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Agent skill

Customer Research

by w95 in w95/awesome-claude-corporate-skills

Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer.

MITAuto-check passedKnowledge Management

Install Customer Research

skills CLI
$ npx skills add w95/awesome-claude-corporate-skills --skill customer-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install w95/awesome-claude-corporate-skills customer-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/11-customer-success/customer-research .claude/skills/customer-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
customer-research
GitHub stars
237
Token cost
~2.3k tokens
SKILL.md length
1,003 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
MIT

At a glance

Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer.

  • Works in 5 steps: Note the contradiction explicitly → Identify which source is more… → Present both perspectives with context → …
  • A customer asks a question you need to investigate
  • SKILL.md covers Multi-Source Research…, Source Prioritization, Answer Synthesis and When to Escalate vs. Answer…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Customer Research is an agent skill from w95/awesome-claude-corporate-skills. Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Market research, Knowledge bases and Customer success. The repository describes itself as: 166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer…. The licence is MIT.

When your agent uses it

  • A customer asks a question you need to investigate
  • Building background on a customer situation
  • You need account context

Example prompts

  • “/customer-research”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Note the contradiction explicitly
  2. Identify which source is more authoritative or more recent
  3. Present both perspectives with context
  4. Recommend how to resolve the discrepancy
  5. If going to a customer: use the most conservative/cautious answer until resolved

What it can do on your machine

Read from SKILL.md and the folder at commit 78dbc7c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Customer Research loads about 2.3k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,003 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from w95/awesome-claude-corporate-skills at commit 78dbc7c, republished under its MIT licence (© w95). 1,003 words, ~2,253 tokens.

Download SKILL.mdSave it as .claude/skills/customer-research/SKILL.md (or your agent's skills folder).
name
customer-research
description
Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.

Customer Research Skill

You are an expert at conducting multi-source research to answer customer questions, investigate account contexts, and build comprehensive understanding of customer situations. You prioritize authoritative sources, synthesize across inputs, and clearly communicate confidence levels.

Multi-Source Research Methodology

Research Process

Step 1: Understand the Question 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)?

Step 2: Plan Your Search Strategy Map the question to likely source types:

  • Product capability question → documentation, knowledge base, product specs
  • Customer context question → CRM, email history, meeting notes, chat
  • Process/policy question → internal wikis, runbooks, policy docs
  • Technical question → documentation, engineering resources, support tickets
  • Market/competitive question → web research, analyst reports, competitive intel

Step 3: Execute Searches Systematically Search sources in priority order (see below). Don't stop at the first result — cross-reference across sources.

Step 4: Synthesize and Validate Combine findings, check for contradictions, and assess overall confidence.

Step 5: Present with Attribution Always cite sources and note confidence level.

Source Prioritization

Search sources in this order, with decreasing authority:

Tier 1 — Official Internal Sources (Highest Confidence)

These are authoritative and should be trusted unless outdated.

  • Product documentation: Official docs, specs, API references
  • Knowledge base / wiki: Internal articles, runbooks, FAQs
  • Policy documents: Official policies, terms, SLAs
  • Product roadmap (internal-facing): Feature timelines, priorities

Confidence level: High (unless clearly outdated — check dates)

Tier 2 — Organizational Context

These provide context but may reflect one perspective.

  • CRM records: Account notes, activity history, opportunity details
  • Support tickets: Previous resolutions, known issues, workarounds
  • Internal documents (Drive, shared folders): Specs, plans, analyses
  • Meeting notes: Previous discussions, decisions, commitments

Confidence level: Medium-High (may be subjective or incomplete)

Tier 3 — Team Communications

Informal but often contain the most recent information.

  • Chat history: Team discussions, quick answers, context
  • Email threads: Customer correspondence, internal discussions
  • Calendar notes: Meeting agendas and post-meeting notes

Confidence level: Medium (informal, may be out of context, could be speculative)

Tier 4 — External Sources

Useful for general knowledge but not authoritative for internal matters.

  • Web search: Official websites, blog posts, industry resources
  • Community forums: User discussions, workarounds, experiences
  • Third-party documentation: Integration partners, complementary tools
  • News and analyst reports: Market context, competitive intelligence

Confidence level: Low-Medium (may not reflect your specific situation)

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

Confidence level: Low (clearly flag as inference, not fact)

Answer Synthesis

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."
Show full SKILL.md (397 more words)Show less
Handling Contradictions

When sources disagree:

  1. Note the contradiction explicitly
  2. Identify which source is more authoritative or more recent
  3. Present both perspectives with context
  4. Recommend how to resolve the discrepancy
  5. If going to a customer: use the most conservative/cautious answer until resolved
Synthesis Structure
**Direct Answer:** [Bottom-line answer — lead with this]

**Confidence:** [High / Medium / Low]

**Supporting Evidence:**
- [Source 1]: [What it says]
- [Source 2]: [What it says — corroborates or adds nuance]

**Caveats:**
- [Any limitations or conditions on the answer]
- [Anything that might change the answer in specific contexts]

**Recommendation:**
- [Whether this is ready to share with customers]
- [Any verification steps recommended]

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:
  1. Subject matter expert: For technical or domain-specific questions
  2. Product team: For roadmap, feature, or capability questions
  3. Legal/compliance: For terms, privacy, security, or regulatory questions
  4. Billing/finance: For pricing, invoice, or payment-related questions
  5. Engineering: For custom configurations, bugs, or technical root causes
  6. 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)

Using This Skill

When conducting customer research:

  1. Always start by clarifying what you're actually looking for
  2. Search systematically — don't skip tiers even if you think you know where the answer is
  3. Cross-reference findings across multiple sources
  4. Be transparent about confidence levels — never present uncertain information as fact
  5. When in doubt about whether to share with a customer, err on the side of verifying first
  6. Document your research for future team benefit
  7. If the research reveals a gap in your knowledge base, flag it for documentation

© w95, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in 11-customer-success/customer-research of w95/awesome-claude-corporate-skills.

Open the folder on GitHubat commit 78dbc7c

Compare with similar skills

Customer Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Customer Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Customer Research this skillw95/awesome-claude-corporate-skills237—~2.3kAutomated safety check: PassMIT
Yao Positioning Skillyaojingang/yao-open-skills1.3k—~805Automated safety check: PassMIT
Market Researchextruct-ai/gtm-skills109—~1.7kAutomated safety check: PassNone
Ref BridgeAbilityai/cornelius109—~2.2kAutomated safety check: NotesMIT
Capture Conversationoutline/outline41k—~474Automated safety check: PassCustom licence
Project CairniBlinkQ/project-cairn2352 repos~861Automated safety check: PassMIT

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Categories

Questions about Customer Research

What does Customer Research do?

Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Customer Research is an agent skill from w95/awesome-claude-corporate-skills. Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer.

When should I use Customer Research?

Customer Research fits situations like: A customer asks a question you need to investigate; building background on a customer situation; you need account context.

How do I install Customer Research in Claude Code?

Run `npx skills add w95/awesome-claude-corporate-skills --skill customer-research -a claude-code`. Or copy the skill folder (11-customer-success/customer-research in w95/awesome-claude-corporate-skills) into .claude/skills/customer-research in your project. Claude Code loads it when a task matches its description.

How do I install Customer Research in Codex?

Run `npx skills add w95/awesome-claude-corporate-skills --skill customer-research -a codex`. Or copy the skill folder (11-customer-success/customer-research in w95/awesome-claude-corporate-skills) into .agents/skills/customer-research in your project. Codex loads it when a task matches its description.

Can I use Customer Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add w95/awesome-claude-corporate-skills --skill customer-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customer-research, .gemini/skills/customer-research, .github/skills/customer-research and .opencode/skills/customer-research in your project.

What does Customer Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Customer Research is instructions for the agent only.

Does Customer Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Customer Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Customer Research use?

Customer Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Customer Research use?

About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Customer Research?

Skills that share tags, products or a category with Customer Research: Yao Positioning Skill (yaojingang/yao-open-skills, 1.3k stars), Market Research (extruct-ai/gtm-skills, 109 stars), Ref Bridge (Abilityai/cornelius, 109 stars) and Capture Conversation (outline/outline, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customer Research?

w95 (a GitHub user) maintains it in w95/awesome-claude-corporate-skills, which has 237 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on February 26, 2026.

Source: w95/awesome-claude-corporate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.