/install cell-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
- Clarify whether the user needs a shortlist or a judgment on one existing benchmark.
- Identify the user's real learning target: content, offer, channel, conversion, positioning, or business model.
- Run the five filters before talking about taste or preference.
- Separate copyable mechanism from non-copyable surface traits.
- 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:
- Economic signal
- Is there evidence of a real business, not just attention?
- Model legibility
- Can we roughly understand how this person gets attention, trust, money, and delivery done?
- Copyable mechanism
- What part is learnable process, and what part is likely talent, timing, capital, or reputation advantage?
- Stage relevance
- Is the benchmark too far ahead or operating in a structurally different game?
- 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 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 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.
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-researchonly when deeper case study would materially help
Resource Map
- references/filter-framework.md
- Read for the five filters, signal examples, and mixed-case judgment rules.
- 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
- Use for the standard output structure.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install cell-benchmark-filter - 安装完成后,直接呼叫该 Skill 的名称或使用
/cell-benchmark-filter触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
对标筛选 是什么?
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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 109 次。
如何安装 对标筛选?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install cell-benchmark-filter」即可一键安装,无需额外配置。
对标筛选 是免费的吗?
是的,对标筛选 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
对标筛选 支持哪些平台?
对标筛选 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 对标筛选?
由 cellinlab(@cellinlab)开发并维护,当前版本 v0.1.0。