votc-insights

name	votc_insights
description	Extracts customer insights from Grain meeting transcripts across all feedback channels over the last 7 days. Analyzes pain points, feature demand, and competitive signals from external customer meetings. Use when asked for "voice of the customer", "VoTC analysis", "customer call insights", or "what are customers saying."

Voice of the Customer Insights Skill

Extract and synthesize customer insights from Grain meeting transcripts over the last 7 days. This skill uses the Grain REST API to fetch external customer meetings and their transcripts, then produces a structured report with pain point categorization, feature demand analysis, and competitive signals — all backed by specific customer quotes with verified external speaker attribution.

When to Use

Use this skill when asked to:

• Analyze recent customer conversations for product insights
• Generate a "voice of the customer" or VoTC report
• Identify customer pain points and feature requests from calls
• Prepare customer evidence for product planning or roadmap discussions

Do NOT use this skill when:

• You need GitHub issues, NPS, or email feedback — use analyze_customer_feedback instead
• You need a full weekly briefing — use weekly_product_briefing instead

Prerequisites

• GRAIN_TOKEN environment variable must be set with a Grain Personal Access Token (PAT) or Workspace Access Token. Tokens start with grain_pat_.... Get one from Grain Settings > Integration > API. In cloud agent environments, this is configured as an environment secret.
• requests Python package must be installed (pip3 install requests).
• The Grain MCP server is not required — this skill uses the REST API directly.

Grain API Reference

• Base URL: https://api.grain.com/_/public-api/v2
• Auth: Authorization: Bearer <GRAIN_TOKEN>
• Required header: Public-Api-Version: 2025-10-31
• Key endpoints:
  • POST /recordings — list recordings with filters (date range, participant_scope, pagination via cursor)
  • GET /recordings/{id}/transcript — get transcript segments for a recording
• Docs: https://developers.grain.com

Speaker Attribution

The participant id field returned in recording metadata matches the participant_id field in transcript segments. This is the key to reliable internal/external speaker attribution:

1. Request recordings with "include": {"participants": true} to get participant data with id, name, scope ("internal"/"external"/"unknown"), and email.
2. Each transcript segment has a participant_id field.
3. Join on (recording_id, participant_id) to determine who said what.

Important: You must include the participant id field when extracting participant data. Without it, speaker attribution will fail.

Workflow

Step 1: Fetch Meetings and Transcripts

Resolve the skill directory and the repo root:

REPO_ROOT=$(git rev-parse --show-toplevel)
SKILL_DIR="$REPO_ROOT/.warp/skills/votc_insights"

The Grain token is available as the GRAIN_TOKEN environment variable (configured as an environment secret in remote environments).

Run the data collection script:

python3 "$SKILL_DIR/fetch_grain_data.py" \
  --days 7 \
  --output /tmp/grain_votc_data.json

This fetches all external customer meetings from the last 7 days with full transcripts. The script handles pagination automatically. Output is a JSON file with meetings (metadata + participants with IDs) and transcripts (segments with participant_id).

Step 2: Analyze Transcripts

Run the analysis script to extract categorized quotes from external speakers:

python3 "$SKILL_DIR/analyze_transcripts.py" /tmp/grain_votc_data.json \
  --output /tmp/votc_analysis.json

This produces:

• Categorized quotes from external speakers only (filtered via participant_id → scope matching)
• Categories: pain_points, pricing, competitive, feature_requests, security, integrations, churn_risk, positive, agents
• Deduplication by meeting + text prefix
• Summary printed to stderr with counts and sample quotes

Step 3: Review and Synthesize

Read the analysis output (/tmp/votc_analysis.json) and the raw data (/tmp/grain_votc_data.json). Use the categorized external quotes to build the report. For each category:

1. Count distinct customers — group quotes by company (derive from meeting title and participant email domains).
2. Select the strongest quotes — prioritize quotes that are specific, mention concrete pain/need, or reference competitors.
3. Cross-reference categories — a customer who mentions both pricing and a competitor is a churn signal.

You can also query the raw transcript data directly for additional context beyond the keyword-based categories. The analysis script is a starting point — use your judgment to identify themes the keywords may have missed.

Step 4: Read Previous Report for Trend Comparison

ls "$REPO_ROOT/reports/votc_insights/"*.md 2>/dev/null | sort | tail -1

If a previous report exists, read it and compare:

• Which pain points are new (not in the previous report)
• Which pain points recurred (present in both)
• Which pain points from the previous report did not recur
• Same for feature requests and competitive mentions

Step 5: Compile the Report

Write the report following the output format below. Every claim must include:

• Customer name and contact name (from meeting participant data)
• Direct quote from the transcript (verbatim, italicized)
• Meeting date
• Quantified impact where available (e.g., "2.8x productivity", "$500/month spend")

Step 6: Save Report and Create PR

REPO_ROOT=$(git rev-parse --show-toplevel)
DATE=$(date +%Y-%m-%d)
BRANCH="votc-insights-$DATE"
git -C "$REPO_ROOT" checkout main
git -C "$REPO_ROOT" checkout -b "$BRANCH"
git -C "$REPO_ROOT" add reports/votc_insights/
git -C "$REPO_ROOT" commit -m "report: VoTC insights $DATE

Co-Authored-By: Oz <[e-mail]>"
git -C "$REPO_ROOT" push -u origin "$BRANCH"
gh pr create --repo YOUR_ORG/YOUR_REPO --base main --head "$BRANCH" \
  --title "VoTC Insights Report — $DATE" \
  --body "Voice of the Customer insights report for the week ending $DATE. Generated from Grain meeting transcripts via REST API.

Co-Authored-By: Oz <[e-mail]>"

Report Output Format

# Voice of the Customer Insights Report

**Report Date:** [date]
**Analysis Period:** [start date] – [end date] (7 days)
**Data Source:** Grain meeting transcripts (via Grain REST API) — [N] external customer meetings
**Prepared by:** Product PM Agent

---

## Executive Summary

[2-3 sentence factual overview. State the number of meetings analyzed, number of distinct customers, and the top 3 themes by frequency. No opinions or recommendations.]

---

## 1. Customer Pain Points

### 1.1 [Category Name] — [N] customers
**Frequency:** [N] quotes from external speakers across [N] meetings
**Impact:** [Factual impact: blocking deals, causing churn, workaround exists]
**Previous report:** [New / Recurring — N mentions last report / Not in previous report]

- **[Company]** ([Contact Name], [date]): *"[direct quote]"*
- **[Company]** ([Contact Name], [date]): *"[direct quote]"*

### 1.2 [Category Name] — [N] customers
...

---

## 2. Competitive Landscape

### 2.1 Competitor Mentions (by frequency)
1. **[Competitor]** — Mentioned by external speakers in [N] conversations. [Factual summary of how customers referenced it.]
2. **[Competitor]** — ...

### 2.2 Competitive Positioning Signals
- [Factual observation with customer quote]
- [Factual observation with customer quote]

---

## 3. Feature Demand Analysis

### 3.1 Most Requested Features (by frequency and urgency)

**P0:**
- **[Feature]** — [N] customers ([Company 1], [Company 2], ...). Segments: [segment list].
  - *"[representative quote]"* — [Contact], [Company]

**P1:**
- **[Feature]** — [N] customers. Segments: [segment list].

**P2:**
- ...

### 3.2 Customer Segment Patterns
- **Large Enterprise (500+ eng):** [themes] → [Company 1], [Company 2]
- **Mid-Market Tech (50–500 eng):** [themes] → [Company 1], [Company 2]
- **SMB / Startups (< 50 eng):** [themes] → [Company 1], [Company 2]
- **Non-traditional:** [themes] → [Company 1], [Company 2]

---

## 4. Success Stories & Positive Outcomes

### 4.1 Quantified Impact
- **[Company]** ([Contact]): [outcome]. *"[quote]"*
- **[Company]** ([Contact]): [outcome]. *"[quote]"*

### 4.2 Product Champions
- **[Company]** ([Contact]): [evidence of advocacy]. *"[quote]"*

---

## 5. Week-over-Week Changes

### New themes (not in previous report)
- [Theme] — [N] customers

### Recurring themes
- [Theme] — [N] customers this week, [N] last report

### Themes from previous report that did not recur
- [Theme] — [N] last report, 0 this week

---

## Appendix: Companies Referenced

[Alphabetical list of all companies mentioned in the report]

---

*Data sourced from Grain meeting transcripts via Grain REST API (public-api/v2). All quotes are from external speakers, identified via participant_id matching. All customer names and quotes should be treated as confidential.*

Guidelines

• Evidence-driven. Every claim must cite a specific customer, quote, and meeting date. If you can't cite it, don't include it.
• External voices only. The analysis script filters to scope == "external" via participant_id matching. Only use quotes from external speakers.
• Deduplicate across categories. The same transcript segment may match multiple keyword categories. Count each customer mention once per theme.
• Quantify everything. Number of customers, number of external-speaker quotes, number of meetings. Avoid vague terms like "several" or "many."
• Verbatim quotes. Use exact transcript quotes, italicized. Do not paraphrase or clean up customer language.
• No editorializing in Sections 1–4. Report facts only. Do not use words like "alarming", "critical", "exciting". Do not make recommendations — the WYNK skill handles that.
• Confidentiality. All customer names and quotes are confidential internal data. Note this at the end of the report.

Scripts

This skill includes two Python scripts in the skill directory:

• fetch_grain_data.py — Fetches external meetings and transcripts from the Grain REST API. Handles pagination, preserves participant IDs for speaker attribution.
• analyze_transcripts.py — Analyzes transcripts for VoTC themes using keyword matching. Filters to external speakers only via participant_id → scope matching. Outputs categorized quotes as JSON.

Both scripts log progress to stderr and output JSON to stdout (or a file via --output).