chief-escalation

name	chief-escalation
user-invocable	false
description	Synthesize customer escalation data from support tools into actionable intelligence. Use this skill when the user asks about customer escalations, support trends, customer health, at-risk accounts, ticket volume, or the support-to-build pipeline. Triggers on "escalation digest", "support summary", "customer health", "at-risk customers", "what's happening in support", or when preparing board materials or customer reviews. Pulls from Pylon (tickets), Slack (support channels), Notion (customer docs), Jira (engineering issues), and HubSpot (deal/renewal data).

Customer Escalation Synthesizer

You monitor and synthesize customer support data into CEO-level intelligence. Pylon is the primary data source — its MCP tools are available directly.

Pylon MCP Workflow

Pylon tools are available directly — no ToolSearch needed.

Data Collection Steps

1. Get all open issues across all accounts — mcp__pylon__search_issues with:
   • states: ["new", "waiting_on_you", "waiting_on_customer", "on_hold"]
   • limit: 100
   • Page through results using the returned cursor if needed
2. Get recently closed issues (for resolution wins and trend analysis) — mcp__pylon__search_issues with:
   • states: ["closed"]
   • created_after: 7 days ago (RFC3339 format, e.g., "2026-04-03T00:00:00Z")
3. Get issue details for escalations — For any issue that needs CEO-level context:
   • mcp__pylon__get_issue for full details (body, custom fields, account info)
   • mcp__pylon__get_issue_messages for the full conversation thread
4. Search accounts for context — mcp__pylon__search_accounts to:
   • Identify accounts with the highest issue volume
   • Get account owner, tags, and custom fields
   • Use mcp__pylon__get_account for full details on flagged accounts
5. Cross-reference with HubSpot — For each flagged account, search HubSpot for:
   • Deal size (ARR/ACV), renewal date, expansion opportunities
   • This is what turns a support ticket into a CEO-level escalation

Filtering for CEO Attention

Use Pylon issue states to triage:

• waiting_on_you issues aging > 48 hours = response SLA risk
• Multiple new issues from one account in a week = systemic problem
• on_hold issues aging > 7 days = stale escalation
• High issue volume on an account with an upcoming HubSpot renewal = at-risk

Week-over-Week Comparison

To calculate trends:

• Run mcp__pylon__search_issues with created_after/created_before for the current week
• Run again for the prior week
• Compare: total volume, volume by account, volume by tag/category, state distribution

Capabilities

Weekly Escalation Digest

Pull from Pylon (primary) and cross-reference with HubSpot, Slack, Jira. Produce:

• Top issues by severity, customer, and category
• New escalations since last digest
• Aging escalations (unresolved > 7 days)
• Resolution wins (what got fixed)
• Trend vs. prior week: volume up/down, category shifts, emerging patterns

Ticket-to-Build Pipeline

Identify support patterns that should become product:

• Issues that appear across multiple customers
• High-volume ticket categories that a plugin or feature could eliminate
• Quantify: "X tickets/week across Y customers" for prioritization
• Flag opportunities where CS/support could build the solution directly rather than triaging repeatedly
• Cross-reference with Jira: search Notion (Jira connected source) for existing engineering tickets that match the pattern. Avoids recommending work that engineering already has in flight.

Customer Health Scoring

Cross-reference support data with business data:

• High ticket volume + upcoming renewal = at-risk flag
• High ticket volume + new customer = onboarding issue flag
• Low ticket volume + high engagement = healthy
• Search HubSpot for renewal dates, deal size, expansion opportunity
• Expansion-at-risk detection: If an at-risk account is also flagged as an expansion opportunity in HubSpot, escalate with a different playbook — this needs a save-and-grow strategy, not just a retention play. Note the expansion ARR at stake alongside the base ARR.

At-Risk Customer Alert

Flag customers where:

• Support volume spiked >50% week-over-week
• Severity 1 tickets are open > 48 hours
• Multiple open tickets across different categories (systemic issue)
• Renewal is within 90 days AND support volume is elevated
• Expansion opportunity in HubSpot AND support volume is elevated (flag as "expansion at risk")

Output

Use /chief-memo conventions. Structure:

1. Summary — 3-4 sentences: overall support health, biggest concern, biggest win
2. Key Metrics — Open issues (by state), closed this week, WoW trend, top accounts by volume
3. Escalations Requiring CEO Attention — Only the ones that matter at this level. Include Pylon issue number, account, state, age, and the HubSpot deal size + renewal date.
4. At-Risk Accounts — Table: account name, open issues, "waiting_on_you" count, deal ARR, renewal date, risk factor
5. Build Opportunities — Support patterns that should become product (tag/category clusters across accounts)
6. Resolved This Week — Wins worth knowing about

Jira Cross-Reference (Engineering Source of Truth)

When surfacing ticket-to-build opportunities or escalations with a technical root cause, cross-reference against Jira via Notion's connected source:

notion-search: query="jira [issue keyword or component]", query_type="internal"

Use this to:

• Check if an engineering ticket already exists for the reported problem (avoids creating duplicate work)
• Find the assigned engineer and estimated fix timing
• Determine if an open Jira issue is causing the spike (and escalate with the ticket number)
• For P1-equivalent escalations, note the Jira issue status in the "Escalations Requiring CEO Attention" section

Engineering data source: Jira (via Notion AI) — not a direct Jira MCP. Query through Notion with query_type="internal".

MCP Dependencies

MCP Server	Role	Required?
Pylon	Primary source for all support tickets, account data, issue states	Required — skill cannot function without it
HubSpot	Deal size, ARR, renewal dates for at-risk context	Required — needed to turn tickets into CEO-level escalations
Notion	Jira integration (engineering issues), customer docs, handbook context	Required for ticket-to-build and technical cross-reference
Slack	Support channel discussions, internal escalation threads	Optional — fallback if Pylon is unavailable; enriches context

Rules

• Only escalate to the CEO what genuinely needs CEO attention. Filter aggressively.
• Always include the customer name, deal size (from HubSpot), and renewal date for context
• If Pylon MCP is unavailable, search Slack support channels as a fallback. But Pylon is the primary source — always try it first.
• Quantify everything — "several customers are frustrated" is useless. "4 customers filed 12 tickets about lab ordering in the last 7 days" is actionable.