Score 25 anti-patterns from logs and memory to flag execution drift.
npx skills add https://github.com/CodeAlive-AI/ceo-ai-os --skill anti-pattern-audit
Please help me install this Agent Skill. Skill: anti-pattern-audit Source: https://github.com/CodeAlive-AI/ceo-ai-os/tree/main/skills/anti-pattern-audit Command: npx skills add https://github.com/CodeAlive-AI/ceo-ai-os --skill anti-pattern-audit
This Skill helps founders and CEOs identify self-deception, weak assumptions, and execution mistakes before they become expensive operating problems.
Ask the skill to review the past month’s logs, memory state, and weekly reviews, then generate the anti-pattern audit in the required scorecard format.
High-intent search queries and answers about installing and using this skill.
To conduct a founder self-audit, score 25 common anti-patterns across strategy, sales, metrics, growth, and AI adoption. The audit grounds judgments in daily logs and weekly reviews to produce a scorecard that flags critical risks and prioritizes corrective actions.
An anti-pattern audit is a monthly CEO review process that scores 25 common founder mistakes to surface weak assumptions and execution drift. You should run one during your monthly retrospective to catch operating problems before they scale into expensive mistakes.
Review past monthly logs, memory state, and weekly reviews to generate a founder scorecard by applying structured scoring to 25 anti-patterns. This produces evidence-based judgments, flags critical risks, and outputs a prioritized action list for the next month.
Yes, you can use a self-audit to catch AI adoption mistakes and growth strategy errors because the 25 anti-patterns evaluated specifically cover strategy, sales, metrics, growth, and AI-era execution drift. It identifies weak assumptions across these areas to prevent operating problems.
The best way to identify founder blind spots is an evidence-based review that scores self-deception patterns using your daily logs and memory state. This generates a clean scorecard flagging critical risks and recommending top corrective actions before issues scale.
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