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Use when building AI-native products where user data can fine-tune performance, when static software fails to improve with usage, or when designing products that learn from interaction
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when designing AI agent products, defining roadmaps for agentic workflows, or evaluating how to evolve AI from passive tool to proactive partner in software development
Shift from rigid roles (PM, Designer, Engineer) towards a model where individuals use AI to bridge skill gaps, enabling smaller and flatter teams. Use in early-stage startups or innovation units where speed is critical.
Use when designing AI agents or chatbots, when raw model outputs feel like a black box, or when building natural language interfaces that need structure and trust
Use when building AI-powered products or agents, when raw model intelligence isn't enough to solve user problems, or when designing the architecture for agentic workflows
View yourself as an entity with infinite dimensions rather than a single good/bad identity. Strengths and weaknesses are often the same trait in different contexts. Use during performance reviews or when receiving tough feedback.
Review your calendar, color-code activities by energy impact (Green/Red), and systematically delegate draining tasks to maximize time in your Zone of Genius.
| name | organism-conversion-loop |
| description | Use when building AI-native products where user data can fine-tune performance, when static software fails to improve with usage, or when designing products that learn from interaction |
A shift from treating product as a static "artifact" to a living "organism" that improves with usage. The core mechanism is a metabolism that ingests data and digests rewards to autonomously improve outcomes.
Core principle: What is the metabolism of a product team to ingest data and improve output?
┌─────────────────────────────────────────────────────────────────┐
│ │
│ ┌───────────────┐ │
│ │ INGEST │◄───────────────────────────────┐ │
│ │ Interaction │ │ │
│ │ Data │ │ │
│ └───────┬───────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌───────────────┐ │ │
│ │ DIGEST │ │ │
│ │ via Rewards │ │ │
│ │ Model │ │ │
│ └───────┬───────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌───────────────┐ │ │
│ │ OPTIMIZE │ │ │
│ │ Outcome │ │ │
│ └───────┬───────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌───────────────┐ │ │
│ │ DEPLOY & │────────────────────────────────┘ │
│ │ OBSERVE │ │
│ └───────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
| Principle | Description |
|---|---|
| Living entity | Product is organism, not artifact |
| Metabolism design | Rate of data ingestion matters |
| Rewards model | RLHF/Fine-tuning steers outcomes |
| Loop focus | Ingestion → Improvement → Deployment |
Source: Asha Sharma (Microsoft AI Platform VP) via Lenny's Podcast