Skill: cellinlab/cell-benchmark-filter (version 0.1.0)

--- name: benchmark-filter description: Benchmark filtering for Chinese creator, OPC, and one-person-business work. Use when Codex needs to judge whether a person, creator, or business is actually worth studying; separate business signal from vanity signal; decide what layer is worth copying; and recommend whether to stop at a shortlist or hand the target to $opc-case-research for deeper study. metadata: {"openclaw":{"homepage":"https://github.com/cellinlab/cell-skills/tree/main/skills/benchmark-filter"}} --- # Benchmark Filter ## Overview Use this skill when the user needs help choosing who to study, who to copy from, or whether an existing benchmark is actually useful. This skill does not do a full case study. It filters first. The core job is to answer: - is this benchmark worth learning from - what exactly is worth learning - what should not be copied - should we stop here or move into deeper case research ## Quick Start 1. Clarify whether the user needs a shortlist or a judgment on one existing benchmark. 2. Identify the user's real learning target: content, offer, channel, conversion, positioning, or business model. 3. Run the five filters before talking about taste or preference. 4. Separate copyable mechanism from non-copyable surface traits. 5. End with one concrete first imitation or research move. ## Default Contract Assume the following unless the user says otherwise: - write in Chinese - creator, OPC, one-person-business, or content-led business context - filter first, deep-research later - look for operating signal, not just personal charisma - do not let "this doesn't feel like me" override mechanism analysis too early ## Workflow ### Phase 1: Clarify the Learning Target Ask what the user is really trying to learn: - content system - offer design - channel growth - conversion path - brand or positioning - overall business model If the learning target is fuzzy, the benchmark choice will be fuzzy too. ### Phase 2: Run the Five Filters Judge each benchmark through these filters: 1. **Economic signal** - Is there evidence of a real business, not just attention? 2. **Model legibility** - Can we roughly understand how this person gets attention, trust, money, and delivery done? 3. **Copyable mechanism** - What part is learnable process, and what part is likely talent, timing, capital, or reputation advantage? 4. **Stage relevance** - Is the benchmark too far ahead or operating in a structurally different game? 5. **Ego-noise control** - Is the user rejecting the benchmark because it truly cannot be learned from, or because it feels unglamorous, repetitive, or not self-expressive enough? Read [references/filter-framework.md](references/filter-framework.md) when the judgment is mixed. ### Phase 3: Name the Layer to Study Do not say only "study this person." Say which layer is worth studying: - content angle - packaging - offer ladder - conversion path - audience selection - operating rhythm And say which layer should not be copied blindly. ### Phase 4: Check Copy Granularity If the user already has a benchmark and says they are "learning from" it, verify the level of imitation. Read [references/copy-granularity.md](references/copy-granularity.md) when doing a copy check. Common failure: - copying the vibe but not the mechanism - copying the topic but not the offer structure - copying the output but not the cadence or conversion path ### Phase 5: Recommend the Next Move Choose the smallest next step: - shortlist 1-3 worthy benchmarks - copy one specific layer first - or escalate one benchmark into `$opc-case-research` ## Output Format Default to [assets/benchmark-card-template.md](assets/benchmark-card-template.md). At minimum, include: - one-line judgment - five-filter result - worth-learning layers - do-not-copy layers - first move - whether deeper research is recommended ## Hard Rules Do not: - use follower count as the main proof of worth - confuse charisma with business model - say "learn from them" without naming what to learn - let personal taste override mechanism analysis too early - turn a quick filter into a fake full case study Always: - clarify the learning target - separate signal from surface - mark copyable versus non-copyable parts - give one concrete next move - recommend `$opc-case-research` only when deeper case study would materially help ## Resource Map - [references/filter-framework.md](references/filter-framework.md) - Read for the five filters, signal examples, and mixed-case judgment rules. - [references/copy-granularity.md](references/copy-granularity.md) - Read for how to test whether the user is copying at a useful level of detail. - [assets/benchmark-card-template.md](assets/benchmark-card-template.md) - Use for the standard output structure.

path: agents/openai.yaml
content: interface:
  display_name: "对标筛选"
  short_description: "判断一个创作者或一人公司案例是否值得学，以及到底该学哪一层"
  default_prompt: "Use $benchmark-filter to evaluate this creator, OPC, or one-person business benchmark in Chinese: separate business signal from vanity signal, tell me what layer is actually worth copying, and decide whether it should be escalated into $opc-case-research."


path: assets/benchmark-card-template.md
content: # 对标筛选卡

## 一句话判断

- 

## 学习目标

- 这次主要想学：

## 五层筛选

| 筛选项 | 判断 | 说明 |
| --- | --- | --- |
| Economic Signal |  |  |
| Model Legibility |  |  |
| Copyable Mechanism |  |  |
| Stage Relevance |  |  |
| Ego-Noise Control |  |  |

## 值得学的层

- 

## 不要盲抄的层

- 

## 第一动作

- 

## 是否建议升级为 OPC Case Research

- 


path: references/copy-granularity.md
content: # 模仿颗粒度检查

## 1. 不要只说“我在学他”

必须能说清：

- 你学的是哪个层
- 你没学的是哪个层
- 你为什么先学这一层

## 2. 可检查的颗粒

至少可以检查以下项目：

- 内容主题与切口
- 内容形式
- 发布频率
- 标题与包装风格
- 私域或咨询承接
- 产品层级
- 转化路径
- 交付方式

## 3. 常见伪模仿

### 3.1 学了语气，没学结构

结果：

- 看起来像
- 但做不出同样的结果

### 3.2 学了内容，没学产品

结果：

- 内容越来越多
- 商业结果没有同步出现

### 3.3 学了表层动作，没学节奏

结果：

- 知道别人做什么
- 但不知道别人怎么持续做

## 4. 更适合升级成 case research 的情况

如果你已经发现某个 benchmark：

- 经营信号很强
- 模型能大致看懂
- 值得长期研究

就不要停留在筛选，转交给 `$opc-case-research` 做更完整的公开信息拆解。


path: references/filter-framework.md
content: # 五层筛选框架

## 1. Economic Signal

先看这是不是一个真实运转的生意或内容系统。

信号可以包括：

- 明确产品或服务
- 可见成交入口
- 稳定内容分发与承接关系
- 可推断的转化链

弱信号包括：

- 只有粉丝数
- 只有热闹评论
- 只有人设，没有产品

## 2. Model Legibility

判断你能不能大致看懂它怎么工作。

最低要求：

- 怎么被看见
- 为什么被信任
- 靠什么赚钱
- 如何交付

如果只看见结果，看不见机制，就还不适合深学。

## 3. Copyable Mechanism

把可复制部分和不可复制部分拆开。

较可复制：

- 选题结构
- 内容节奏
- 产品分层
- 交付路径
- 转化链条

较难复制：

- 十几年行业资历
- 名人背书
- 特殊历史窗口
- 独家渠道与关系

## 4. Stage Relevance

判断这个 benchmark 是否处在一个可比较的阶段。

如果对方已经是成熟公司或超级头部 IP，而用户还在最初期：

- 可以学局部机制
- 不要把整体状态拿来当直接对比对象

## 5. Ego-Noise Control

常见噪音：

- 我不喜欢这种表达
- 这不像我
- 这个看起来太土
- 这个太重复了

先问：

- 它是不值得学，还是只是“不够让我感觉高级”
- 真学不动，是机制不成立，还是 ego 在拒绝重复和笨功夫


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)