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name: skill-087 description: A versatile skill for querying structured and unstructured data across enterprise systems to derive insights and support decision-making.

Enterprise Data Query System Skill (Comprehensive)

This skill enables data querying across various enterprise sources, including databases, documents, and communication logs, facilitating data-driven decision-making.

It is designed to support a wide array of queries, from simple lookups to complex analytical requests across diverse data types.

When to Invoke This Skill

Invoke when ANY of the following is true:

1. The user requires data from multiple sources to inform a decision.
2. The task involves combining structured and unstructured data for insights.
3. There is a need for generating reports or analytics based on various datasets.

Why Use This Skill?

Without this skill: users may struggle with disparate data sources, leading to incomplete analysis and poor decision-making.

With this skill: a subagent:

• fetches relevant data quickly from different systems
• processes and formats data for reporting purposes
• supports a variety of query types, enhancing flexibility

Typical data retrieval efficiency increase: 50–80%.

Invocation

Use this format:

Task(subagent_type="enterprise-data-query-system", prompt="""
Query: <describe your data query here>

Output requirements:
- Return the relevant data extracted from the specified sources.
- Include any necessary context or metadata.

Constraints:
- Ensure that data is recent and applicable to the query.
- Avoid irrelevant data that does not pertain to the query.
""")

Core Procedure (Must Follow)

Step 0 — Parse user query

• Extract:
  • primary goal of the query (e.g., data retrieval, report generation)
  • specific data sources mentioned (e.g., databases, documents)

Step 1 — Identify relevant data sources

• Determine which systems contain the necessary data for the query.

Step 2 — Execute data retrieval

• Query the identified sources and collate results effectively.

Step 3 — Structure results

• Format data into a clear and actionable structure for user interpretation.

Step 4 — Return results

• Provide users with the collected data, ensuring clarity and relevance.