Live Chat: Live chat implementation enables real-time communication between users and support agents, providing instant customer support and improving user satisfaction. Effective live chat systems handle real-t
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
npx agent-skills-cli listSkill Instructions
id: SKL-live-LIVECHAT name: Live Chat description: Live chat implementation enables real-time communication between users and support agents, providing instant customer support and improving user satisfaction. Effective live chat systems handle real-t version: 1.0.0 status: active owner: '@cerebra-team' last_updated: '2026-02-22' category: Backend tags:
- api
- backend
- server
- database stack:
- Python
- Node.js
- REST API
- GraphQL difficulty: Intermediate
Live Chat
Skill Profile
(Select at least one profile to enable specific modules)
- DevOps
- Backend
- Frontend
- AI-RAG
- Security Critical
Overview
Live chat implementation enables real-time communication between users and support agents, providing instant customer support and improving user satisfaction. Effective live chat systems handle real-time messaging, typing indicators, file sharing, chat history, and agent assignment.
Why This Matters
- <Benefit>: <short explanation>
- <Benefit>: <short explanation>
- <Benefit>: <short explanation>
Core Concepts
Inputs / Outputs / Contracts
- Inputs:
- <e.g., env vars, request payload, file paths, schema>
- Entry Conditions:
- <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
- Outputs:
- <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
- Artifacts Required (Deliverables):
- <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
- Acceptance Evidence:
- <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
- Success Criteria:
- <e.g., p95 < 300ms, coverage ≥ 80%>
Skill Composition
- Depends on: None
- Compatible with: None
- Conflicts with: None
- Related Skills: None
Quick Start
Assumptions / Constraints / Non-goals
- Assumptions:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
- Constraints:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
- Non-goals:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training
Compatibility & Prerequisites
- Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
- Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
- Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
- Environment Setup:
.env.examplekeys:API_KEY,DATABASE_URL(no values)
Test Scenario Matrix (QA Strategy)
| Type | Focus Area | Required Scenarios / Mocks |
|---|---|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
Technical Guardrails & Security Threat Model
1. Security & Privacy (Threat Model)
- Top Threats: Injection attacks, authentication bypass, data exposure
- Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
- Authorization: Validate user permissions before state changes
2. Performance & Resources
- Execution Efficiency: Consider time complexity for algorithms
- Memory Management: Use streams/pagination for large data
- Resource Cleanup: Close DB connections/file handlers in finally blocks
3. Architecture & Scalability
- Design Pattern: Follow SOLID principles, use Dependency Injection
- Modularity: Decouple logic from UI/Frameworks
4. Observability & Reliability
- Logging Standards: Structured JSON, include trace IDs
request_id - Metrics: Track
error_rate,latency,queue_depth - Error Handling: Standardized error codes, no bare except
- Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%
Agent Directives & Error Recovery
(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)
- Thinking Process: Analyze root cause before fixing. Do not brute-force.
- Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- Self-Review: Check against Guardrails & Anti-patterns before finalizing.
- Output Constraints: Output ONLY the modified code block. Do not explain unless asked.
Definition of Done (DoD) Checklist
- Tests passed + coverage met
- Lint/Typecheck passed
- Logging/Metrics/Trace implemented
- Security checks passed
- Documentation/Changelog updated
- Accessibility/Performance requirements met (if frontend)
Anti-patterns
Reference Links & Examples
- Internal documentation and examples
- Official documentation and best practices
- Community resources and discussions
Versioning & Changelog
- Version: 1.0.0
- Changelog:
- 2026-02-22: Initial version with complete template structure
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