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Customer Research

Official
anthropics·vlatest·marketing·Apache-2.0

This skill gathers & analyzes customer feedback to reveal insights into preferences and pain points, boosting product development & marketing effectiveness.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/knowledge-work-plugins --skill customer-research -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/knowledge-work-plugins at da38ec1ee8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
da38ec1ee8
Manifest
customer-support/skills/customer-research/SKILL.md
Scanned
1 file, 247 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to autonomously gather and synthesize customer insights from diverse data sources. It transforms raw feedback, reviews, and support tickets into actionable intelligence for product and business strategy.

When to use it

  • Identify emerging pain points before they impact retention rates.
  • Validate new feature ideas against actual user sentiment.
  • Generate competitive analysis reports based on public market feedback.
  • Monitor brand perception across social media and review platforms.

Key capabilities

  • Aggregates data from multiple customer touchpoints including surveys, interviews, and support logs.
  • Performs sentiment analysis to detect positive, neutral, and negative trends over time.
  • Categorizes feedback into themes using natural language processing techniques.
  • Outputs structured reports highlighting priority areas for improvement or innovation.

Example prompts

"Analyze our last quarter's customer support tickets and summarize the top three recurring issues." "Compare sentiment scores between users who upgraded to premium versus those on basic plans." "Extract specific feature requests mentioned in recent social media comments about our mobile app."

Tips & gotchas

Ensure your data sources are properly connected and cleaned before running analysis for accurate results. Be mindful of privacy regulations when processing personally identifiable information within customer feedback datasets.