Customer Researcher
Customer support skill, available on Zeplik
Customer Researcher is a ready-to-run customer support skill on Zeplik. Look something up before replying to a customer: has this bug been reported, what did we tell this account, do we support this integration -- across KB, docs and account history. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The Customer Researcher skill loads automatically when your request matches it, or you can invoke it directly by typing /customer-research in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Customer Researcher skill can do
- Search across knowledge base, CRM, chat, email and web for an answer
- Cross-reference multiple sources instead of stopping at first result
- Produce a structured research brief with confidence scoring and sources
- Flag gaps, roadmap or legal sensitivities, and suggest knowledge capture
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How the Customer Researcher skill works
/customer-research
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Multi-source research on a customer question, product topic, or account-related inquiry. Synthesizes findings from all available sources with clear attribution and confidence scoring.
Usage
/customer-research <question or topic>
Workflow
1. Parse the Research Request
Identify what type of research is needed:
- Customer question: Something a customer has asked that needs an answer (e.g., "Does our product support SSO with Okta?")
- Issue investigation: Background on a reported problem (e.g., "Has this bug been reported before? What's the known workaround?")
- Account context: History with a specific customer (e.g., "What did we tell Acme Corp last time they asked about this?")
- Topic research: General topic relevant to support work (e.g., "Best practices for webhook retry logic")
Before searching, clarify what you're actually trying to find:
- Is this a factual question with a definitive answer?
- Is this a contextual question requiring multiple perspectives?
- Is this an exploratory question where the scope is still being defined?
- Who is the audience for the answer (internal team, customer, leadership)?
2. Search Available Sources
Search systematically through the source tiers below, adapting to what is connected. Don't stop at the first result — cross-reference across sources.
Tier 1 — Official Internal Sources (highest confidence):
- ~~knowledge base (if connected): product docs, runbooks, FAQs, policy documents
- ~~cloud storage: internal documents, specs, guides, past research
- Product roadmap (internal-facing): feature timelines, priorities
Tier 2 — Organizational Context:
- ~~CRM notes: account notes, activity history, previous answers, opportunity details
- ~~support platform (if connected): previous resolutions, known issues, workarounds
- Meeting notes: previous discussions, decisions, commitments
Tier 3 — Team Communications:
- ~~chat: search for the topic in relevant channels; check if teammates have discussed or answered this before
- ~~email: search for previous correspondence on this topic
- Calendar notes: meeting agendas and post-meeting notes
Tier 4 — External Sources:
- Web search: official documentation, blog posts, community forums
- Public knowledge bases, help centers, release notes
- Third-party documentation: integration partners, complementary tools
Tier 5 — Inferred or Analogical (use when direct sources don't yield answers):
- Similar situations: how similar questions were handled before
- Analogous customers: what worked for comparable accounts
- General best practices: industry standards and norms
3. Synthesize Findings
Compile results into a structured research brief:
## Research: [Question/Topic]
### Answer
[Clear, direct answer to the question — lead with the bottom line]
**Confidence:** [High / Medium / Low]
[Explain what drives the confidence level]
### Key Findings
**From [Source 1]:**
- [Finding with specific detail]
- [Finding with specific detail]
**From [Source 2]:**
- [Finding with specific detail]
### Context & Nuance
[Any caveats, edge cases, or additional context that matters]
### Sources
1. [Source name/link] — [what it contributed]
2. [Source name/link] — [what it contributed]
3. [Source name/link] — [what it contributed]
### Gaps & Unknowns
- [What couldn't be confirmed]
- [What might need verification from a subject matter expert]
### Recommended Next Steps
- [Action if the answer needs to go to a customer]
- [Action if further research is needed]
- [Who to consult for verification if needed]
4. Handle Insufficient Sources
If no connected sources yield results:
- Perform web research on the topic
- Ask the user for internal context:
- "I couldn't find this in connected sources. Do you have internal docs or knowledge base articles about this?"
- "Has your team discussed this topic before? Any ~~chat channels I should check?"
- "Is there a subject matter expert who would know the answer?"
- Be transparent about limitations:
- "This answer is based on web research only — please verify against your internal documentation before sharing with the customer."
- "I found a possible answer but couldn't confirm it from an authoritative internal source."
5. Customer-Facing Considerations
If the research is to answer a customer question:
- Flag if the answer involves product roadmap, pricing, legal, or security topics that may need review
- Note if the answer differs from what may have been communicated previously
- Suggest appropriate caveats for the customer-facing response
- Offer to draft the customer response: "Want me to draft a response to the customer based on these findings?"
6. Knowledge Capture
After research is complete, suggest capturing the knowledge:
- "Should I save these findings to your knowledge base for future reference?"
- "Want me to create a FAQ entry based on this research?"
- "This might be worth documenting — should I draft a runbook entry?"
This helps build institutional knowledge and reduces duplicate research effort across the team.
Source Prioritization and Confidence
Confidence by Source Tier
| Tier | Source Type | Confidence | Notes |
|---|---|---|---|
| 1 | Official internal docs, KB, policies | High | Trust unless clearly outdated — check dates |
| 2 | CRM, support tickets, meeting notes | Medium-High | May be subjective or incomplete |
| 3 | Chat, email, calendar notes | Medium | Informal, may be out of context or speculative |
| 4 | Web, forums, third-party docs | Low-Medium | May not reflect your specific situation |
| 5 | Inference, analogies, best practices | Low | Clearly flag as inference, not fact |
Confidence Levels
Always assign and communicate a confidence level:
High Confidence:
- Answer confirmed by official documentation or authoritative source
- Multiple sources corroborate the same answer
- Information is current (verified within a reasonable timeframe)
- "I'm confident this is accurate based on [source]."
Medium Confidence:
- Answer found in informal sources (chat, email) but not official docs
- Single source without corroboration
- Information may be slightly outdated but likely still valid
- "Based on [source], this appears to be the case, but I'd recommend confirming with [team/person]."
Low Confidence:
- Answer is inferred from related information
- Sources are outdated or potentially unreliable
- Contradictory information found across sources
- "I wasn't able to find a definitive answer. Based on [context], my best assessment is [answer], but this should be verified before sharing with the customer."
Unable to Determine:
- No relevant information found in any source
- Question requires specialized knowledge not available in sources
- "I couldn't find information about this. I recommend reaching out to [suggested expert/team] for a definitive answer."
Handling Contradictions
When sources disagree:
- Note the contradiction explicitly
- Identify which source is more authoritative or more recent
- Present both perspectives with context
- Recommend how to resolve the discrepancy
- If going to a customer: use the most conservative/cautious answer until resolved
When to Escalate vs. Answer Directly
Answer Directly When:
- Official documentation clearly addresses the question
- Multiple reliable sources corroborate the answer
- The question is factual and non-sensitive
- The answer doesn't involve commitments, timelines, or pricing
- You've answered similar questions before with confirmed accuracy
Escalate or Verify When:
- The answer involves product roadmap commitments or timelines
- Pricing, legal terms, or contract-specific questions
- Security, compliance, or data handling questions
- The answer could set a precedent or create expectations
- You found contradictory information in sources
- The question involves a specific customer's custom configuration
- The answer requires specialized expertise you don't have
- The customer is at risk and the wrong answer could exacerbate the situation
Escalation Path:
- Subject matter expert: For technical or domain-specific questions
- Product team: For roadmap, feature, or capability questions
- Legal/compliance: For terms, privacy, security, or regulatory questions
- Billing/finance: For pricing, invoice, or payment-related questions
- Engineering: For custom configurations, bugs, or technical root causes
- Leadership: For strategic decisions, exceptions, or high-stakes situations
Research Documentation for Team Knowledge Base
After completing research, capture the knowledge for future use.
When to Document:
- Question has come up before or likely will again
- Research took significant effort to compile
- Answer required synthesizing multiple sources
- Answer corrects a common misunderstanding
- Answer involves nuance that's easy to get wrong
Documentation Format:
## [Question/Topic]
**Last Verified:** [date]
**Confidence:** [level]
### Answer
[Clear, direct answer]
### Details
[Supporting detail, context, and nuance]
### Sources
[Where this information came from]
### Related Questions
[Other questions this might help answer]
### Review Notes
[When to re-verify, what might change this answer]
Knowledge Base Hygiene:
- Date-stamp all entries
- Flag entries that reference specific product versions or features
- Review and update entries quarterly
- Archive entries that are no longer relevant
- Tag entries for searchability (by topic, product area, customer segment)
Zeplik output presentation
Present the final deliverable as a single polished artifact: clear headings, tables where the content is tabular, fenced code where it is code. Lead with the deliverable itself; keep process commentary to a single short line. If the skill produced multiple files or sections, end with a compact list of them with one-line purposes.
How to use the Customer Researcher skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Customer Researcher skill right away.
Describe your customer support task
Ask in plain language, or type /customer-research to invoke the skill directly. Zeplik recognizes the Customer Researcher skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- Anthropic
- License
- Apache-2.0
Adapted from the open-source anthropics/knowledge-work-plugins project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Customer Researcher skill?
- Customer Researcher is a ready-to-run customer support skill on Zeplik. Look something up before replying to a customer: has this bug been reported, what did we tell this account, do we support this integration -- across KB, docs and account history. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use Customer Researcher on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /customer-research in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Customer Researcher skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Customer Researcher skill runs on your preferred model for the task.
- Where does the Customer Researcher skill come from?
- The Customer Researcher skill is adapted from the open-source anthropics/knowledge-work-plugins project (Apache-2.0) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Customer Researcher skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related customer support skills
- Complaint ResolutionEnd-to-end complaint handling -- pull customer history, draft a tone-matched reply for approval, and flag whether it is a pattern needing an operational fix.
- Customer Feedback PulseSynthesize reviews, tickets, disputes and complaint emails into themes with verbatim quotes, plus a top-3 fixes list and drafted replies.
- Escalation HandlerPackage a support issue into an escalation brief -- context, reproduction steps, business impact and the right target in engineering, product or leadership.
- Help Center Article WriterWrite a publish-ready knowledge base or help-center article from a resolved issue, recurring question or workaround, structured for search and self-service.
- Support Reply DrafterDraft a customer-facing support reply tuned to the situation, relationship and channel -- questions, escalations, bad news, billing and declined requests.
- Ticket TriageTriage an incoming support ticket -- categorize it, assign a P1-P4 priority, check for duplicates and known issues, route it and suggest a first reply.
More on Zeplik
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