console.agent – The jQuery of AI Agents

Comprehensive guide for implementing @console-agent/agent — drop console.agent(...) anywhere in your code to execute agentic workflows with the simplicity of console.log().

Official Documentation: https://console-agent.github.io
Package: @console-agent/agent (npm) / console-agent (PyPI)
Version: v1.2.0
Provider: Google Gemini (gemini-2.5-flash-lite, gemini-3-flash-preview)

When to Apply

Reference this skill when:

• User wants to add AI agent capabilities to their code
• User asks about debugging with AI assistance
• User mentions “console.agent” or agentic workflows
• User wants runtime analysis, security audits, or code review
• User needs intelligent logging, testing assistance, or data validation
• User asks about tools like code execution, Google Search, or file analysis

Core Concepts

1. Fire-and-Forget by Default (Non-blocking)

// Returns immediately, agent runs async in background
console.agent("analyze this error", error);
// Code continues executing...

2. Blocking Mode (Await for Structured Results)

// Wait for complete AgentResult
const result = await console.agent("validate email", email);
if (!result.success) throw new Error(result.summary);

3. AI Personas (Auto-detected or Explicit)

• Security 🛡️ – OWASP expert (detects: injection, xss, vulnerability, csrf)
• Debugger 🐛 – Senior debugging expert (detects: slow, error, optimize, crash)
• Architect 🏗️ – Principal engineer (detects: design, architecture, schema)
• General 🔍 – Full-stack senior engineer (default fallback)

4. Output Modes

Default (verbose: false):

SQL injection detected in user input
fix: Use parameterized queries
severity: critical

Verbose (verbose: true):

[AGENT] ✓ 🛡️ Security audit Complete
[AGENT] ├─ ✓ SQL injection detected in user input
[AGENT] ├─ Tool: google_search
[AGENT] ├─ fix: Use parameterized queries
[AGENT] └─ confidence: 0.94 | 247ms | 156 tokens | model: gemini-2.5-flash-lite

Installation & Setup

JavaScript/TypeScript

npm install @console-agent/agent

Python

pip install console-agent

Get Free API Key

https://aistudio.google.com/apikey

Minimal Setup (Zero Config Works!)

// Just set environment variable
export GEMINI_API_KEY="your-key-here"

// Import and use
import '@console-agent/agent';
console.agent("analyze this", data);

Optional Configuration

import { init } from '@console-agent/agent';

init({
  apiKey: process.env.GEMINI_API_KEY,
  model: 'gemini-2.5-flash-lite',  // Default
  persona: 'general',
  mode: 'fire-and-forget',
  timeout: 10000,
  
  budget: {
    maxCallsPerDay: 100,
    maxTokensPerCall: 8000,
    costCapDaily: 1.00  // USD
  },
  
  anonymize: true,              // Auto-strip secrets/PII
  localOnly: false,             // Disable cloud tools
  includeCallerSource: true,    // Auto-read source file
  logLevel: 'info'
});

Python Configuration

from console_agent import init_agent, agent

init_agent(
    api_key=os.getenv('GEMINI_API_KEY'),
    model='gemini-2.5-flash-lite',
    persona='general',
    budget={'maxCallsPerDay': 100}
)

API Reference

console.agent(prompt, context?, options?)

Main API – call it like console.log().

// Simple fire-and-forget
console.agent("explain this error", error);

// Await structured results
const result = await console.agent("analyze", data, {
  persona: 'security',
  model: 'gemini-3-flash-preview',
  thinking: { level: 'high', includeThoughts: true },
  tools: ['google_search', 'code_execution']
});

Return Type: AgentResult

interface AgentResult {
  success: boolean;           // Overall task success
  summary: string;            // Human-readable conclusion
  reasoning?: string;         // Agent's thought process (if thinking enabled)
  data: Record<string, any>;  // Structured findings
  actions: string[];          // Tools used / steps taken
  confidence: number;         // 0-1 confidence score
  metadata: {
    model: string;
    tokensUsed: number;
    latencyMs: number;
    toolCalls: ToolCall[];
    cached: boolean;
  };
}

Persona Shortcuts

// Auto-selects security persona
console.agent.security("audit this query", sql);

// Auto-selects debugger persona
console.agent.debug("why is this slow?", metrics);

// Auto-selects architect persona
console.agent.architect("review API design", endpoint);

Built-in Tools

IMPORTANT: Tools are opt-in. Only activated when explicitly passed via tools: [...].

1. Google Search 🔍

Real-time web grounding – search for current info, CVEs, documentation.

const result = await console.agent(
  "What is the current population of Tokyo?",
  null,
  { tools: ['google_search'] }
);

2. Code Execution 💻

Python sandbox (Gemini-hosted) – calculations, data processing, algorithm verification.

const result = await console.agent(
  "Calculate the 20th Fibonacci number",
  null,
  { tools: ['code_execution'] }
);
// result.data.result → 6765

3. URL Context 🌐

Fetch and analyze web pages – read docs, analyze APIs, extract content.

const result = await console.agent(
  "Summarize this page",
  null,
  { tools: ['url_context'] }
);

Combining Multiple Tools

// Agent decides which tools to use based on prompt
const result = await console.agent(
  "Search for current world population, then calculate 1% of it",
  null,
  { tools: ['google_search', 'code_execution'] }
);
// 1. Uses google_search to find population
// 2. Uses code_execution to calculate 1%
// 3. Returns combined result

Common Use Cases

1. Security Auditing 🛡️

app.post('/api/search', async (req, res) => {
  const query = req.body.q;
  
  const audit = await console.agent.security(
    "check for SQL injection", 
    query
  );
  
  if (audit.data.severity === 'critical') {
    return res.status(400).json({ error: "Invalid input" });
  }
  
  const results = await db.search(query);
  res.json(results);
});

2. Debugging Failed Tests 🐛

import { agent } from '@console-agent/agent';
import { test, expect } from 'vitest';

test('payment processing', async () => {
  const result = await processPayment(order);
  
  if (!result.success) {
    await agent.debug("why did payment fail?", {
      order,
      result,
      testName: 'payment processing'
    });
  }
  
  expect(result.success).toBe(true);
});

Output:

Likely cause: Missing await on async fn
Suggested fix: Add 'await' on line 47
Confidence: 0.92 | 312ms | 189 tokens

3. Data Validation 📊

const records = await fetchBatch();

const validation = await console.agent(
  "validate batch meets schema", 
  records,
  { 
    schema: z.object({
      valid: z.boolean(),
      errors: z.array(z.string()),
      quality_score: z.number()
    })
  }
);

if (!validation.data.valid) {
  console.log("Issues:", validation.data.errors);
}

4. Architecture Review 🏗️

console.agent.architect("review API design", {
  endpoint: '/api/users',
  method: 'POST',
  handler: userController,
  middleware: [auth, rateLimit]
});

5. Performance Analysis

const startTime = Date.now();
const result = await slowOperation();
const duration = Date.now() - startTime;

if (duration > 1000) {
  agent.debug("why is this slow?", {
    operation: 'slowOperation',
    duration,
    input: operationInput
  });
}

6. Research with Tools 🔍

const research = await console.agent(
  "research known CVEs for lodash@4.17.20",
  null,
  { 
    tools: ['google_search'],
    persona: 'security'
  }
);
console.log(research.summary);

Advanced Features

Structured Output with Zod Schema

import { z } from 'zod';

const result = await console.agent(
  "analyze sentiment", 
  review,
  {
    schema: z.object({
      sentiment: z.enum(["positive", "negative", "neutral"]),
      score: z.number(),
      keywords: z.array(z.string())
    })
  }
);

result.data.sentiment;  // "positive" ✅ typed and validated

File Attachments

import { readFileSync } from 'fs';

const doc = await console.agent(
  "What does this document say?", 
  null,
  {
    files: [{
      data: readFileSync('./data/report.pdf'),
      mediaType: 'application/pdf',
      fileName: 'report.pdf'
    }]
  }
);

Thinking Mode (Extended Reasoning)

const result = await console.agent(
  "design optimal database schema for multi-tenant SaaS",
  requirements,
  {
    model: 'gemini-3-flash-preview',
    thinking: { 
      level: 'high',          // 'low' | 'medium' | 'high'
      includeThoughts: true   // Return reasoning summary
    }
  }
);

console.log(result.reasoning);  // Extended thought process

Best Practices

1. Provide Rich Context

// ❌ Too vague
agent("fix this");

// ✅ Specific with context
agent.debug("why does payment fail?", {
  error,
  order,
  user,
  timestamp,
  environment: process.env.NODE_ENV,
  recentLogs: logs.slice(-10)
});

2. Use Appropriate Personas

// Security tasks
agent.security("audit SQL query", query);

// Performance tasks
agent.debug("analyze slow response", { duration, query });

// Architecture tasks
agent.architect("review this pattern", codeStructure);

// General tasks (auto-detected)
agent("validate email format", email);

3. Handle Results Properly

// Fire-and-forget for logging/analysis
agent("log unusual event", eventData);

// Await for decisions that affect flow
const isValid = await agent("validate input", userInput);
if (!isValid.success) {
  throw new ValidationError(isValid.summary);
}

4. Enable Tools When Needed

// No tools - uses only LLM knowledge
agent("explain async/await");

// With search - gets current info
agent("latest React best practices", null, { 
  tools: ['google_search'] 
});

// With code execution - performs calculations
agent("optimize this algorithm", code, { 
  tools: ['code_execution'] 
});

5. Budget Monitoring

init({
  budget: {
    maxCallsPerDay: 50,
    costCapDaily: 0.50
  },
  onBudgetWarning: (usage) => {
    console.warn(`Budget: ${usage.calls}/50 calls used`);
  }
});

Available Models

Configuration Options Reference

Full InitOptions

interface InitOptions {
  // Core
  apiKey?: string;                      // Default: GEMINI_API_KEY env
  model?: 'gemini-2.5-flash-lite' | 'gemini-3-flash-preview';
  persona?: 'debugger' | 'security' | 'architect' | 'general';
  
  // Execution
  mode?: 'fire-and-forget' | 'blocking';
  timeout?: number;                     // Default: 10000ms
  
  // Budget
  budget?: {
    maxCallsPerDay?: number;            // Default: 100
    maxTokensPerCall?: number;          // Default: 8000
    costCapDaily?: number;              // Default: 1.00 USD
  };
  
  // Privacy
  anonymize?: boolean;                  // Default: true
  localOnly?: boolean;                  // Default: false
  
  // Output
  logLevel?: 'silent' | 'errors' | 'info' | 'debug';
  includeCallerSource?: boolean;        // Default: true
  
  // Advanced
  dryRun?: boolean;                     // Default: false
}

Per-Call Options

agent(prompt, context, {
  persona?: string;
  model?: string;
  verbose?: boolean;
  timeout?: number;
  tools?: Array<'google_search' | 'code_execution' | 'url_context'>;
  thinking?: {
    level?: 'low' | 'medium' | 'high';
    includeThoughts?: boolean;
  };
  schema?: ZodSchema;                   // For structured output
  files?: Array<{
    data: Buffer;
    mediaType: string;
    fileName: string;
  }>;
});

Privacy & Security

Auto-Anonymization

When anonymize: true (default), automatically strips:

• API keys and secrets
• Email addresses
• IP addresses
• AWS keys
• Private keys
• Database connection strings

Budget Controls

Hard limits prevent cost explosion:

• maxCallsPerDay: 100 calls/day default
• maxTokensPerCall: 8K tokens/call default
• costCapDaily: $1.00/day default

Rate Limiting

Token bucket algorithm spreads calls evenly across 24 hours with graceful degradation.

Troubleshooting

Issue: “No API key configured”

# Solution: Set environment variable
export GEMINI_API_KEY="your-key"

# Or configure explicitly
init({ apiKey: 'your-key' });

Issue: “Budget exceeded”

// Increase limits
init({
  budget: { 
    maxCallsPerDay: 200,
    costCapDaily: 2.00
  }
});

Issue: “Agent timeout”

// Increase timeout
agent("complex task", data, { timeout: 30000 });  // 30s

Issue: “Results not specific enough”

// ❌ Vague
agent("help");

// ✅ Specific with rich context
agent.debug("why does API return 500?", {
  endpoint: '/api/users',
  request: { method: 'POST', body },
  response: { status: 500, body: errorBody },
  logs: recentLogs,
  environment: process.env.NODE_ENV
});

Python-Specific Examples

Basic Usage

from console_agent import agent

# Fire-and-forget
agent("analyze this error", error)

# Blocking
result = agent("validate input", data)
if not result.valid:
    raise ValueError(result.reason)

With Pytest

import pytest
from console_agent import agent

def test_data_pipeline():
    result = process_batch(test_data)
    
    if result.errors:
        agent.debug("pipeline failure", {
            "input": test_data,
            "output": result,
            "errors": result.errors
        })
    
    assert len(result.errors) == 0

Persona Shortcuts

# Security persona
agent.security("audit SQL query", query)

# Debug persona
agent.debug("why is this slow?", metrics)

# Architect persona
agent.architect("review design", schema)

Implementation Tips for AI Assistants

1. Default to fire-and-forget unless user needs the result
2. Auto-suggest personas based on task context
3. Include rich context in examples – show what data to pass
4. Explain tools – they’re opt-in, not automatic
5. Show budget controls for production scenarios
6. Emphasize zero-config – just set GEMINI_API_KEY
7. Use in tests – highlight debugging failed tests use case
8. Reference official docs at https://console-agent.github.io

Key Differences from Other Agents

Official Links

• Homepage: https://console-agent.github.io
• API Reference: https://console-agent.github.io/reference
• npm: https://www.npmjs.com/package/@console-agent/agent
• PyPI: https://pypi.org/project/console-agent/
• GitHub (JS): https://github.com/console-agent/console_agent
• GitHub (Python): https://github.com/console-agent/console_agent_python
• Get API Key: https://aistudio.google.com/apikey

Version

This SKILL.md is for @console-agent/agent v1.2.0

License: MIT © Pavel