C-Level Advisory BoardPreparationMentoring

Executive Mentor — Board Prep

Board meeting preparation and rehearsal framework.

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Real Conversations

See how the Executive Mentor — Board Prep skill responds to actual requests. These are representative examples of what your AI agent will produce.

You

Board meeting Friday. We missed the MRR target by 22% ($310K vs $400K target). Churn spiked to 11% this quarter, up from 6%. I have one board member who goes straight to 'what's the plan' mode before I finish explaining the situation. How do I prepare for this?

Agent

A bad-numbers board meeting requires a different preparation than a good-numbers meeting. Here's the structure that works.

The narrative architecture for a miss: Don't lead with the miss. Lead with what you know to be true — the diagnosis. Boards hate hearing numbers without explanation. They feel like they're getting the situation managed rather than the situation. The structure is:

  1. What happened (1 slide, factual): actual vs. plan, the specific drivers of the miss (e.g., 2 large deals slipped, churn concentrated in one cohort)
  2. Why it happened (1 slide, honest): root cause analysis. If you don't know yet, say that and tell them when you will.
  3. What you're doing about it (2 slides): specific actions with owners and dates, not themes
  4. What you need from the board (1 ask): specific, not "support"

Handling the "what's the plan" board member: Name the pattern before the meeting, in writing (pre-read email): "I'm sending this pre-read because I want the meeting focused on decision-making, not information transfer. My ask is that we hold diagnosis discussion until slide 4." This primes him to let you finish.

In the meeting, use a redirect: "I want to make sure the plan I present actually addresses root cause — can I take 90 seconds to confirm we're aligned on diagnosis first?" This isn't defensive — it's process.

On the churn spike specifically: 11% quarterly churn (annualizing to ~37%) is the number your board will fixate on. Come with cohort data — is this concentrated in a specific segment, acquisition channel, or tenure band? A concentrated churn problem is very different from a diffuse one and each requires a different plan.

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Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase. Web-based AI can use the knowledge and frameworks, but won't have tool access.

Models & Context

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Recommended Models

Best
Claude Opus 4 Claude Sonnet 4 GPT-4.1 Gemini 2.5 Pro Grok 3 Kimi K2
Good
Claude Haiku 4.5 GPT-4.1 mini Gemini 2.5 Flash Grok 3 mini

Larger models produce more detailed, production-ready outputs.

Context Window

This skill's SKILL.md is typically 3–10 KB — fits in any modern context window.

8K Skill only
32K+ Skill + conversation
100K+ Skill + references + codebase

All current frontier models (Claude, GPT, Gemini) support 100K+ context. Use the full window for complex multi-service work.

Pro tips for best results

1

Be specific

Include numbers — users, budget, RPS — so the skill can size the architecture.

2

Share constraints

Compliance needs, team size, and existing stack all improve the output.

3

Iterate

Start with a high-level design, then ask follow-ups for IaC, cost analysis, or security review.

4

Combine skills

Pair with companion skills below for end-to-end coverage.

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