C-Level Advisory PostmortemAnalysisLearning

Executive Mentor — Postmortem

Honest analysis of what went wrong and why.

$skillfish add ~skills/executive-mentor-postmortem

Real Conversations

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

You

We launched our enterprise tier 3 months ago after 9 months of development. We have 2 customers on it (target was 12). The feature set is solid but we priced at $48K ACV without doing enough customer discovery. Sales team resisted it from day one — they said 'customers won't pay that.' I ignored them. Help me do an honest postmortem.

Agent

A good postmortem separates causation from correlation and identifies system failures, not just the final mistake. Let me work through this with you.

What actually happened (the causal chain):

  • 9 months of development without customer validation of price point
  • Sales team flagged resistance; you overrode it
  • Launched at $48K ACV; market accepted 2 of 12

But the root cause is earlier. When did you set the $48K price? What was the logic? If it was margin math ("we need $48K to make the economics work"), that's a pricing-from-cost error. If it was competitive benchmarking ("Ironclad charges $60K"), that ignores your brand position as a newer entrant. If it was investor pressure ("you should be at enterprise price points"), that's strategy captured by optics.

The sales team signal: This is the part that stings most in a good postmortem. The sales team told you the market wouldn't pay that price. You had a data point from the people closest to the market, and you discounted it. Why? Possible answers:

  • You didn't trust their judgment on strategic pricing (understandable but needs examination)
  • You thought they were afraid of selling premium (possible, but did you test it?)
  • You needed the $48K price to make the business model work and didn't want to confront what $24K ACV meant for your unit economics

The honest question: If you'd done 10 customer discovery calls about willingness-to-pay before finalizing the price, what would you have heard?

System fixes (not just individual lessons):

  1. Price validation before development commitment, not after
  2. A formal "war game" where sales presents their strongest objection case to any new product initiative
  3. A definition of what constitutes enough customer signal to override sales pushback

The 2 customers you have: That's your beachhead. What do they have in common? Build the enterprise motion around those profiles.

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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

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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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