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Agent Skill · 效率

content-product-analyzer(内容产品分析器)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

content-product-analyzer 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 12,384。

总安装 4,162周安装 170GitHub Stars 公开资料未说明下载 1,346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
1.0.0

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:content-product-analyzer(内容产品分析器)
来源仓库:https://github.com/guoxinling/content-product-analyzer
安装命令:
openclaw skills install content-product-analyzer
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 content-product-analyzer

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

根据产品页面、截图或文本生成商业分析与受众洞察报告。支持推断用户画像、竞品优势与市场需求痛点。适用于产品经理、创业者与市场研究人员。content-product-analyzer 属于效率类 Skill,可作为该场景下的辅助能力补充。帮助快速理解产品核心价值与目标人群特征。输出包含策略建议与优化方向的完整拆解文档。

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 content-product-analyzer;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

content-product-analyzer 怎么安装?

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

content-product-analyzer 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。

content-product-analyzer 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

content-product-analyzer 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。

content-product-analyzer 用的人多吗?

公开数据是累计安装 4,162 次,近一周安装 170 次,下载 1,346 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。

content-product-analyzer 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 需要处理效率类任务、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。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
content-product-analyzer

内容产品分析器 · 本页

4,162—只读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
content-product-analyzer
description
produce a commercial teardown of a post, creator profile, product page, landing page, or app page from urls, screenshots, or pasted text. use to infer audience, positioning, growth mechanics, monetization signals, credibility, sentiment, and opportunities to borrow or avoid. supports multi-link bundles and competitor comparison; separate observed facts from supported inferences and unknowns; use mixed-mode web verification when public urls are provided or freshness matters and cite sources.

Content Product Analyzer

Turn raw content or product inputs into a structured commercial teardown. Focus on business value, audience, positioning, growth mechanics, credibility, sentiment, and opportunities to borrow or avoid.

Inputs supported

  • Single object: one post / creator profile / product page / landing page / app page
  • Bundle: multiple URLs / screenshots / text snippets for one product or campaign
  • Competitor set: multiple products/pages to compare side-by-side

Evidence modes (must choose explicitly)

1) User-provided only - Use only what the user pasted or what is visible in screenshots. - Do not claim external facts.

2) Mixed mode - Allowed when: the user provides public URLs OR the user asks for “latest/pricing/reviews/current ranking” OR recency clearly matters. - Verify and enrich with public sources. - Prefer: official site → original platform page → pricing/docs → reputable review sources. - Cite factual claims from public sources.

Non-negotiable rules

  • Start from evidence, not vibes.
  • Distinguish clearly between:

- Observed facts (directly visible/quoted) - Supported inferences (reasonable, evidence-linked) - Unknowns (explicitly list)

  • Prefer commercial and strategic analysis over technical implementation detail.
  • For screenshots: only claim what is visible or directly implied.
  • Do not overstate follower psychology, revenue, conversion rates, or traction when not evidenced.
  • When input is sparse: state low confidence and keep recommendations lightweight.

Workflow

1) Identify object type(s): post / creator profile / product page / landing page / app page / bundle. 2) Pick evidence mode: user-only vs mixed-mode. 3) Extract observable facts first. 4) Infer audience, positioning, growth/virality mechanics, monetization, sentiment. 5) Separate confirmed facts vs inferences vs unknowns. 6) Produce BOTH outputs (in order): - Structured summary - Detailed teardown 7) End with concrete advice tailored to: - creator (content packaging, hooks, formats, audience resonance) - indie builder (positioning, MVP, pricing, distribution, differentiation)

Analysis lenses (pick what’s relevant; don’t force all)

For creators / posts / social profiles

Assess:

  • Content topic and promise
  • Hook structure & attention mechanics
  • Tone/persona & stylistic signatures
  • Engagement structure (replies/saves/reposts/CTAs)
  • Likely audience and motivations
  • Credibility signals & weak signals
  • Commercial intent (sponsorship, lead-gen, affiliate, community funnel, product funnel)
  • What is repeatable vs personality-dependent

For products / landing pages / app pages

Assess:

  • Core problem and target user
  • Positioning & category framing
  • Value proposition clarity
  • Feature packaging vs differentiation
  • Pricing/monetization signals
  • Social proof, trust, risk
  • Marketability (share/buy/ignore triggers)
  • Objections & opportunity space for an indie builder

Required output format

1) Structured summary (compact)

Use these fields when applicable:

  • 对象类型
  • 一句话定位
  • 目标用户
  • 用户想要的结果
  • 内容/产品风格特征
  • 增长或传播机制
  • 商业模式/变现线索
  • 可信度信号
  • 风险或不足
  • 可借鉴点
  • 建议动作
  • 结论置信度: 高 / 中 / 低

2) Detailed teardown (long-form)

Use this default structure (adapt as needed):

分析标题

核心信息提炼

Only the highest-signal facts.

结构化拆解

Topic/audience/style/mechanics/commercial logic.

用户与市场判断

Who it is for, what desire/pain it maps to, market signal.

商业价值与增长判断

Monetization clues, retention/funnel logic, why it may or may not work.

风险、槽点与反证

What’s missing, inflated, weak, or misleading.

可复用方法

What a creator/indie builder can actually borrow.

建议

Tailor advice:

  • For creators: topic ideas, hook patterns, audience angles, content directions.
  • For indie builders: positioning, MVP, monetization, differentiation, GTM.

一句话结论

Sharp final judgment.

Competitor comparison add-on (when user provides 2+ competitors)

If the user asks “竞品对比 / compare / vs”, append this section after the teardown:

竞品对比矩阵

Create a markdown table comparing:

  • Positioning sentence
  • Target user / job-to-be-done
  • Key differentiators
  • Pricing / plan structure signals
  • Trust & proof
  • Distribution channel guesses
  • Likely wedge (why users start)
  • Likely lock-in (why users stay)
  • Risks / gaps

对比结论与切入策略

  • What is easiest to copy vs defensible
  • Underserved segment(s)
  • Recommended wedge for the user
  • 3–7 action items (sequenced)

Quality bar

A good answer should:

  • Read like a sharp operator, not a generic summarizer
  • Contain explicit judgment, not just description
  • Surface upside AND failure modes
  • Produce at least 3 concrete suggestions when enough evidence exists
  • Be honest about uncertainty

平台分布

OpenClaw

97.94%
按下载量换算1,318

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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