WhatsApp Chatbot Audit Lite — Intent Gaps + Fix Order
Process-only WhatsApp chatbot audit: intent coverage gaps, handoff/UX risks, and a prioritized fix order. Evidence-first for agents — no invented conversion or reply-rate claims.
$5
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Example session with this skill installed
business: Real Estate Agency
channel: WhatsApp
goal: Qualify leads for rental viewings
artefacts: Welcome message, 4-step qualification flow, fallback message
integrations: Google Sheets (user-stated)
- Read your context and instructions
- Compiled the whatsapp chatbot audit
- Generated the document
I have completed the audit for the Real Estate Agency lead qualification bot. The report highlights a critical P0 risk regarding the lack of a human handoff trigger and several P1 improvements for data collection.
Chatbot audit: Real Estate Agency
Channel: WhatsApp
Stated goal: Qualify leads for rental viewings
Evidence reviewed: Welcome message, 4-step qualification flow, fallback message
Generated: 2024-05-20 · Draft for human verification
Executive summary
The bot successfully captures basic lead data but lacks a clear exit path. The biggest risk is the "dead-end" fallback which may lead to user churn if the bot fails to understand a specific query. Recommended next step: Define a manual escalation path for non-standard questions.
Findings
| ID | Priority | Area | Evidence | Impact / risk | Recommended fix |
|---|---|---|---|---|---|
| F-01 | P0 | Handoff | No mention of human agent in flow | Users get stuck if the bot fails | Add "Speak to Agent" keyword trigger |
| F-02 | P1 | Data | Asking for phone number on WhatsApp | Redundant friction | Remove phone prompt; use sender ID |
| F-03 | P2 | Tone | Formal "Bot" greeting | Low engagement | Soften language to match agency brand |
Conversation coverage
| User intent | Covered? | Current path / evidence | Gap or ambiguity | Test example |
|---|---|---|---|---|
| Check availability | Yes | 4-step flow | No real-time DB check | "Is the Smith St flat still open?" |
| Book viewing | Partial | Google Sheets export | No confirmation message | "I want to see it tomorrow" |
| Cancel viewing | No | Not supplied | No path to revoke | "I can't make it anymore" |
Handoff and recovery checks
- Human escalation trigger: missing
- Destination and owner: Not supplied — confirm
- Hours or SLA: Not supplied — confirm
- Fallback and error copy: revise (generic "I don't understand")
- Opt-out / stop path: missing
Prioritised action plan
P0 — before usable testing
- Map the "Speak to Agent" intent to a notification for the office team.
- Implement a "STOP" command for GDPR compliance.
P1 — next iteration
- Integrate a confirmation message once data is sent to Google Sheets.
- Clarify operating hours in the fallback message.
Test cases
| Case | User message | Expected behaviour | Result / owner |
|---|---|---|---|
| Happy path | "I want a flat" | Starts 4-step qualification | QA Team |
| Ambiguous request | "Is it near a park?" | Triggers fallback / Agent path | QA Team |
| Opt-out | "Stop" | Confirm data deletion/stop | Dev Team |
Next steps
- Confirm the destination for lead data (Google Sheets vs CRM).
- Draft a specific "Human Handoff" message for out-of-hours requests.
- Review the legal requirement for data retention on the captured lead info.
whatsapp-chatbot-audit-lite-intent-gaps-.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Chatbot deployments often fail because of broken fallback loops, missing handoff triggers, or poor intent coverage. This skill provides a systematic audit framework to identify friction points before they impact your customer satisfaction scores.
What it does
- Intent gap analysis identifies missing or ambiguous user paths in your current bot logic.
- Handoff validation checks for clear escalation paths to human agents and operating hour transparency.
- Evidence-led reporting separates confirmed bot capabilities from unverified assumptions and missing data.
- Prioritised issue tracking categorises findings into P0 blockers, P1 high-friction risks, and P2 polish items.
- Test case generation creates a structured plan for happy paths, edge cases, and opt-out requests.
How it works
- Input your business context, channel, bot goal, and any existing artefacts like transcripts or flow exports.
- The skill cross-references your inputs against conversational best practices for WhatsApp and Instagram.
- It generates a structured markdown report covering coverage gaps, data hygiene, and recovery checks.
- You receive a prioritised action plan and a ready-to-use test suite for your QA team.
Frameworks & tools
Works with chatbot flows from platforms like ManyChat, Typebot, Landbot, or custom LLM-based agents. It evaluates channel-specific constraints for WhatsApp Business API, Telegram, Messenger, and Instagram.
Why this beats prompting it yourself
Writing a consistent audit requires a mental checklist of dozens of edge cases like fallback loops and data minimisation. This skill enforces a rigorous structure that ensures you never miss a missing opt-out path or a broken handoff trigger, providing a professional deliverable for clients or internal stakeholders.
Use cases
- Audit a new WhatsApp FAQ bot before it goes live to customers.
- Review existing customer support transcripts to find where the bot is failing to resolve queries.
- Evaluate a third-party chatbot proposal against actual business requirements.
- Standardise the QA process for a suite of multi-channel messaging bots.
Known limitations
This tool provides process guidance and usability reviews only. It does not perform technical API testing, security penetration testing, or provide legal certification for GDPR/CCPA compliance.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 2
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- 3
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