Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PANGKAIFENG/ai-product-manager-skills --skill ai-work-assetization-diagnosergit clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/ai-work-assetization-diagnoser)<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/ai-work-assetization-diagnoser"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/ai-work-assetization-diagnoser/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/ai-work-assetization-diagnoser"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/ai-work-assetization-diagnoser.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 148 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.01676 |
| Opus 5 | $0.00069 | $0.00838 |
| Sonnet 5 | $0.00028 | $0.00335 |
| Haiku 4.5 | $0.00014 | $0.00168 |
Grade A, and why
ai-work-assetization-diagnoser scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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：
- 这段 AI 工作是否值得沉淀。
- 如果值得，最小有用资产层是什么。
- 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
-
Identify input shape
- AI conversation
- repeated manual task
- prompt or prompt pack
- team workflow
- successful delivery trace
- failed or over-engineered asset proposal
-
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
-
Score only what matters
- repeatability
- input stability
- output stability
- validation clarity
- context dependency
- human judgment dependency
- failure cost
- reuse audience
-
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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 180 lines · 139 tokens per session scan A fafe320bf923
ai-work-assetization-diagnoser is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (11 stars, last pushed 13d ago), licensed MIT. It adds 139 tokens to every session and 1,676 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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