Terminal.skills
Skills/claude-hud
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claude-hud

Build heads-up display (HUD) dashboards for AI coding agents — show context usage, active tools, running sub-agents, and task progress in real-time. Use when: monitoring AI agent activity, building developer tools for AI-assisted coding, creating status dashboards for agent workflows.

#claude-code#hud#dashboard#devtools#monitoring
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed claude-hud v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Information

Version
1.0.0
Author
terminal-skills
Category
Development
License
MIT

Documentation

Overview

Build heads-up display dashboards that monitor AI coding agents in real-time. Track context window consumption, active tool calls, sub-agent status, task progress, and cost — all rendered in a terminal UI or web interface. Inspired by claude-hud (13k+ stars).

Instructions

Step 1: Understand the HUD Architecture

ComponentWhat It ShowsData Source
Context meterTokens used / remainingAgent API response headers
Tool trackerActive tool calls + historyHook into tool execution
Sub-agent panelSpawned agents + statusAgent orchestration layer
Task progressTodo items + completionParse agent task lists
Cost tracker$ spent this sessionToken count x model pricing

Step 2: Set Up the Project

bash
mkdir ai-hud && cd ai-hud
npm init -y
npm install blessed blessed-contrib chalk ws

Step 3: Build the Context Usage Monitor

javascript
// context-monitor.js
class ContextMonitor {
  constructor(maxTokens = 200000) {
    this.maxTokens = maxTokens;
    this.inputTokens = 0;
    this.outputTokens = 0;
    this.cacheHits = 0;
  }
  update(apiResponse) {
    const usage = apiResponse.usage || {};
    this.inputTokens = usage.input_tokens || 0;
    this.outputTokens = usage.output_tokens || 0;
    this.cacheHits = usage.cache_read_input_tokens || 0;
    return this.getStatus();
  }
  getStatus() {
    const total = this.inputTokens + this.outputTokens;
    const pct = ((total / this.maxTokens) * 100).toFixed(1);
    return {
      used: total, remaining: this.maxTokens - total,
      percentage: parseFloat(pct), cached: this.cacheHits,
      warning: parseFloat(pct) > 80 ? 'HIGH' : 'OK'
    };
  }
}

Step 4: Build the Tool Call Tracker

javascript
// tool-tracker.js
class ToolTracker {
  constructor() {
    this.active = [];
    this.history = [];
    this.counts = {};
  }
  onToolStart(toolName, input) {
    const call = {
      id: Date.now(), tool: toolName,
      input: JSON.stringify(input).slice(0, 100),
      startedAt: new Date(), status: 'running'
    };
    this.active.push(call);
    this.counts[toolName] = (this.counts[toolName] || 0) + 1;
    return call;
  }
  onToolEnd(callId, output) {
    const idx = this.active.findIndex(c => c.id === callId);
    if (idx !== -1) {
      const call = this.active.splice(idx, 1)[0];
      call.status = 'done';
      call.duration = Date.now() - call.startedAt;
      call.output = String(output).slice(0, 80);
      this.history.unshift(call);
      if (this.history.length > 50) this.history.pop();
    }
  }
  getTopTools(n = 5) {
    return Object.entries(this.counts).sort((a, b) => b[1] - a[1]).slice(0, n);
  }
}

Step 5: Build the Terminal Dashboard

javascript
// dashboard.js
const blessed = require('blessed');
const contrib = require('blessed-contrib');
const screen = blessed.screen({ smartCSR: true, title: 'AI Agent HUD' });
const grid = new contrib.grid({ rows: 12, cols: 12, screen });

const contextGauge = grid.set(0, 0, 3, 4, contrib.gauge, {
  label: ' Context Usage ', stroke: 'green', fill: 'white'
});
const toolLog = grid.set(0, 4, 6, 8, contrib.log, {
  label: ' Tool Calls ', fg: 'green', selectedFg: 'green'
});
const taskBar = grid.set(3, 0, 3, 4, contrib.bar, {
  label: ' Tasks ', barWidth: 6, maxHeight: 10
});
const costLine = grid.set(6, 0, 6, 6, contrib.line, {
  label: ' Cost ($) ', showLegend: true, minY: 0
});
const agentTable = grid.set(6, 6, 6, 6, contrib.table, {
  label: ' Sub-Agents ', keys: true, columnWidth: [20, 10, 15]
});

function refresh(state) {
  contextGauge.setPercent(state.context.percentage);
  state.tools.active.forEach(t => toolLog.log(`> ${t.tool} - ${t.input}`));
  screen.render();
}
screen.key(['escape', 'q', 'C-c'], () => process.exit(0));
screen.render();

Step 6: Connect via WebSocket

javascript
// server.js
const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8765 });

wss.on('connection', (ws) => {
  console.log('HUD client connected');
  ws.on('message', (data) => {
    const event = JSON.parse(data);
    switch (event.type) {
      case 'context_update': contextMonitor.update(event.data); break;
      case 'tool_start': toolTracker.onToolStart(event.tool, event.input); break;
      case 'tool_end': toolTracker.onToolEnd(event.id, event.output); break;
      case 'task_update': taskTracker.update(event.tasks); break;
    }
    broadcastState();
  });
});

Step 7: Add Cost Tracking

javascript
const PRICING = {
  'claude-sonnet-4-20250514': { input: 3.0, output: 15.0 },
  'claude-opus-4-20250514': { input: 15.0, output: 75.0 },
  'gpt-4o': { input: 2.5, output: 10.0 },
};

function calculateCost(model, inputTokens, outputTokens) {
  const p = PRICING[model] || PRICING['claude-sonnet-4-20250514'];
  return ((inputTokens * p.input + outputTokens * p.output) / 1_000_000).toFixed(4);
}

Examples

Example 1: Monitor a Claude Code Refactoring Session

A developer launches the HUD while Claude Code refactors a large codebase:

javascript
const monitor = new ContextMonitor(200000); // Claude Sonnet 200k context
const tracker = new ToolTracker();

// Simulated events from a real refactoring session
monitor.update({ usage: { input_tokens: 45200, output_tokens: 12800, cache_read_input_tokens: 31000 } });
console.log(monitor.getStatus());
// { used: 58000, remaining: 142000, percentage: 29.0, cached: 31000, warning: 'OK' }

tracker.onToolStart('Read', { file_path: '/src/components/Dashboard.tsx' });
tracker.onToolStart('Grep', { pattern: 'useState', path: '/src' });
tracker.onToolEnd(tracker.active[0].id, '245 lines read');
console.log(tracker.getTopTools());
// [['Read', 12], ['Grep', 8], ['Edit', 6], ['Bash', 3]]
// Dashboard shows: context at 29%, 2 active tools, $0.0234 session cost

Example 2: Multi-Agent Workflow Dashboard

A team runs 3 agents in parallel and monitors all of them on one HUD:

javascript
const agents = {
  'agent-1-backend': new ContextMonitor(200000),
  'agent-2-frontend': new ContextMonitor(200000),
  'agent-3-tests': new ContextMonitor(200000),
};

// Agent 1: refactoring API routes — 67% context used
agents['agent-1-backend'].update({ usage: { input_tokens: 98000, output_tokens: 36000 } });
// Agent 2: building React components — 23% context used
agents['agent-2-frontend'].update({ usage: { input_tokens: 32000, output_tokens: 14000 } });
// Agent 3: writing test suites — 45% context used
agents['agent-3-tests'].update({ usage: { input_tokens: 61000, output_tokens: 29000 } });

// Dashboard renders 3 gauges side-by-side:
// [agent-1: 67% HIGH] [agent-2: 23% OK] [agent-3: 45% OK]
// Total session cost: $0.0234 + $0.0108 + $0.0179 = $0.0521

Guidelines

  • Keep the HUD lightweight — avoid heavy polling; use WebSocket push for real-time updates
  • Set context alerts at 80% — warn developers before hitting the context window limit
  • Log all events to disk — enable session replay for debugging and optimization
  • Support multiple agents — design the dashboard to handle parallel agent workflows
  • Customize per workflow — different tasks benefit from different widget layouts
  • Respect privacy — do not log sensitive code content in tool call history; truncate inputs

References