Agent skill

engineering-chatbot-demo

GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture

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Install this agent skill to your Project

npx add-skill https://github.com/vamseeachanta/workspace-hub/tree/main/.claude/skills/business/client-demo/engineering-chatbot

SKILL.md

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

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