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customer-snapshot

Aggregate Jira, Slack, Asana, and BigQuery data into interactive dashboards.

1|Updated Apr 7, 2026
Search Tags:#analytics#jira#bigquery#asana#dashboard-generation#customer-dashboard#slack-sentiment
One-click install
npx skills add https://github.com/al-stev/field-eng-skills --skill customer-snapshot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-snapshot
Source: https://github.com/al-stev/field-eng-skills/tree/main/.claude/skills/customer-snapshot
Command: npx skills add https://github.com/al-stev/field-eng-skills --skill customer-snapshot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Solutions Engineers need a single, screen-shareable view that aggregates a customer's Jira issues, Slack sentiment, Asana actions, and BigQuery usage so they can prepare for calls, QBRs, and escalations without jumping between tools.

Core Features & Use Cases

  • Deterministic two-stage pipeline: assemble data (assemble.py) and compose folder-based dashboards (compose.py) into customers/<name>/dashboard/.
  • Multi-source aggregation: pulls Jira issues with comments, optional Slack sentiment, Asana actions, and BigQuery usage; normalizes components and parent epics and computes trending/velocity metrics.
  • Professional, shareable output: index.html shell, data.js INTELLIGENCE_DATA, modular panels/, lib/ (ECharts), and history snapshots with internal/external audience toggle.
  • Graceful degradation and diagnostics: panels show empty states when sources are unavailable, and analytics transforms are stubbed with clear reasons.

Quick Start

Invoke the skill with a customer name, for example /customer-snapshot GResearch, to fetch sources, assemble INTELLIGENCE_DATA, and compose a dashboard folder you can open in a browser.

Frequently Asked Questions about customer-snapshot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I combine Jira issues, Slack sentiment, and BigQuery usage into a single customer call prep dashboard?▼

You can generate a customer intelligence dashboard that aggregates Jira issues, Slack sentiment, Asana actions, and BigQuery usage into a single shareable HTML view. The pipeline normalizes components, computes trending metrics, and assembles the data for screen-sharing during calls.

What is the best way to create a QBR dashboard from multiple project management and analytics sources?▼

Creating a QBR dashboard is streamlined by a deterministic pipeline that fetches Jira, Asana, and BigQuery data, computes velocity metrics, and composes a folder-based dashboard. It provides a professional index.html output with modular panels ready for presentation.

Can I generate a customer snapshot if my Slack sentiment data or Asana actions are currently unavailable?▼

Yes, the dashboard generation supports graceful degradation when sources are unavailable. Panels display empty states and analytics transforms are stubbed with clear reasons, allowing you to prepare for customer calls even with partial data.

Does the dashboard generation pipeline normalize Jira parent epics and components for trending metrics?▼

Yes, the pipeline normalizes Jira components and parent epics during the data assembly stage. It computes trending and velocity metrics from these normalized issues before composing the final INTELLIGENCE_DATA JSON for the dashboard.

How do I prepare for a customer escalation using aggregated tickets and usage data?▼

You can prepare for escalations by invoking the skill with a customer name to fetch open tickets, sentiment, and usage data. It assembles an interactive intelligence dashboard that summarizes trending metrics and SE actions for quick reference.

What format does the customer intelligence dashboard output use for browser-based screen sharing?▼

The dashboard output is a folder-based structure containing an index.html shell, a data.js file with INTELLIGENCE_DATA, modular panels, and ECharts libraries. This format is designed to be opened directly in a browser for screen-sharing.

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