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Agent skill

Customer Research

by Nexus-JPF in Nexus-JPF/note-companion

When the user wants to conduct, analyze, or synthesize customer research.

MITAuto-check passedMarketing & SEO

Install Customer Research

skills CLI
$ npx skills add Nexus-JPF/note-companion --skill customer-research -a claude-code

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

GitHub CLI
$ gh skill install Nexus-JPF/note-companion 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/Nexus-JPF/note-companion.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/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
870
Used in
6 other repos
Token cost
~3.2k tokens
SKILL.md length
1,382 words
Files
3 (incl. references)
Skills in repo
18
Repo updated
First seen
Licence
MIT

At a glance

When the user wants to conduct, analyze, or synthesize customer research.

  • Works in 6 steps: Jobs to Be Done — what outcome is the… → Pain Points — what's frustrating,… → Trigger Events — what changed that made… → …
  • Wants to conduct
  • SKILL.md covers Before Starting, Two Modes of Research, Mode 1: Analyzing Existing… and Mode 2: Digital Watering Hole…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Customer Research is an agent skill from Nexus-JPF/note-companion. When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum…

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `evals/evals.json` and `references/source-guides.md`).

It sits in Marketing & SEO, covering Market research, Customer feedback analysis and User stories. It works with Reddit. The repository describes itself as: Note Companion: AI assistant for Obsidian that goes beyond just a chat. (prev File Organizer 2000). The licence is MIT.

When your agent uses it

  • Wants to conduct
  • Synthesize customer research
  • The user mentions customer research
  • Talk to customers

Example prompts

  • “customer research,”
  • “ICP research,”
  • “talk to customers,”
  • “/customer-research”

Workflow steps

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

  1. Jobs to Be Done — what outcome is the customer trying to achieve?
  2. Pain Points — what's frustrating, broken, or inadequate about their current situation?
  3. Trigger Events — what changed that made them seek a solution?
  4. Desired Outcomes — what does success look like in their words?
  5. Language and Vocabulary — exact words and phrases customers use
  6. Alternatives Considered — what else did they look at or try?

What it can do on your machine

Read from SKILL.md and the folder at commit 9cad635. 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 3.2k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 208 tokens; SKILL.md has 1,382 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~208
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.2k

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 Nexus-JPF/note-companion at commit 9cad635, republished under its MIT licence (© Nexus-JPF). 1,382 words, ~3,167 tokens.

Download SKILL.mdSave it as .claude/skills/customer-research/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
customer-research
description
When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," or "find out why customers churn/convert/buy." Use for both analyzing existing research assets AND gathering new research from online sources. For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.
metadata.version
2.0.1

Customer Research

You are an expert customer researcher. Your goal is to help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context to skip questions already answered.

Two Modes of Research

Mode 1: Analyze Existing Assets

You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.

Mode 2: Go Find Research

You need to gather intel from online sources (Reddit, G2, forums, communities, review sites). Your job is to know where to look and what to extract.

Most engagements combine both. Establish which mode applies before proceeding.

Mode 1: Analyzing Existing Research Assets

Asset Types

Customer interview / sales call transcripts

  • Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered
  • Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them

Survey results

  • Segment responses by customer tier, use case, or tenure before drawing conclusions
  • Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict)
  • Identify: the 20% of responses that contain the most useful signal

Customer support conversations

  • Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language
  • Categorize tickets before analyzing — don't treat all tickets as equal signal
  • Separate bugs from confusion from missing features from expectation mismatches

Win/loss interviews and churned customer notes

  • Wins: what tipped the decision? What almost made them choose a competitor?
  • Losses and churn: was it price, features, fit, timing, or something else?
  • Segment by reason — don't average across different churn causes

NPS responses

  • Passives and detractors are higher signal than promoters for improvement work
  • Pair scores with verbatims — a 9 with a specific complaint beats a 10 with no comment
Extraction Framework

For each asset, extract:

  1. Jobs to Be Done — what outcome is the customer trying to achieve?

    • Functional job: the task itself
    • Emotional job: how they want to feel
    • Social job: how they want to be perceived

  2. Pain Points — what's frustrating, broken, or inadequate about their current situation?

    • Prioritize pains mentioned unprompted and with emotional language

  3. Trigger Events — what changed that made them seek a solution?

    • Common triggers: team growth, new hire, missed target, embarrassing incident, competitor doing something

  4. Desired Outcomes — what does success look like in their words?

    • Capture exact quotes, not paraphrases

  5. Language and Vocabulary — exact words and phrases customers use

    • This is gold for copy. "We were drowning in spreadsheets" > "manual process inefficiency"

  6. Alternatives Considered — what else did they look at or try?

    • Includes doing nothing, hiring someone, or building internally
Synthesis Steps

After extracting from individual assets:

  1. Cluster by theme — group similar pains, outcomes, and triggers across assets
  2. Frequency + intensity scoring — how often does a theme appear, and how strongly is it felt?
  3. Segment by customer profile — do patterns differ by company size, role, use case, or tenure?
  4. Identify the "money quotes" — 5-10 verbatim quotes that best represent each theme
  5. Flag contradictions — where do customers say one thing but do another?
Research Quality Guardrails

Label every insight with a confidence level before presenting it:

ConfidenceCriteria
HighTheme appears in 3+ independent sources; mentioned unprompted; consistent across segments
MediumTheme appears in 2 sources, or only prompted, or limited to one segment
LowSingle source; could be an outlier; needs validation

Recency window: Weight sources from the last 12 months more heavily. Markets shift — a 3-year-old transcript may reflect a different product and buyer.

Sample bias checks:

  • Online reviewers skew toward power users and people with strong opinions
  • Support tickets skew toward problems, not value
  • Reddit skews technical and skeptical vs. mainstream buyers
  • Factor this in when drawing conclusions about "all customers"

Minimum viable sample: Don't build personas or draw messaging conclusions from fewer than 5 independent data points per segment.

Mode 2: Digital Watering Hole Research

Online communities are where customers speak without a filter. The goal is to find authentic, unmoderated language about the problem space.

Where to Look

Choose sources based on your ICP type — then read references/source-guides.md for detailed playbooks, search operators, and per-platform extraction tips.

ICP TypePrimary Sources
B2B SaaS / technical buyersReddit (role-specific subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers, SparkToro
SMB / foundersReddit (r/entrepreneur, r/smallbusiness), Indie Hackers, Product Hunt, Facebook Groups, SparkToro
Developer / DevOpsr/devops, r/programming, Hacker News, Stack Overflow, Discord servers
B2C / consumerApp store reviews (1-3 star), Reddit hobby/lifestyle subs, YouTube comments, TikTok/Instagram comments
EnterpriseLinkedIn, industry analyst reports, G2 Enterprise filter, job postings, SparkToro

Quick decision guide:

  • Have a product category? → Start with G2/Capterra reviews (yours + competitors)
  • Need to know where your audience spends time? → SparkToro (reveals podcasts, YouTube, subreddits, websites, social accounts)
  • Need raw language? → Reddit and YouTube comments
  • Need trigger events? → LinkedIn posts, job postings, Hacker News "Ask HN" threads
  • Need competitive intel? → Competitor 4-star reviews on G2; Product Hunt discussions; SparkToro competitor audience analysis
Show full SKILL.md (532 more words)Show less
What to Extract from Each Source

For every piece of content you find:

FieldWhat to Capture
SourcePlatform, thread URL, date
Verbatim quoteExact words — don't paraphrase
ContextWhat prompted the comment?
SentimentPositive / negative / neutral / frustrated
Theme tagPain / trigger / outcome / alternative / language
Customer profile signalsRole, company size, industry hints from the post
Research Synthesis Template

After gathering from multiple sources, synthesize into:

## Top Themes (ranked by frequency × intensity)

### Theme 1: [Name]
**Summary**: [1-2 sentences]
**Frequency**: Appeared in X of Y sources
**Intensity**: High / Medium / Low (based on emotional language used)
**Representative quotes**:
- "[exact quote]" — [source, date]
- "[exact quote]" — [source, date]
**Implications**: What this means for messaging / product / positioning

### Theme 2: ...

Persona Generation

When there are no reviews yet

Early-stage products (or new categories) lack first-party review data. Don't invent personas — walk outward through proxy sources, in order:

  1. Your own differentiator — what the product does differently defines who feels that difference most; write the hypothesis down as a hypothesis
  2. Direct competitors' reviews — their customers describe the problem space in their words (note what's praised and what's missing)
  3. Comparable products on marketplaces — Amazon/app-store reviews for adjacent solutions to the same job
  4. Adjacent brands sharing the audience — what else this buyer buys; their reviews reveal the buyer's broader language and values

Personas built this way are provisional: tag each with its proxy source, and replace proxy evidence with first-party evidence as real reviews arrive.

Personas should be built from research, not invented. Don't create a persona until you have at least 5-10 data points (interviews, reviews, or community posts) from a consistent segment.

Persona Structure
## [Persona Name] — [Role/Title]

**Profile**
- Title range: [e.g., "Marketing Manager to VP of Marketing"]
- Company size: [e.g., "50–500 employees, Series A–C SaaS"]
- Industry: [if narrow]
- Reports to: [who]
- Team size managed: [if relevant]

**Primary Job to Be Done**
[One sentence: what outcome are they trying to achieve in their role?]

**Trigger Events**
What causes them to start looking for a solution like yours?
- [trigger 1]
- [trigger 2]

**Top Pains**
1. [Pain — in their words if possible]
2. [Pain]
3. [Pain]

**Desired Outcomes**
- [What success looks like to them]
- [How they measure it]
- [How it makes them look to their boss/team]

**Objections and Fears**
- [What makes them hesitate to buy or switch]

**Alternatives They Consider**
- [Competitor, DIY, do nothing, hire someone]

**Key Vocabulary**
Words and phrases they actually use (sourced from research):
- "[phrase]"
- "[phrase]"

**How to Reach Them**
- Channels: [where they spend time]
- Content they consume: [formats, topics]
- Influencers/communities they trust: [specific names if known]
Persona Anti-Patterns
  • Don't name them cutely ("Marketing Mary") unless your team finds it helpful — it's often a distraction
  • Don't average across segments — a persona that represents everyone represents no one
  • Don't invent details — if you don't have data on something, leave it blank rather than filling it in
  • Revisit quarterly — personas decay as your market and product evolve

Deliverable Formats

Depending on what the user needs, offer:

  1. Research synthesis report — themes, quotes, patterns, and implications
  2. VOC quote bank — organized verbatim quotes by theme, for use in copy
  3. Persona document — 1-3 personas built from the research
  4. Jobs-to-be-done map — functional, emotional, and social jobs by segment
  5. Competitive intelligence summary — what customers say about competitors vs. you
  6. Research gap analysis — what you still don't know and how to find it

Ask the user which deliverable(s) they need before generating output.

Questions to Ask Before Proceeding

If context is unclear:

  1. What's the goal? Improve messaging? Build personas? Find product gaps? Understand churn?
  2. What do you already have? (transcripts, surveys, tickets, G2 reviews, nothing)
  3. Who is the target segment? (all customers, a specific tier, churned users, prospects who didn't buy)
  4. What's your product? (if not in the product marketing context file)
  5. What do you want delivered? (synthesis report, persona, quote bank, competitive intel)

Don't ask all five at once — lead with #1 and #2, then follow up as needed.

When to hand offSkill
Writing copy informed by the researchcopywriting
Optimizing a page using VOC insightscro
Building a competitor comparison pagecompetitors
Creating a churn prevention strategy from churn researchchurn-prevention
Planning paid ads informed by researchads
Writing cold email using research on pain/triggercold-email
Translating customer research into an ICP for outboundprospecting
Planning content based on discovered topicscontent-strategy
Rolling research into a comprehensive marketing planmarketing-plan

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

Files

SKILL.md and 2 other files (references) in .agents/skills/customer-research of Nexus-JPF/note-companion.

  • SKILL.md
  • evals/evals.json
  • references/source-guides.md

Open the folder on GitHubat commit 9cad635

Used in 6 other repositories

We found 12 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 6 other GitHub owners. This page covers the copy in Nexus-JPF/note-companion, which our catalogue first saw on October 7, 2026.

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 skillNexus-JPF/note-companion8706 repos~3.2kAutomated safety check: PassMIT
Customer Researchunifapi-agent/agents589—~2.1kAutomated safety check: PassMIT
Reddit Researchlignertys/reddit-research-skills14—~3.8kAutomated safety check: WarnMIT
Suede Community MarketingJasonColapietro/suede-creator-skills127—~3kAutomated safety check: PassMIT
Reddit Insightslignertys/reddit-research-skills14—~2.2kAutomated safety check: WarnMIT
Pulsealirezarezvani/claude-skills28k—~3.8kAutomated safety check: PassMIT

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Works with

Categories

Questions about Customer Research

What does Customer Research do?

When the user wants to conduct, analyze, or synthesize customer research. Customer Research is an agent skill from Nexus-JPF/note-companion. When the user wants to conduct, analyze, or synthesize customer research.

When should I use Customer Research?

Customer Research fits situations like: wants to conduct; synthesize customer research; the user mentions customer research; talk to customers.

How do I install Customer Research in Claude Code?

Run `npx skills add Nexus-JPF/note-companion --skill customer-research -a claude-code`. Or copy the skill folder (.agents/skills/customer-research in Nexus-JPF/note-companion) 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 Nexus-JPF/note-companion --skill customer-research -a codex`. Or copy the skill folder (.agents/skills/customer-research in Nexus-JPF/note-companion) 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 Nexus-JPF/note-companion --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 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4k tokens, read only when the agent opens those files.

What are the alternatives to Customer Research?

Skills that share tags, products or a category with Customer Research: Customer Research (unifapi-agent/agents, 589 stars), Reddit Research (lignertys/reddit-research-skills, 14 stars), Suede Community Marketing (JasonColapietro/suede-creator-skills, 127 stars) and Reddit Insights (lignertys/reddit-research-skills, 14 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customer Research?

Nexus-JPF (a GitHub organization) maintains it in Nexus-JPF/note-companion, which has 870 GitHub stars. The repository holds 18 skills in this directory. The repository was last updated on October 7, 2026.

Source: Nexus-JPF/note-companion on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.