Poc analysis

POC Analysis Skill

Required Environment Variables

• HUBSPOT_ACCESS_TOKEN — HubSpot private app token (needs deals read scope)
• METABASE_API_KEY — Metabase API key
• POC_BOT_SLACK_TOKEN — Slack bot OAuth token (xoxb-...)
• SLACK_CHANNEL — Target channel (default: #poc-bot)

Workflow

Step 1: Fetch Data

Run the fetch script to pull all companies and their Metabase metrics:

python3 scripts/fetch_poc_data.py

Dashboard cards, output labels, and parameter bindings are defined in config/metabase_cards.json (override the path via METABASE_CARDS_CONFIG). With the default labels, this writes /tmp/poc_data.json containing, per company:

• name, team_id, demo_held_date, date_range (demo date → today)
• usage_overview: total_primary_metric, users_on_team, active_users, wau, primary_feature_wau, secondary_feature_wau
• user_breakdown: users_active_days (aggregate per user), users_daily_activity (per user per day) See references/metabase_config.md for the full column schemas for these tables.

Step 2: Analyze

Before analyzing, read config/methodology.md for this deployment's primary metric definition, user tier criteria, and summary framing guidance.

For each company in /tmp/poc_data.json:

Usage Overview (from usage_overview):

• Compute the activation rate and call out the primary metric total prominently
• Trace each weekly trend column week-over-week to identify growth, plateau, or decline
• Note any significant divergence between the general engagement trend and the primary metric trend

User Breakdown — categorize users into tiers (criteria in config/methodology.md):

• Power users: high primary metric, high activity signal
• Mid-tier: moderate primary metric, consistent presence
• Core-only: active days_active but near-zero primary metric — using the product but not its key value feature; note as a growth opportunity
• Joined but inactive: low days_active, last_active_day is recent but usage is minimal
• Never used: last_active_day is null

Daily activity trends (from users_daily_activity):

• Identify users who started strong then fell off
• Identify users who started slow but are ramping
• Note bursty vs. consistent usage patterns
• Highlight if a user's primary metric usage is concentrated vs. broad

Step 3: Write Analysis JSON

Write /tmp/poc_analysis.json — a list of objects, one per company:

[
  {
    "company": "<Company Name>",
    "text": "POC Analysis: <Company Name>",
    "blocks": [ ...Slack Block Kit JSON... ]
  }
]

Block Kit structure per company (in order):

1. header block — company name and date range
2. section with fields — key metrics: team size, active users, activation %, primary metric total, WAU trend, primary-feature WAU trend
3. divider
4. section — Power users bullet list
5. section — Mid-tier users bullet list
6. section — Engagement snapshot (core-only + inactive + never-used)
7. section — Summary (1–2 sentence synthesis of notable trends and patterns) Keep each section block under 3,000 characters. Use *bold* for names and metric values.

Step 4: Post to Slack

python3 scripts/post_to_slack.py

Reads /tmp/poc_analysis.json and posts one top-level message per company to $SLACK_CHANNEL.

Reference

• Metabase dashboard structure, card IDs, and metric definitions: references/metabase_config.md