open-agent-skills

🥈Silver

We are dedicated to building a set of open agent skills that deliver superior performance, higher determinism, and greater consistency on targeted tasks, while operating at a lower cost and with reduced context usage.

14540Updated 4d ago

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/MassLab-SII/open-agent-skills.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Define the task and constraints","action":"Replace [TARGET_TASK], [CONTEXT_WINDOW], [PERFORMANCE_METRIC], [OUTPUT_FORMAT], and [TASK_DESCRIPTION] with your specific requirements. For example, use '[TARGET_TASK] = customer support ticket triage' or '[PERFORMANCE_METRIC] = 99% categorization accuracy'.","tip":"Start with a narrow scope (e.g., 'categorize tickets by sentiment') before expanding to multi-step workflows."},{"step":"Provide structured context","action":"Format your input data as a JSON object with clear fields (e.g., {'tickets': [{'id': 'TKT-123', 'text': '...', 'metadata': {...}}]}). Use tools like Python scripts or Excel macros to pre-process raw data into this format.","tip":"Limit context to essential fields to stay within the [CONTEXT_WINDOW]. Remove redundant or noisy data (e.g., stopwords, irrelevant metadata)."},{"step":"Validate the output","action":"Compare the AI's output against a small sample of manually labeled data. Use metrics like precision/recall for classification tasks or edit distance for text generation. Adjust the prompt or context if discrepancies exceed 5%.","tip":"For high-stakes tasks, run a pilot with 100-200 samples before full deployment."},{"step":"Iterate for optimization","action":"If the determinism score is below 0.95, refine the prompt by adding explicit rules (e.g., 'Classify tickets as 'billing' only if the word 'invoice' appears in the text'). For cost reduction, experiment with smaller context windows or model variants.","tip":"Use tools like LangSmith or Weights & Biases to track performance metrics over time."},{"step":"Deploy and monitor","action":"Integrate the skill into your workflow using APIs or automation tools (e.g., Zapier, Make). Set up alerts for context overflow or determinism drops. Schedule weekly reviews to update the skill based on new data patterns.","tip":"Start with a 5% rollout to production and gradually increase as confidence grows."}]

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/MassLab-SII/open-agent-skills

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

Act as an [OPEN_AGENT_SKILL] specialized in [TARGET_TASK]. Follow these strict guidelines: 1) Use only the provided [CONTEXT_WINDOW] to ensure consistency. 2) Prioritize deterministic outputs—avoid creative variations unless explicitly requested. 3) Optimize for [PERFORMANCE_METRIC] (e.g., speed, accuracy, cost). 4) Return structured output in [OUTPUT_FORMAT]. Execute the following task: [TASK_DESCRIPTION].

Example Output

```json
{
  "task": "Process 500 customer support tickets for sentiment analysis and categorization",
  "context_window": "1000 tokens (ticket text + metadata)",
  "performance_metric": "95% accuracy with <2s processing per ticket",
  "output": {
    "sentiment_analysis": {
      "positive": 120,
      "neutral": 280,
      "negative": 100
    },
    "categories": {
      "billing": 150,
      "technical_support": 180,
      "product_feedback": 70,
      "general_inquiry": 100
    },
    "escalation_flags": [
      {"ticket_id": "TKT-452", "reason": "Negative sentiment + billing category"},
      {"ticket_id": "TKT-189", "reason": "Technical issue with no resolution in metadata"}
    ]
  },
  "determinism_score": 0.98,
  "context_usage": "987/1000 tokens used"
}
```

**Key Observations:**
- Negative sentiment tickets (100) are 20% higher than the previous week, indicating a potential issue with billing or product performance.
- The 'technical_support' category (180 tickets) suggests a need for improved self-service documentation.
- Two tickets flagged for escalation require immediate human review due to high negative sentiment combined with unresolved issues.

**Recommendations:**
1. Trigger a follow-up email campaign for customers with negative sentiment tickets to address concerns proactively.
2. Update the knowledge base with solutions for the top 3 technical support issues identified in this batch.
3. Allocate additional resources to the billing team during peak hours (10 AM - 2 PM) to reduce ticket volume.