/                # Agent behavior tests
│   ├── tools/                 # Tool unit tests
│   └── evals/                 # End-to-end evaluations
└── skills/                    # Agent skills (Anthropic pattern)
    ├── skill-name/
    │   ├── instructions.md
    │   ├── scripts/
    │   └── resources/
```

---

## Workflow Pattern: Explore-Plan-Execute-Verify

### 1. Explore Phase
```typescript
// Gather context before acting
async function explore(task: Task): Promise<Context> {
  const relevantFiles = await agent.searchCodebase(task.query);
  const existingPatterns = await agent.analyzePatterns(relevantFiles);
  const dependencies = await agent.identifyDependencies(task);

  return { relevantFiles, existingPatterns, dependencies };
}
```

### 2. Plan Phase (Critical)
```typescript
// Plan explicitly before execution
async function plan(task: Task, context: Context): Promise<Plan> {
  const prompt = `
    Task: ${task.description}
    Context: ${JSON.stringify(context)}

    Create a step-by-step plan. For each step:
    1. What action to take
    2. What tools to use
    3. How to verify success
    4. What could go wrong

    Output JSON with steps array.
  `;

  return await llmCall({ prompt, schema: PlanSchema });
}
```

### 3. Execute Phase
```typescript
// Execute with verification at each step
async fu

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source ea59a0c53d79

Questions about Agentic Development

• github.com/alinaqi/claude-bootstrap/tree/main/skills/agentic-dev — the audited source at commit ea59a0c53d79 and every finding aboveObserved · 14 Oct 2026

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