Mines reviews, interviews, support tickets and forum threads for voice-of-customer insight — verbatim pains, desires, objections and triggers, clustered into themes with JTBD statements and a message-market map. Use when the user says "analyze these reviews", "what do customers actually want", "synthesize this feedback", or before writing any positioning or copy.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Customer Research — Voice of Customer
> "Synthesise user voice"
## When to use
- "Here are 200 reviews — what do customers actually care about?"
- "Synthesize these interview notes / support tickets"
- "What objections keep coming up?"
- Before positioning, landing copy, or ads — copy written without voice-of-customer is guessing
- Works on pasted text or files; optional upgrade: web search to mine public reviews and Reddit threads
## Workflow
1. **Ingest the corpus**: pasted text, CSV/exports, or files. Note the source mix and any sampling bias (e.g. only angry customers write tickets).
2. **Extract verbatims** into four buckets: pains, desires, objections, buying triggers. Keep the customer's exact words — never paraphrase at this stage.
3. **Cluster into themes** per bucket; count frequency so loud-but-rare doesn't beat quiet-but-common.
4. **Write JTBD statements** for the top clusters: "When {situation}, I want to {motivation}, so I can {outcome}."
5. **Build the message-market map**: their words → your copy blocks (headline candidates, bullet candidates, objection-handling lines) — quoted or lightly compressed, never marketing-speak.
…
The line at the top of this page pins the label git:20260711.2713ff9, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (3036 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
alebgl77/claude-inc · 21 stars · license MIT · pushed 2026-10-22 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_dt7etvedt2s7l4j4
GET https://markdownregistry.com/api/v1/resolve?ref=alebgl77/claude-inc/customer-research
GET https://markdownregistry.com/api/v1/blob/85e27a83ffc5658af9dbfda3c6204b99638e6c07fbbae277212ade30717d8c2e
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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