Agent-Skills-for-Context-Engineering

🥇Gold

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.

14,8076220Updated 4d ago

Intermediate30min to implementautomation

Saves ~75 min per use

Quick InstallView Source

git clone https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering.git

Works with:

Claude

Overview

About This Skill

https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering

How to Use

1. **Define Your Use Case:** Start by specifying the [TASK] your multi-agent system will perform (e.g., customer support, code review, data analysis). Clearly outline the [NUMBER] of agents needed and their roles (e.g., Intake, Resolution, Escalation). 2. **Select Your Framework:** Choose an agent orchestration framework like LangGraph, CrewAI, or AutoGen. Ensure it supports context-sharing mechanisms (e.g., shared memory, Redis, or a message queue). 3. **Design Context Protocols:** Define the structure of shared context (e.g., JSON schema) and interaction protocols (e.g., turn-taking, error handling). Use tools like Mermaid.js to diagram agent workflows before implementation. 4. **Implement and Test:** Deploy agents incrementally, starting with the simplest (e.g., Intake Agent). Use monitoring tools (Prometheus, Grafana) to track metrics like resolution time, accuracy, and escalation rates. Validate performance against human benchmarks. 5. **Optimize and Scale:** Iterate based on feedback from the Feedback Agent or human reviewers. Scale the system by adding more agents or refining context-sharing strategies (e.g., caching frequent queries).

Use Cases

Optimize agent performance by applying context compression techniques.

Diagnose and resolve context degradation issues in multi-agent systems.

Design and implement effective memory architectures for long-running agent sessions.

Evaluate agent outputs using advanced evaluation frameworks to ensure quality.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Design a multi-agent system for [TASK] using [NUMBER] specialized agents. Each agent should have a clear role: [ROLE_1], [ROLE_2], [ROLE_3], etc. Define their interaction protocols, context-sharing mechanisms, and error-handling strategies. Provide a step-by-step execution plan for testing and deploying the system, including metrics to validate performance. Use [FRAMEWORK] for implementation and [TOOLS] for monitoring.

Example Output

### Multi-Agent System Design for Automated Customer Support

**Agents & Roles:**
1. **Intake Agent** (Role: Triage) – Uses a lightweight LLM to classify incoming support tickets by urgency (P0-P3) and topic (billing, technical, feature request). Context shared: ticket metadata, customer history, and initial classification confidence scores.
2. **Resolution Agent** (Role: Specialist) – Handles P1/P2 technical issues using a fine-tuned model trained on internal documentation and past resolutions. Context shared: ticket details, previous interactions, and resolution templates.
3. **Escalation Agent** (Role: Human Handoff) – Manages P0 issues or unresolved P1/P2 cases by routing to the appropriate team with a summarized context packet (e.g., error logs, customer sentiment, attempted fixes). Context shared: escalation reason, customer SLA status, and agent notes.
4. **Feedback Agent** (Role: Continuous Improvement) – Monitors agent performance metrics (resolution time, customer satisfaction, escalation rate) and suggests refinements to the Intake Agent’s classification model or Resolution Agent’s response templates.

**Interaction Protocols:**
- **Context Sharing:** Agents use a shared JSON structure with fields like `ticket_id`, `customer_id`, `context_timestamp`, `confidence_scores`, and `escalation_reason`. The Intake Agent writes to this structure first, and subsequent agents append updates.
- **Error Handling:** If the Resolution Agent fails to resolve a ticket within 15 minutes, it automatically triggers the Escalation Agent with a `timeout` flag. The Feedback Agent logs all failures and triggers a review if the escalation rate exceeds 5% for a given agent.
- **Performance Metrics:** Track resolution time (avg. 8.2 minutes for P2 tickets), customer satisfaction (NPS of +45), and escalation rate (3.1% monthly average).

**Execution Plan:**
1. **Week 1:** Deploy the Intake Agent in a sandbox environment with 100 historical tickets for classification testing. Use Prometheus for monitoring.
2. **Week 2:** Integrate the Resolution Agent with a subset of tickets (P2 technical issues) and validate response accuracy against human agent benchmarks.
3. **Week 3:** Roll out the Escalation Agent to handle P0 tickets, with the Feedback Agent logging all interactions for model retraining.
4. **Week 4:** Full A/B test with 50% of tickets routed to the multi-agent system vs. the legacy human-only workflow. Measure impact on resolution time and customer satisfaction.

**Tools:**
- **Framework:** LangGraph for agent orchestration.
- **Monitoring:** Prometheus (metrics) + Grafana (dashboards) + Weights & Biases (model performance tracking).
- **Context Storage:** Redis for low-latency shared context updates.

**Validation:**
After 4 weeks, the system reduced average resolution time by 37% (from 13.1 to 8.2 minutes) and improved NPS by 12 points. The escalation rate stabilized at 3.1%, meeting the target threshold.