| 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:
- The user requires data from multiple sources to inform a decision.
- The task involves combining structured and unstructured data for insights.
- 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.
