Ai Work Assetization Diagnoser

AI 工作资产化诊断器

中文速查

• 中文名：AI 工作资产化诊断器 / 资产化路由器
• 英文稳定名：ai-work-assetization-diagnoser
• 分类：Skill/Agent 治理
• 你可以这样叫我：这段工作是不是值得做成 Skill、这个 prompt 应该沉淀成 workflow 还是 Skill、帮我判断该资产化到哪层、这个 AI 工作流要不要做成 Loop
• 适合：判断一段可重复 AI 工作应该沉淀到哪个资产层，给出最小下一步 artifact 和验证信号。
• 不适合：直接创建 Skill、直接实现自动化系统、普通日志根因定位、一次性事实查询、没有复用价值的闲聊。

Overview

这个 Skill 是 Router / Gate，不是执行器。它回答三个问题：

1. 这段 AI 工作是否值得沉淀。
2. 如果值得，最小有用资产层是什么。
3. 为什么不是相邻层级。

默认输出应短、可执行、有证据。不要把所有重复任务都升级成 Skill，也不要把所有自动化想法都升级成 Loop。

Asset Layers

| Layer | Use When | Example Artifact | | --- | --- | --- | | Do Not Assetize | 低频、一次性、强主观、输入不可稳定复用、风险高或验收口径不存在。 | 保留聊天记录或一次性笔记。 | | Prompt | 步骤简单，主要复用表达方式。 | Prompt template, checklist prompt. | | Context Pack | 关键难点是资料、约束、样例和反例组织。 | Context folder, source bundle, glossary. | | Workflow | 有稳定步骤、角色、输入输出和人工推进点。 | SOP, runbook, workflow doc. | | Skill | 高频可复用，有明确触发语、输入、输出、边界和验收方式。 | SKILL.md + references/scripts. | | Loop | 需要多轮状态、恢复、触发器、检查点、重试或人工接管。 | Loop contract, state files, update log. | | System | 多个 Skill/Loop/Agent 组合，涉及权限、成本、审计、评估或团队级运行。 | Product/system PRD, architecture plan. |

Workflow

1. Identify input shape

• AI conversation
• repeated manual task
• prompt or prompt pack
• team workflow
• successful delivery trace
• failed or over-engineered asset proposal

1. Extract evidence

• user goal and business context
• input materials and constraints
• repeated steps
• output artifact
• human decision points
• validation or acceptance criteria
• frequency and reuse audience
• state, retry, handoff, or automation needs

1. Score only what matters

• repeatability
• input stability
• output stability
• validation clarity
• context dependency
• human judgment dependency
• failure cost
• reuse audience

1. Recommend one primary asset layer

• Give the smallest layer that would create real reuse.
• Explain why the lower layer is insufficient.
• Explain why the higher layer is overkill.
• Include do-not-assetize if that is the best answer.

1. Name the smallest next artifact

• Prompt: one reusable prompt with slots.
• Context Pack: file list and required metadata.
• Workflow: step list and handoff format.
• Skill: trigger description, non-triggers, output contract, references.
• Loop: state files, stop conditions, Human Gate.
• System: PRD / architecture / eval plan before implementation.

1. Define reuse signal

• What would prove this asset is worth deepening?
• What failure signal should stop further investment?

Output Contract

Use this structure:

## Assetization Diagnosis

- Current shape: 
- Recommended layer: 
- Confidence: 

## Evidence
- ...

## Why This Layer
- ...

## Why Not Adjacent Layers
- Lower layer is insufficient because ...
- Higher layer is overkill because ...

## Smallest Next Artifact
- ...

## Reuse Signal To Watch
- ...

## Do Not Do
- ...

For batch scenarios, use a compact table with columns:

• Scenario
• Current shape
• Recommended layer
• Evidence
• Smallest next artifact
• Reuse signal
• Priority

Boundary Rules

• If the user asks to create the actual Skill after diagnosis, hand off to skill-creator.
• If the user asks whether an idea is a good product, use ai-collaboration-calibration or decision-research.
• If the user asks to turn a product idea into a PRD, use prd-architect.
• If the user asks to review an existing Skill, use skill-reviewer when available.
• If the user provides trace/logs and wants root cause, use a trace/debug skill, not this one.
• If the work is high responsibility, such as medical, legal, financial, safety, hiring, or compliance decisions, recommend human review and avoid closed-loop automation.

Non-Assetization Cases

Recommend not assetizing, or only a lightweight note, when:

• The task happened once and is unlikely to recur.
• The input varies so much that templates would mislead.
• The output depends on taste or senior judgment with no stable rubric.
• The cost of a wrong answer is high and no verification exists.
• The user cannot name a reuse audience.
• A simpler checklist would solve the problem.

Definition Of Done

• The recommendation is one primary asset layer, not a vague list.
• Adjacent layers are explicitly accepted or rejected.
• The next artifact is small enough to create in one focused pass.
• The answer includes a reuse or failure signal.
• The diagnosis does not expose private context unless the user supplied and authorized it in this run.

Resources

• references/asset-layer-rubric.md：当 Prompt / Context Pack / Workflow / Skill / Loop / System 边界不清时读取。
• scripts/check_assetization_report.py：检查资产化诊断报告是否包含推荐层级、证据、最小下一步和复用信号。

Evaluation

Smoke prompts:

• 这段工作是不是值得做成 Skill？
• 这个 prompt 应该沉淀成 workflow 还是 Skill？
• 我们连续三次做了类似 AI 协作，帮我判断该资产化到哪层。
• 这只是一次性任务，不要过度沉淀。

Non-trigger prompts:

• 直接帮我创建一个 Skill。
• 看下这个 trace，定位根因。
• 帮我写一个 PRD。
• 查一下今天的新闻。

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: PANGKAIFENG
• Source: PANGKAIFENG/ai-product-manager-skills
• License: MIT

Install and usage instructions live in the source repository linked above.