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cell-benchmark-filter细胞基准过滤器

Agent Skill

cell-benchmark-filter 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,345

周安装

134

GitHub Stars

公开资料未说明

下载量

1,083
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cell-benchmark-filter(细胞基准过滤器)
来源仓库:https://github.com/cellinlab/cell-benchmark-filter
安装命令:
openclaw skills install cell-benchmark-filter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install cell-benchmark-filter

简介

cell-benchmark-filter 判断创作者或企业是否符合一人商业作品基准。

  • 适合在 OpenClaw 中进行中国内容创作者的资质筛查。
  • 通过 clawhub 安装,基于公开数据源生成评估报告。
  • 使用前应确认筛选维度和权重设置。cell-benchmark-filter 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议核对原始文档了解数据来源和更新频率。

SKILL.md

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?

  1. Model legibility

- Can we roughly understand how this person gets attention, trust, money, and delivery done?

  1. Copyable mechanism

- What part is learnable process, and what part is likely talent, timing, capital, or reputation advantage?

  1. Stage relevance

- Is the benchmark too far ahead or operating in a structurally different game?

  1. 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-research only when deeper case study would materially help

Resource Map

- Read for the five filters, signal examples, and mixed-case judgment rules.

- Read for how to test whether the user is copying at a useful level of detail.

- Use for the standard output structure.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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