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对标筛选

by @cellinlab

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...

Versionv0.1.0
Downloads720
#crypto-defi#translation#legal
TERMINAL
clawhub install cell-benchmark-filter

📖 About This Skill

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 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

  • 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.

    💡 Examples

    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.