customer-research

Name: customer-research
Availability: InStock
Author: nmoralescyber

Automate structured customer research with source attribution and confidence tagging.

Updated Apr 25, 2026

Search Tags:#escalation #source-attribution #customer-research #research-report #confidence-levels #crm-tickets #internal-external-sources

Ready to run

npx skills add https://github.com/nmoralescyber/claude-skill-optimization --skill customer-research-nmoralescyber

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/nmoralescyber/claude-skill-optimization/tree/main/skills/customer-support/customer-research
Command: npx skills add https://github.com/nmoralescyber/claude-skill-optimization --skill customer-research-nmoralescyber

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Multi-source research with source attribution and confidence levels for existing customer questions. It provides a disciplined, traceable report that can feed downstream actions such as drafted responses, escalations, or formal decisions.

Core Features & Use Cases

Multi-source data fusion from internal sources (tickets, CRM, email, Slack, docs, engineering tickets) and external sources (website, news, public filings) with citations and confidence tagging.
Decision-path awareness supporting quick lookup, standard research, and deep package with tiered depth.
Structured outputs designed for downstream workflows: draft responses, escalation packets, or compliance reviews.

Quick Start

Ask the skill to generate a research package for a named customer and topic to produce a fully attributed report.

Frequently Asked Questions about customer-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compile customer research from internal tickets and external sources with citations?▼

You can compile customer research by combining internal records like CRM tickets, email, and Slack logs with external sources like public filings, tagging each data point with source attribution and confidence levels like [Confirmed] or [Likely].

What is the best way to structure an escalation packet from multi-source customer data?▼

To structure an escalation packet, generate a tiered research report applying confidence tagging across internal and external sources, producing a traceable output ready for downstream formal decisions and compliance reviews.

Can I use CRM tickets and Slack logs to generate a research report for existing customers?▼

Yes, you can use CRM tickets, Slack logs, engineering tickets, and email as internal inputs to generate a structured research report for existing customers, ensuring all findings include proper source attribution.

How do I tag confidence levels in a customer research report?▼

Confidence levels are tagged using [Confirmed], [Likely], and [Unconfirmed] markers applied directly to findings, ensuring downstream teams understand the reliability of the fused internal and external source data.

Does structured customer research support quick lookup or only deep package investigations?▼

Structured customer research supports tiered decision paths including quick lookup, standard research, and deep package investigations, allowing you to scale the depth of source attribution and data fusion based on the need.

Why do I need source attribution for downstream actions like drafted responses?▼

Source attribution is needed for drafted responses because it provides a disciplined, traceable report backing up findings with internal and external evidence, ensuring accountability during escalations or compliance reviews.