engineering-chatbot-demo

name	engineering-chatbot-demo
version	1.0.0
category	business/client-demo
description	GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture
type	reference
capabilities	["system_prompt_design","demo_script_builder","calculation_template_builder","knowledge_base_structuring","pilot_feedback_capture","chatbot_pitch_delivery"]
requires	[]
trigger	manual

Engineering Chatbot Demo Skill

Full GTM workflow for presenting engineering AI chatbots to clients — covers system prompt design through ROI capture. Reusable template for any new engineering discipline.

System Prompt Template

Produce discipline-specific system prompts in this order:

1. Role definition — "You are a senior [discipline] engineer with X years of [domain] experience"
2. Core competencies — domain-specific bullet list (6–10 items)
3. Primary codes & standards — with version years (e.g. API 2A-WSD 22nd Ed. 2014)
4. Calculation capabilities — formula notation, accepted inputs/outputs
5. Persona and Tone — precision · practicality · caution · transparency
6. Known Limitations — hallucination risk, no proprietary data, no software execution
7. Standard disclaimer — "Outputs are preliminary engineering estimates requiring QA review"

Demo Script Builder (15–20 min flow)

Phase	Duration	Content
Hook	2 min	Live lookup: "What does API 2A say about pile fatigue?"
Calculation	5 min	Step-by-step calc: formula → substitution → result → acceptance check
Data processing	4 min	Paste inspection data → AI generates corrosion rate summary table
Document gen	4 min	AI drafts scope of work or memo from bullet points
Q&A	5 min	Open questions; capture objections

Calculation Template Format

### [Calc Name]
**Code ref:** API/DNV/ISO clause X.Y.Z
**Formula:** σ = F / A
**Inputs:** F = [value] kN, A = [value] m²
**Result:** σ = [value] MPa
**Acceptance:** σ ≤ F_y / 1.67 = [value] MPa → PASS/FAIL

Knowledge Base Structuring

Structure markdown KB files for reliable AI citation:

• Top-level ## headings per topic (AI retrieves by heading)
• Tables for code values (yield strengths, load factors, limits)
• Numbered clauses matching source document numbering
• > Note: callouts for exceptions or applicability limits

Pilot Feedback Capture

After each demo session record:

• Time savings estimate: "Task X took Y hours; AI did it in Z minutes"
• Q&A log: questions asked + AI answer quality (Good / Needs refinement / Wrong)
• Objections: capture verbatim; map to rebuttal
• ROI metric: hours saved × billable rate / demo session cost

Chatbot Pitch Delivery

Tier	Description	Price signal
T1	Read-only assistant (Q&A, code lookups)	Project-based
T2	T1 + calculation templates + doc generation	Retainer
T3	T2 + custom KB + pilot + 3-month support	Enterprise

Objection handling:

• "It hallucinates" → Show disclaimer; position as senior-engineer-reviewer tool, not replacement
• "Our data is proprietary" → Explain no-training policy; local-deploy option (T3)
• "Too expensive" → Anchor to billable hours saved in pilot log