Voice of Customer skills for Claude Code

A VoC pipeline that refuses to report a pattern until several independent channels agree. Pulls from call recordings, review sites, internal chat, meeting notes and anything else you wire up. Weights each signal by who said it. Reports only what corroborates. Routes the result to the person who can act on it, with the customer's exact words attached.

Most "AI for customer feedback" workflows stop at summarize this. A summary of a hundred conversations is a paragraph nobody acts on, and the human is still the bottleneck. This is the system version: signal in continuously, confirmation applied mechanically, human attention spent only on judgment calls.

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What it produces

Here is the shape of the output. Company names are fictional and the numbers are illustrative — this is a format example, not a real run.

SYNTHESIS — rule: 3+ independent channels, or 2+ with a tier-A account. Minimum 3 mentions.
──────────────────────────────────────────────────────────────────────────────
34 clusters in → 6 confirmed · 9 watchlist · 19 dropped

CONFIRMED
  [HIGH  ] Export is slow on large datasets
           pain · 11 mentions · 3 channels (call, review, chat) · 7 accounts · A:2 B:8 C:1
           3 independent channels
           +4 unattributed signal(s) excluded from the count

  [HIGH  ] Cannot buy without SSO
           request · 6 mentions · 2 channels (call, ticket) · 4 accounts · A:2 B:4
           2 channels including a tier-A account

  [MEDIUM] Onboarding felt long
           pain · 5 mentions · 2 channels · 1 account · B:5
           2 channels, no tier-A contributor — downgraded: all mentions from one
           account (Northwind Trading)

WATCHLIST (single channel — not reported, kept for corroboration)
  · Reporting filters are confusing (review, 3 mentions)

DROPPED (19) — kept in 2026-04-26-dropped.json for traceability

That last downgrade is the point of the whole thing. Five mentions of a pain looks like a trend right up until you notice they are all the same customer. This pipeline checks the account spread in code, every time, so nobody has to remember to.

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What this gets wrong

Read this before you trust anything it outputs.

• The confirmation rule is a heuristic, not a truth test. Three channels can all reflect one loud market segment. It filters noise; it does not establish fact.
• Every source is a biased sample. Sales calls only contain people who took a meeting. Reviews only contain the delighted, the furious, and the incentivized. Chat only contains what a colleague found notable enough to paste. None of these is your market.
• Adding connectors makes confirmation easier, not truer. Wire up six channels and things start confirming that should not. Raise min_independent_sources when you add sources.
• Cross-source duplicates manufacture confidence. The same complaint relayed in chat, captured on a call, and filed as a ticket is one event, not three. Deduplication happens before profiling, and if you skip it the rule cannot save you.
• It cannot tell you what nobody said. The biggest reason people do not buy is usually invisible to every source here.

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Verify it in thirty seconds

git clone <this-repo> && cd voc-claude-skills
npm install
npm run check

npm run check validates every skill's frontmatter, catches lowercase skill.md files that break on Linux, scans for absolute paths and leaked credentials, syntax-checks every script, and confirms the threshold config parses. No credentials needed. It exits non-zero on failure, so it works in CI.

Then, with nothing configured yet:

node scripts/synthesize.js examples/clusters.sample.json --out /tmp/voc-demo

That runs the actual confirmation rule against sample clusters and shows you exactly what confirms, what goes to the watchlist, and what gets dropped. It is the core of the system and it runs without an API key.

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Architecture

  calls · reviews · chat · notes · custom
                  │
                  ▼
          /voc-source-*              normalize to one signal shape
                  │
                  ▼
          /voc-profiler              CRM context, ICP fit, weight tier
                  │                  (tier U = unattributed = quarantined)
                  ▼
          /voc-synthesize            cluster by meaning (subagents)
                  │                  apply the rule (code)
                  ▼
          /voc-route                 testimonial · case study · objection map
                  │                  product feedback · exec memo
                  ▼
          /voc-dispatch              outreach emails · copy briefs
                                     PM one-pagers · exec memo

Clustering is judgment, so a model does it. Counting is not, so code does it. The confirmation rule lives in scripts/synthesize.js reading config/thresholds.json, and nowhere else — it is a threshold you tune, not a paragraph a model interprets differently each run.

Skills

Skill	What it does
/voc-orchestrator	The weekly run. All six phases, with preflight.
/voc-source-calls	Pull transcripts from your call recorder
/voc-analyze-calls	Transcripts → prospect intelligence report
/voc-source-reviews	G2, Capterra and friends → messaging brief. Costs money.
/voc-source-chat	Customer quotes your team pasted into Slack
/voc-source-notes	AI meeting notes, for the conversations the recorder missed
/voc-source-custom	Surveys, tickets, CRM email, anything else
/voc-profiler	Attach CRM context and weight to one signal
/voc-synthesize	Cross-source confirmation
/voc-route	Send each theme where it gets acted on
/voc-dispatch	Generate the actual deliverables

Setup

Full walkthrough in docs/SETUP.md. The short version:

cp .env.example .env                                  # call recorder + tokens
cp .mcp.json.example .mcp.json                        # MCP servers
cp context/product-context.example.md context/product-context.md

Then fill in product-context.md. It takes about twenty minutes and it is the difference between useful output and generic themes.

You do not need everything. The pipeline runs with one source and tells you honestly that nothing can reach high confidence. Start with calls, add chat, then reviews.

After connecting any MCP server, run /mcp and copy the exact tool names into the relevant skill's allowed-tools. Tool namespaces differ between servers for the same product, and a skill that declares a name your server does not expose fails at its first call while looking like it ran. This is the most common setup failure by a wide margin.

Cost

Everything is free except review mining, which runs paid scrapers. A four-domain run typically lands between $2 and $8. Apify's free tier is $5 of credit total, not monthly — one wide run can consume it. See docs/COST.md before your first review run.

Privacy

This pipeline writes customer conversation transcripts to disk. They are personal data under GDPR and CCPA.

• Point VOC_OUTPUT_DIR at a local, gitignored path. Not Dropbox, not iCloud. The orchestrator's preflight warns you, but check it yourself.
• Recording laws vary by jurisdiction. This tool assumes you already have lawful recordings.
• Tell your team the chat harvesting is running.
• A public review is not permission to put someone's words in an ad with their logo. Nothing in this repo ever marks a quote as approved.

Requirements

Node 18+. Claude Code. A call recorder with a transcript API is the only near-essential dependency; everything else is optional.

Contributing

Adding a call-recorder adapter is the most useful contribution — see scripts/providers/README.md, it is four functions. Run npm run check before opening a PR.

License

MIT. See LICENSE.

Frameworks referenced in the skills are credited in CREDITS.md.