sales-maestroqa

What this skill does

# MaestroQA Platform Help

## Step 1 — Gather context

If `references/learnings.md` exists, read it first for accumulated platform knowledge.

1. **What do you need?**
   - A) Set up or refine QA scorecards
   - B) Build coaching workflows from QA data
   - C) Configure conversation analytics / AskAI
   - D) Integrate with helpdesk, phone system, or CRM
   - E) Export data to warehouse or via API
   - F) Monitor AI chatbot quality
   - G) Ingest CSAT scores and correlate with QA
   - H) Other

2. **Current setup?**
   - A) New to MaestroQA (not yet connected)
   - B) Connected to helpdesk, building scorecards
   - C) Running QA, want to improve coaching
   - D) Mature — need analytics, API, or warehouse export

3. **Helpdesk / phone system?**
   - A) Zendesk / Freshdesk / Intercom / Front / Gorgias
   - B) Salesforce Service Cloud / ServiceNow / Dynamics 365
   - C) Five9 / Talkdesk / Amazon Connect / NICE / Genesys
   - D) Aircall / RingCentral / Dialpad / Twilio / Vonage
   - E) Other / multiple

Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.

## Step 2 — Route or answer directly

| Problem domain | Route to |
|---|---|
| Comparing QA tools (MaestroQA vs Observe.AI vs Enthu.AI) | `/sales-coaching` for cross-platform comparison |
| Choosing a CCaaS platform | `/sales-ccaas-selection` |
| Real-time agent coaching during calls | `/sales-balto` or `/sales-cresta` |
| Help desk platform selection | `/sales-helpdesk-selection` |
| Salesforce-specific questions | `/sales-salesforce` |
| Sales call review and coaching | `/sales-call-review` |

When routing, provide the exact command: "This is a {problem domain} question — run: `/sales-{skill} {user's original question}`"

## Step 3 — MaestroQA platform reference

**Read `references/platform-guide.md`** for the full platform reference — modules, pricing, integrations, data model, workflows.

Answer the user's question using only the relevant section. Don't dump the full reference.

## Step 4 — Actionable guidance

Focus on the user's specific situation.

- **Scorecard design**: Start with 5-8 binary criteria per scorecard. Binary (yes/no) scores faster and more consistently than scaled (1-5). Add weighted sections for compliance vs quality vs sales effectiveness.
- **Coaching connection**: Link low QA scores to coaching sessions automatically — don't let scores sit in reports. Use reverse-ETL to push coaching tasks to Slack or your CRM.
- **Analytics adoption**: Start with AskAI for ad-hoc queries before building custom dashboards. AskAI answers natural language questions about conversation data without needing SQL.
- **API automation**: Use the Rippit API for bulk data export, CSAT ingestion, and agent provisioning. Rate limit is 10 req/s — batch operations where possible.
- **Chatbot QA**: Connect Ada, Sierra, Agentforce, or Forethought to grade bot conversations with the same scorecards used for human agents.

If you discover a gotcha, workaround, or tip not covered in `references/learnings.md`, append it there.

## Gotchas

> *Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.*

- **AI features cost extra.** MaestroQA's AI-powered analytics and conversation intelligence are add-ons — budget separately from the base QA subscription.
- **Ticket sync delay.** Zendesk tickets typically take up to 3 hours to sync. Only tickets updated within the last 45 days sync. Plan QA workflows around this lag.
- **API tokens expire every 90 days** by default. Set up a rotation process or disable expiry for long-lived integrations.
- **Export format limitations.** Some users report inability to export grading progress as CSV/PDF directly from the UI. Use the Rippit API `POST /request-raw-export` endpoint as a workaround.
- **Dashboard customization is limited.** For advanced analytics, export to Snowflake/BigQuery and use external BI tools.
- **Learning curve.** MaestroQA requires upfront investment to configure scorecards, calibration sessions, and coaching workflows. Budget 2-4 weeks for initial setup with a dedicated QA admin.
- **Rate limits.** API is 10 req/s and 100 req/min. Batch operations (comments, metrics) accept up to 500 items per request — use these instead of individual calls.

## Related skills

- `/sales-coaching` — Sales coaching and training strategy across all QA and enablement platforms
- `/sales-observe-ai` — Observe.AI platform help (enterprise contact center QA with Auto QA, Agent Copilot)
- `/sales-enthu` — Enthu.AI platform help (affordable contact center QA, fast setup)
- `/sales-balto` — Balto platform help (real-time AI guidance during calls)
- `/sales-cresta` — Cresta platform help (enterprise contact center AI)
- `/sales-convin` — Convin platform help (contact center QA + coaching + LMS)
- `/sales-ccaas-selection` — Choosing a CCaaS platform
- `/sales-helpdesk-selection` — Choosing a help desk platform
- `/sales-do` — Not sure which skill to use? The router matches any sales objective to the right skill. Install: `npx skills add sales-skills/sales --skill sales-do`

## Examples

### Example 1: Set up QA scorecards for a support team
**User says**: "We just connected Zendesk — how do I build QA scorecards for our support team?"
**Skill does**: Walks through scorecard design (binary criteria, weighted sections, compliance vs quality), calibration setup for evaluator consistency, and assignment rules for random sampling vs targeted review.

### Example 2: Export QA data to Snowflake
**User says**: "I need to get our grading data into Snowflake for custom reporting"
**Skill does**: Covers the native Snowflake integration setup and the Rippit API export endpoints (`POST /request-raw-export`, `GET /export-data/:id`) as alternatives, with rate limit considerations.

### Example 3: Correlate CSAT with QA scores
**User says**: "We want to see if our QA scores actually predict customer satisfaction"
**Skill does**: Explains the CSAT Bulk Ingestion API, how to connect Qualtrics/Delighted/Simplesat for native CSAT ingestion, and how to use AskAI to query correlations between QA scores and CSAT data.

## Troubleshooting

### QA scores don't correlate with customer satisfaction
**Cause**: Scorecards measure process compliance (did the agent follow the script) but not outcome quality (was the customer actually helped)
**Solution**: Add outcome-oriented criteria — resolution quality, customer effort reduction, empathy signals. Use CSAT ingestion to validate that QA criteria predict customer satisfaction. If they don't, revise criteria.

### Evaluators score the same ticket differently
**Cause**: Criteria are ambiguous or subjective
**Solution**: Use binary (yes/no) criteria instead of scaled (1-5). Run calibration sessions monthly — have 3+ evaluators score the same 5 tickets independently, then discuss disagreements. MaestroQA has built-in calibration tools for this.

### Coaching sessions feel disconnected from QA data
**Cause**: QA scores sit in MaestroQA dashboards but coaching happens in a separate tool or ad-hoc
**Solution**: Use MaestroQA's coaching workflows — low scores automatically trigger coaching sessions with specific call segments linked. Use reverse-ETL to push coaching tasks to Slack or your CRM so supervisors see them in their workflow.