feat: 4-week AI Initiative series — plan + skeleton presentations
Week 1: The AI Multiplier (MCP + Skills) Week 2: Making AI Actually Work (CLAUDE.md) Week 3: Real Stories, Real Results Week 4: What Could You Build? (Interactive) Skeleton decks in presenterm format. PLAN.md has full series architecture, structure, speaker notes, and open decisions.
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week1-mcp-skills/presentation.md
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week1-mcp-skills/presentation.md
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The AI Multiplier
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MCP + Skills in Action
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<!-- speaker_note: Welcome everyone. Today we're going to talk about what happens when you stop being the middleware between AI and your systems. -->
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The Copy-Paste Loop
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===
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Sound familiar?
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```mermaid +render +width:100%
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sequenceDiagram
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You->>LLM: I'm 10 minutes away from a demo, getting this error
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LLM->>You: Try this...
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You->>LLM: That didn't work! New error!
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LLM->>You: Gather this info..
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You->>LLM: Here it is...
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LLM->>You: Try this...
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loop
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You->>LLM:😤
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LLM->>You:😭
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end
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```
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**You** are the middleware. You copy. You paste. You translate.
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The AI is blind, deaf, and handless. You're doing all the work.
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<!-- speaker_note: Walk through the frustration. Everyone in this room has lived this. -->
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<!-- speaker_note: The AI is compelling you to work FOR it — gathering context, running commands, reporting back. -->
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<!-- speaker_note: Complex problems need a lot of information, iteration and context. One narrow bandwidth interface doesn't cut it. -->
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<!-- font_size: 4 -->
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MCP: Giving AI Hands
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===
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<!-- pause -->
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**Model Context Protocol** — open standard, created by Anthropic (2024)
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<!-- pause -->
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One sentence: **MCP gives tools to AI.**
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```mermaid +render +width:100%
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sequenceDiagram
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You->>LLM: I'm 10 minutes away from a demo, getting this error
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LLM->>You: Let me check that..
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LLM->>OCP: Get pod list
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LLM->>OCP: Get pod logs
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LLM->>ArgoCD: Get GitOps application
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LLM->>Git: Get Git repo
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LLM->>LLM: Plan fix
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LLM->>You: Here is the plan. Requesting Approval
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You->>LLM: Approved
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LLM->>Git: Push fix to Git
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LLM->>OCP: Verify change applied
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LLM->>You: Applied Successfully ✅
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```
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**You** approve the plan. **AI** does the legwork.
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<!-- speaker_note: Quick walk through the sequence diagram -->
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<!-- speaker_note: MCP gives the LLM agency to carry out complex, multi-step plans -->
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<!-- speaker_note: Collecting and collating from multiple sources -->
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<!-- speaker_note: Taking actions, iterating and trying different paths when necessary -->
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<!-- speaker_note: Key point: you're still in control. The AI proposes, you approve. -->
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What Can MCP Connect To?
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===
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Anything with an API. Today, in this room, we have:
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<!-- incremental_lists: true -->
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* **OpenShift / Kubernetes** — full cluster operations
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* **Git** (Gitea, GitHub, GitLab) — repos, commits, PRs
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* **ArgoCD** — GitOps application management
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* **SecureTransport** — admin API for file transfer flows
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* **Databases, monitoring, CI/CD, ticketing...**
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There are 10,000+ MCP servers available. And you can build your own.
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<!-- speaker_note: Don't dwell on the list. The point is: if it has an API, MCP can bridge it. -->
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<!-- speaker_note: The MCP ecosystem is exploding. The standard is winning. -->
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Skills: Giving AI Expertise
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===
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MCP gives AI **tools**. Skills give AI **knowledge of how to use them well.**
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```mermaid +render +width:100%
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graph LR
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subgraph WITHOUT["❌ Without Skills"]
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direction TB
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BP["System Prompt<br/>20,000+ tokens<br/>Everything, always"]
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end
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subgraph WITH["✅ With Skills"]
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direction TB
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AP["System Prompt<br/>~500 tokens"]
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AP -->|"task matches"| L1["Load: Healthcheck<br/>+2,000 tokens"]
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end
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style WITHOUT fill:#ffcccc,stroke:#cc0000
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style WITH fill:#ccffcc,stroke:#00cc00
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style BP fill:#ff6b6b,stroke:#333,color:#fff
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style AP fill:#51cf66,stroke:#333,color:#fff
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style L1 fill:#4a9eff,stroke:#333,color:#fff
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```
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**Lazy-loaded expertise.** Scan descriptions → load on demand → unload when done.
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<!-- speaker_note: The problem with stuffing everything into a system prompt: context pollution, cost, degraded quality. -->
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<!-- speaker_note: Skills = modular SOPs. The AI loads only what it needs, when it needs it. -->
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A Skill is an SOP
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===
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**Without Skill** 🎲
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> "Check if my server is secure"
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- Runs random checks from training data
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- Misses half the important ones
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- Suggests changes that break your infra
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**With Skill** 📋
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> "Check if my server is secure"
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- Loads healthcheck SKILL.md
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- Follows structured audit checklist
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- Firewall → SSH → Updates → Services
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- Prioritised report with specific fixes
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**Your best engineer writes it once. Every agent benefits, forever.**
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People leave. Skills persist.
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<!-- speaker_note: This is the line that lands with the old guard. -->
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<!-- speaker_note: Think about tribal knowledge in your team. What happens when that person goes on leave? Changes roles? Leaves the company? -->
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<!-- speaker_note: Skills are institutional memory that actually works. -->
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Tools + Skills + Identity
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===
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```mermaid +render +width:100%
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graph LR
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T["🔧 Tools (MCP)<br/>What can I do?"] --> A["🤖 Agent"]
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SP["🧠 Identity (System Prompt)<br/>Who am I?"] --> A
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SK["🎯 Skills<br/>How do I do X well?"] --> A
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style T fill:#ff6b6b,stroke:#333,color:#fff
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style SP fill:#ffd93d,stroke:#333,color:#000
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style SK fill:#4a9eff,stroke:#333,color:#fff
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style A fill:#51cf66,stroke:#333,color:#fff
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```
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A workshop full of power tools, no training? Dangerous.
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Power tools + SOPs + a qualified operator? **Productive.**
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<!-- speaker_note: This is the summary slide before the demo. Three layers working together. -->
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DEMO
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===
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Watch the AI. Watch me. Neither of us needs the other.
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<!-- speaker_note: Three demos. Progressively impressive. -->
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<!-- speaker_note: Demo 1: Cluster health check via MCP -->
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<!-- speaker_note: Demo 2: Skill-driven task (healthcheck or equivalent) -->
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<!-- speaker_note: Demo 3: The "figure it out" moment — AI adapts to something unexpected -->
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<!-- speaker_note: IMPORTANT: While AI is working, visibly do something else. The multiplier makes itself. -->
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What You Didn't See
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===
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* Every command the AI ran (and there were dozens)
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* Every error it hit and recovered from
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* Every tool it chose — and the ones it considered and rejected
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* The context it carried across steps without being reminded
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**That's the loop flip.**
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You used to do all of that. Now you approve the plan.
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<!-- speaker_note: This is the "what just happened" debrief after the demo. -->
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<!-- speaker_note: The audience saw the AI work. Now name what they DIDN'T see. -->
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The 3x Multiplier
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===
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**1x**
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You, working.
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The baseline.
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**2x**
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You work + AI works.
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Parallel output.
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**3x**
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Parallel work + AI adds
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value you didn't ask for.
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**Compounded output.**
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The person leveraging AI will outcompete the person who isn't.
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This isn't a threat. It's an opportunity you're choosing to take — or not.
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<!-- speaker_note: Don't make this scary. Make it exciting. The opportunity framing matters. -->
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<!-- speaker_note: "Someone using AI will outperform someone who isn't" — that's the honest truth. -->
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Next Week
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===
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You just saw what's possible.
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Next Friday: **how to get this for yourself.**
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CLAUDE.md, system prompts, project context — the practical playbook
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for making AI actually understand your work.
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🎯
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<!-- speaker_note: Tease Week 2. Keep it short. Leave them wanting more. -->
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<!-- end_slide -->
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Reference in New Issue
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