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对标筛选

作者 cellinlab · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install cell-benchmark-filter
功能描述
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...
使用说明 (SKILL.md)

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 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

安全使用建议
This skill appears coherent and low-risk: it will run only the documented judgment workflow in Chinese and uses the included templates and references. Before installing, confirm you are comfortable submitting any benchmark examples (they may contain personal or business data) and verify you want outputs in Chinese. Remember the skill is a filter, not a full case study—escalate to deeper research only when you need a detailed public-information teardown. If you require the agent to fetch private data or connect to external services, those would be separate permissions that this skill does not request.
功能分析
Type: OpenClaw Skill Name: cell-benchmark-filter Version: 0.1.0 The skill bundle is a framework for evaluating business benchmarks and creators, specifically focused on Chinese one-person businesses. It consists of markdown instructions (SKILL.md), templates (benchmark-card-template.md), and reference guides (filter-framework.md) that guide an AI agent through a structured analysis process. There is no evidence of malicious code, data exfiltration, unauthorized execution, or harmful prompt injection; the logic is entirely aligned with its stated purpose of business model analysis.
能力评估
Purpose & Capability
Name/description, included reference docs, and the agent prompt all align: the skill's goal is to filter benchmarks for creators/OPC/one-person businesses and the files and instructions support that task.
Instruction Scope
SKILL.md limits activity to asking clarifying questions, running five explicit filters, using provided templates and references, and recommending next steps; it does not instruct the agent to read unrelated system files, access external endpoints, or exfiltrate secrets.
Install Mechanism
No install spec and no code files are present (instruction-only), so nothing is written to disk or downloaded; this is the lowest-risk pattern.
Credentials
The skill declares no required environment variables, credentials, or config paths, and the runtime instructions do not reference any hidden secrets—requested access is proportional to its stated purpose.
Persistence & Privilege
always is false and the skill does not request persistent system presence or modify other skills; autonomous invocation is allowed by default but there are no additional privileged behaviors.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install cell-benchmark-filter
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /cell-benchmark-filter 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial public release
元数据
Slug cell-benchmark-filter
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

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。

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