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content-parser内容解析器

Agent Skill

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。

总安装

9,036

周安装

362

GitHub Stars

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下载量

2,925
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:content-parser(内容解析器)
来源仓库:https://github.com/0xfango/content-parser
安装命令:
openclaw skills install content-parser
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-parser

简介

从指定 URL 中提取并解析网页内容,支持结构化输出。

  • 适用于信息抓取、竞品分析与资料整理等场景。content-parser 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 可与其它技能配合使用,进一步处理原始文本。
  • 触发条件为用户主动提供链接并要求提取信息。
  • 确保内容准确性与完整性,避免遗漏关键段落。

SKILL.md

name
content-parser
description
|
metadata
openclaw
emoji
🔗
requires
env
["LISTENHUB_API_KEY"]
primaryEnv
LISTENHUB_API_KEY

When to Use

  • User provides a URL and wants to extract/read its content
  • Another skill needs to parse source material from a URL before generation
  • User says "parse this URL", "extract content from this link"
  • User says "解析链接", "提取内容"

When NOT to Use

  • User already has text content and doesn't need URL parsing
  • User wants to generate audio/video content (not content extraction)
  • User wants to read a local file (use standard file reading tools)

Purpose

Extract and normalize content from URLs across supported platforms. Returns structured data including content body, metadata, and references. Useful as a preprocessing step for content generation skills or standalone content extraction.

Hard Constraints

  • No shell scripts. Construct curl commands from the API reference files listed in Resources
  • Always read shared/authentication.md for API key and headers
  • Follow shared/common-patterns.md for polling, errors, and interaction patterns
  • URL must be a valid HTTP(S) URL
  • Always read config following shared/config-pattern.md before any interaction
  • Never save files to ~/Downloads/ or .listenhub/ — save to the current working directory

<HARD-GATE> Use the AskUserQuestion tool for every multiple-choice step — do NOT print options as plain text. Ask one question at a time. Wait for the user's answer before proceeding to the next step. After collecting URL and options, confirm with the user before calling the extraction API. </HARD-GATE>

Step -1: API Key Check

Follow shared/config-pattern.md § API Key Check. If the key is missing, stop immediately.

Step 0: Config Setup

Follow shared/config-pattern.md Step 0.

If file doesn't exist — ask location, then create immediately:

mkdir -p ".listenhub/content-parser"
echo '{"autoDownload":true}' > ".listenhub/content-parser/config.json"
CONFIG_PATH=".listenhub/content-parser/config.json"
# (or $HOME/.listenhub/content-parser/config.json for global)

Then run Setup Flow below.

If file exists — read config, display summary, and confirm:

当前配置 (content-parser):
  自动下载:{是 / 否}

Ask: "使用已保存的配置?" → 确认,直接继续 / 重新配置

Setup Flow (first run or reconfigure)

  1. autoDownload: "自动保存提取的内容到当前目录?"

- "是(推荐)" → autoDownload: true - "否" → autoDownload: false

Save immediately:

NEW_CONFIG=$(echo "$CONFIG" | jq --argjson dl {true/false} '. + {"autoDownload": $dl}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")

Interaction Flow

Step 1: URL Input

Free text input. Ask the user:

What URL would you like to extract content from?

Step 2: Options (optional)

Ask if the user wants to configure extraction options:

Question: "Do you want to configure extraction options?"
Options:
  - "No, use defaults" — Extract with default settings
  - "Yes, configure options" — Set summarize, maxLength, or Twitter tweet count

If "Yes", ask follow-up questions:

  • Summarize: "Generate a summary of the content?" (Yes/No)
  • Max Length: "Set maximum content length?" (Free text, e.g., "5000")
  • Twitter count (only if URL is Twitter/X profile): "How many tweets to fetch?" (1-100, default 20)

Step 3: Confirm & Extract

Summarize:

Ready to extract content:

  URL: {url}
  Options: {summarize: true, maxLength: 5000, twitter.count: 50} / default

  Proceed?

Wait for explicit confirmation before calling the API.

Workflow

  1. Validate URL: Must be HTTP(S). Normalize if needed (see references/supported-platforms.md)
  2. Build request body:
   {
     "source": {
       "type": "url",
       "uri": "{url}"
     },
     "options": {
       "summarize": true/false,
       "maxLength": 5000,
       "twitter": {
         "count": 50
       }
     }
   }

Omit options if user chose defaults.

  1. Submit (foreground): POST /v1/content/extract → extract taskId
  2. Tell the user extraction is in progress
  3. Poll (background): Run the following exact bash command with run_in_background: true and timeout: 300000. Note: status field is .data.status (not processStatus), interval is 5s, values are processing/completed/failed:
   TASK_ID="<id-from-step-3>"
   for i in $(seq 1 60); do
     RESULT=$(curl -sS "https://api.marswave.ai/openapi/v1/content/extract/$TASK_ID" \
       -H "Authorization: Bearer $LISTENHUB_API_KEY" 2>/dev/null)
     STATUS=$(echo "$RESULT" | tr -d '\000-\037\177' | jq -r '.data.status // "processing"')
     case "$STATUS" in
       completed) echo "$RESULT"; exit 0 ;;
       failed) echo "FAILED: $RESULT" >&2; exit 1 ;;
       *) sleep 5 ;;
     esac
   done
   echo "TIMEOUT" >&2; exit 2
  1. When notified, download and present result:

If autoDownload is true: - Write {taskId}-extracted.md to the current directory — full extracted content in markdown - Write {taskId}-extracted.json to the current directory — full raw API response data

   echo "$CONTENT_MD" > "${TASK_ID}-extracted.md"
   echo "$RESULT" > "${TASK_ID}-extracted.json"

Present:

   内容提取完成!

   来源:{url}
   标题:{metadata.title}
   长度:~{character count} 字符
   消耗积分:{credits}

   已保存到当前目录:
     {taskId}-extracted.md
     {taskId}-extracted.json
  1. Show a preview of the extracted content (first ~500 chars)
  2. Offer to use content in another skill (e.g. /podcast, /tts)

Estimated time: 10-30 seconds depending on content size and platform.

API Reference

  • Content extract: shared/api-content-extract.md
  • Supported platforms: references/supported-platforms.md
  • Polling: shared/common-patterns.md § Async Polling
  • Error handling: shared/common-patterns.md § Error Handling
  • Config pattern: shared/config-pattern.md

Example

User: "Parse this article: https://en.wikipedia.org/wiki/Topology"

Agent workflow:

  1. URL: https://en.wikipedia.org/wiki/Topology
  2. Options: defaults (omit options)
  3. Submit extraction
curl -sS -X POST "https://api.marswave.ai/openapi/v1/content/extract" \
  -H "Authorization: Bearer $LISTENHUB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source": {
      "type": "url",
      "uri": "https://en.wikipedia.org/wiki/Topology"
    }
  }'
  1. Poll until complete:
curl -sS "https://api.marswave.ai/openapi/v1/content/extract/69a7dac700cf95938f86d9bb" \
  -H "Authorization: Bearer $LISTENHUB_API_KEY"
  1. Present extracted content preview and offer next actions.

User: "Extract recent tweets from @elonmusk, get 50 tweets"

Agent workflow:

  1. URL: https://x.com/elonmusk
  2. Options: {"twitter": {"count": 50}}
  3. Submit extraction
curl -sS -X POST "https://api.marswave.ai/openapi/v1/content/extract" \
  -H "Authorization: Bearer $LISTENHUB_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source": {
      "type": "url",
      "uri": "https://x.com/elonmusk"
    },
    "options": {
      "twitter": {
        "count": 50
      }
    }
  }'
  1. Poll until complete, present results.

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

OpenClaw

97.45%
按下载量换算2,850

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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。

来源信息

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