Customer Research
AnthropicOfficialPublished by Anthropic
- Skill
- Research and analysis
- Business and productivity
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The customer-research skill provides a structured methodology for gathering, synthesizing, and documenting information to resolve customer questions. It guides the agent through a tiered search process—ranging from official internal documentation to external web sources—and ensures that findings are presented with clear attribution and confidence levels. The skill also includes protocols for handling information gaps, resolving contradictory data, and determining when to escalate inquiries to subject matter experts.
What it does
- Synthesizes information from internal knowledge bases, CRM data, and external sources.
- Assigns confidence scores to research findings based on source authority.
- Provides a standardized template for research briefs and documentation.
- Offers guidance on when to escalate sensitive or complex inquiries.
- Suggests workflows for capturing new knowledge to improve future team efficiency.
- Handles contradictions by identifying authoritative sources and presenting multiple perspectives.
When to use it
- When a customer asks a question requiring factual verification.
- When investigating whether a reported bug has been documented previously.
- When reviewing account history to understand past communications.
- When gathering background information before drafting a customer response.
- When a topic requires synthesizing information from multiple internal or external sources.
Advantages
- Reduces duplicate research effort by promoting knowledge capture.
- Increases transparency by explicitly stating confidence levels and source attribution.
- Provides a clear escalation path for high-stakes or specialized topics.
- Standardizes the format of research outputs for internal consistency.
- Helps identify and resolve discrepancies between different information sources.
Usage examples
- “/customer-research Does our product support SSO with Okta?”
- The agent searches internal documentation and external integration guides to provide a definitive answer with source attribution and a confidence score.
- “/customer-research Has this bug been reported before? What's the known workaround?”
- The agent queries support tickets and internal issue trackers to identify previous reports and existing workarounds, summarizing the findings for the user.
- “/customer-research What did we tell Acme Corp last time they asked about this?”
- The agent reviews CRM notes and previous correspondence to provide context on the account's history regarding the specific topic.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/customer-research/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip customer-research.zip -d .agents/skills
Documentation by Anthropic
/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)
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
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Attribution and licence
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- Anthropic
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- Source
- anthropics/knowledge-work-plugins · customer-support/skills/customer-research · commit e050c2e
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from customer-support/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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