Customer Research

Customer Research is an official skill from Anthropic's Customer Support plugin in the public knowledge-work-plugins repository. Its direct primary source is the customer-support/skills/customer-research directory. The skill helps Claude investigate customer questions, product questions, previously reported issues, account history, and broader topics that support teams need to understand. It is a research and synthesis guide, not a CRM, ticketing system, knowledge base, or guarantee that a customer-facing answer is correct. This entry follows the official SKILL.md and Anthropic's official documentation about skills. The repository is released under Apache-2.0.

Purpose and request framing

According to the provider, useful research starts by clarifying what must actually be found rather than opening the first search result. The user should distinguish a factual customer question, an investigation of a reported issue, account context, and general topic research. The workflow also asks whether the request has a definitive answer, needs several perspectives, or is still exploratory. The audience matters as well: an internal support team, a customer, and an executive audience require different depth and wording. This framing prevents a large collection of documents from replacing the decision or answer the requester actually needs.

Systematic source search

The source ranks possible evidence by confidence. Internal product documentation, knowledge bases, runbooks, and policies are placed first. CRM notes, previous support resolutions, meeting notes, and organizational context follow. Team chat, email, and calendar notes can add useful clues, but they require caution because informal material may be incomplete or out of context. External sources come next, including official documentation, public knowledge bases, release notes, and third-party documentation. Inference, analogies, and general best practices are the weakest tier. The skill recommends searching systematically and cross-referencing rather than stopping after the first plausible result.

Synthesis and confidence

The result should be a research brief with a direct answer, a confidence level, key findings, context and nuance, sources, gaps, and recommended next steps. According to the provider, confidence must be stated explicitly. High confidence requires current authoritative documentation or several reliable sources that agree. Medium confidence can reflect one source or informal evidence that appears plausible but is not fully corroborated. Low confidence identifies inference, stale information, or contradictory evidence. When sources disagree, the contradiction should be named, the more authoritative or more recent source should be prioritized with an explanation, both perspectives should be presented where useful, and a customer-facing answer should use the cautious interpretation until the discrepancy is resolved.

Boundaries and escalation

Questions involving pricing, contracts, legal terms, security, privacy, product roadmaps, individual configurations, or specialized expertise need review by the appropriate billing, legal, security, product, engineering, or subject-matter team. A skill must not invent commitments, deadlines, revenue impact, customer attributes, or technical causes. Security and data-loss signals are governed by the organization's own response process. Connected sources provide data, but retrieved content is not automatically a trusted instruction to act. Before an external response, verify the claim, its freshness, and whether the information may be shared with the intended audience.

Privacy and professional use

Support and account material can contain personal information, internal correspondence, contract context, logs, or hostile instructions. Share only what is necessary, redact sensitive values, and do not store credentials, tokens, or secret sample data in the skill. A local skill file does not mean that a connected model runs locally; inputs and results may be transmitted to the selected model provider. Customer Research is useful for traceable preliminary research and knowledge capture, but it does not replace subject-matter review or support ownership. After research, the provider suggests capturing recurring findings in an approved knowledge base, FAQ, or runbook so future teams can avoid repeating the same investigation under uncontrolled conditions.