MaestroQA Platform Help
Step 1 — Gather context
If references/learnings.md exists, read it first for accumulated platform knowledge.
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
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
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.
1---2name: sales-maestroqa3description: MaestroQA platform help — conversation data QA platform with customizable scorecards, AI-powered coaching workflows, conversation analytics (AskAI), reverse-ETL to CRM/Slack/data warehouses, CSAT ingestion, chatbot QA monitoring. 60+ integrations (Zendesk, Freshdesk, Salesforce, Intercom, ServiceNow, Kustomer, Front, Gorgias, Gladly, Khoros Care, Five9, Talkdesk, Amazon Connect, NICE, Aircall, Gong, Zoom, Dialpad, Snowflake, BigQuery, Redshift). Rippit API (app.rippit.com/api/v1, token auth, 10 req/s). Use when QA scorecards not catching the right issues, coaching workflows feel disconnected from QA data, grading data export to warehouse, CSAT scores not correlating with QA scores, setting up MaestroQA integrations with helpdesk or phone system, MaestroQA API automation, or evaluating MaestroQA vs Observe.AI or Enthu.AI or Klaus. Do NOT use for CCaaS platform selection (use /sales-ccaas-selection) or real-time agent coaching during calls (use /sales-balto).4license: MIT5---6
7# MaestroQA Platform Help
8
9## Step 1 — Gather context
10
11If `references/learnings.md` exists, read it first for accumulated platform knowledge.
12
131. **What do you need?**
14 - A) Set up or refine QA scorecards
15 - B) Build coaching workflows from QA data
16 - C) Configure conversation analytics / AskAI
17 - D) Integrate with helpdesk, phone system, or CRM
18 - E) Export data to warehouse or via API
19 - F) Monitor AI chatbot quality
20 - G) Ingest CSAT scores and correlate with QA
21 - H) Other
22
232. **Current setup?**
24 - A) New to MaestroQA (not yet connected)
25 - B) Connected to helpdesk, building scorecards
26 - C) Running QA, want to improve coaching
27 - D) Mature — need analytics, API, or warehouse export
28
293. **Helpdesk / phone system?**
30 - A) Zendesk / Freshdesk / Intercom / Front / Gorgias
31 - B) Salesforce Service Cloud / ServiceNow / Dynamics 365
32 - C) Five9 / Talkdesk / Amazon Connect / NICE / Genesys
33 - D) Aircall / RingCentral / Dialpad / Twilio / Vonage
34 - E) Other / multiple
35
36Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.
37
38## Step 2 — Route or answer directly
39
40| Problem domain | Route to |
41|---|---|
42| Comparing QA tools (MaestroQA vs Observe.AI vs Enthu.AI) | `/sales-coaching` for cross-platform comparison |
43| Choosing a CCaaS platform | `/sales-ccaas-selection` |
44| Real-time agent coaching during calls | `/sales-balto` or `/sales-cresta` |
45| Help desk platform selection | `/sales-helpdesk-selection` |
46| Salesforce-specific questions | `/sales-salesforce` |
47| Sales call review and coaching | `/sales-call-review` |
48
49When routing, provide the exact command: "This is a {problem domain} question — run: `/sales-{skill} {user's original question}`"
50
51## Step 3 — MaestroQA platform reference
52
53**Read `references/platform-guide.md`** for the full platform reference — modules, pricing, integrations, data model, workflows.
54
55Answer the user's question using only the relevant section. Don't dump the full reference.
56
57## Step 4 — Actionable guidance
58
59Focus on the user's specific situation.
60
61- **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.
62- **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.
63- **Analytics adoption**: Start with AskAI for ad-hoc queries before building custom dashboards. AskAI answers natural language questions about conversation data without needing SQL.
64- **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.
65- **Chatbot QA**: Connect Ada, Sierra, Agentforce, or Forethought to grade bot conversations with the same scorecards used for human agents.
66
67If you discover a gotcha, workaround, or tip not covered in `references/learnings.md`, append it there.
68
69## Gotchas
70
71> *Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.*
72
73- **AI features cost extra.** MaestroQA's AI-powered analytics and conversation intelligence are add-ons — budget separately from the base QA subscription.
74- **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.
75- **API tokens expire every 90 days** by default. Set up a rotation process or disable expiry for long-lived integrations.
76- **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.
77- **Dashboard customization is limited.** For advanced analytics, export to Snowflake/BigQuery and use external BI tools.
78- **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.
79- **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.
80
81## Related skills
82
83- `/sales-coaching` — Sales coaching and training strategy across all QA and enablement platforms
84- `/sales-observe-ai` — Observe.AI platform help (enterprise contact center QA with Auto QA, Agent Copilot)
85- `/sales-enthu` — Enthu.AI platform help (affordable contact center QA, fast setup)
86- `/sales-balto` — Balto platform help (real-time AI guidance during calls)
87- `/sales-cresta` — Cresta platform help (enterprise contact center AI)
88- `/sales-convin` — Convin platform help (contact center QA + coaching + LMS)
89- `/sales-ccaas-selection` — Choosing a CCaaS platform
90- `/sales-helpdesk-selection` — Choosing a help desk platform
91- `/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`
92
93## Examples
94
95### Example 1: Set up QA scorecards for a support team
96**User says**: "We just connected Zendesk — how do I build QA scorecards for our support team?"
97**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.
98
99### Example 2: Export QA data to Snowflake
100**User says**: "I need to get our grading data into Snowflake for custom reporting"
101**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.
102
103### Example 3: Correlate CSAT with QA scores
104**User says**: "We want to see if our QA scores actually predict customer satisfaction"
105**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.
106
107## Troubleshooting
108
109### QA scores don't correlate with customer satisfaction
110**Cause**: Scorecards measure process compliance (did the agent follow the script) but not outcome quality (was the customer actually helped)
111**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.
112
113### Evaluators score the same ticket differently
114**Cause**: Criteria are ambiguous or subjective
115**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.
116
117### Coaching sessions feel disconnected from QA data
118**Cause**: QA scores sit in MaestroQA dashboards but coaching happens in a separate tool or ad-hoc
119**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.
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