engineering-chatbot-demo
GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture
Best use case
engineering-chatbot-demo is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture
Teams using engineering-chatbot-demo should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/engineering-chatbot/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How engineering-chatbot-demo Compares
| Feature / Agent | engineering-chatbot-demo | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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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