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Agent Skill · 研究检索

cell-benchmark-filter(细胞基准过滤器)

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。

cell-benchmark-filter 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 27,707。

总安装 3,345周安装 134GitHub Stars 公开资料未说明下载 1,083核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install cell-benchmark-filter;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

cell-benchmark-filter 怎么安装?

在终端执行 openclaw skills install cell-benchmark-filter。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cell-benchmark-filter 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

cell-benchmark-filter 需要什么权限?

权限级别是需要联网。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。

cell-benchmark-filter 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

cell-benchmark-filter 用的人多吗?

公开数据是累计安装 3,345 次,近一周安装 134 次,下载 1,083 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

cell-benchmark-filter 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
cell-benchmark-filter

细胞基准过滤器 · 本页

3,345—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

OpenClaw

89.61%
按下载量换算970

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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