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Agent Skill · 研究检索

opencrawl(opencrawl 搜索)

opencrawl 用于处理浏览器自动化、网页检查和页面信息提取,适合在 OpenClaw 中需要让 Agent 打开页面、读取网页或验证前端流程时使用。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。

opencrawl 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 10,266。

总安装 12,854周安装 515GitHub Stars 公开资料未说明下载 4,161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:opencrawl(opencrawl 搜索)
来源仓库:https://github.com/jcools1977/opencrawl
安装命令:
openclaw skills install opencrawl
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 opencrawl

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。

opencrawl 用于浏览器自动化、网页检查和页面信息提取。适合在 OpenClaw 中需要让 Agent 打开页面、读取内容或验证前端流程时使用。将每个对话视为收益工具,支持情感检测和零成本优化引擎。安装前建议确认权限范围、维护状态及是否会触发联网或页面渲染。

可结合来源仓库和 README 进一步核验具体用法和限制条件。

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 opencrawl;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

    它需要调用外部服务,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

opencrawl 怎么安装?

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

opencrawl 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。

opencrawl 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

opencrawl 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。

opencrawl 用的人多吗?

公开数据是累计安装 12,854 次,近一周安装 515 次,下载 4,161 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。

opencrawl 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

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
opencrawl

opencrawl 搜索 · 本页

12,854—调用外部服务MIT-0有提醒

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
convoyield
description
>
version
1.0.0
author
J. DeVere Cooley
homepage
https://github.com/jcools1977/Opencrawl
metadata
openclaw
emoji
📈
requires
bins
os
["macos", "linux", "windows"]
tags
platforms
["discord", "telegram", "whatsapp", "slack", "webchat"]
cost
free
requires_api
false

ConvoYield — Conversational Yield Optimization Engine

"Every conversation is a financial instrument. ConvoYield tells you what it's worth."

What It Does

ConvoYield gives any bot a real-time revenue intelligence layer. On every user message, five engines run in parallel and produce:

  • Sentiment Arbitrage — Detects emotional gaps that create revenue opportunities

(frustration capture, competitor displacement, excitement amplification, etc.)

  • Micro-Conversion Tracking — Finds 12 types of hidden value in every message

(email captures, budget reveals, pain points, referral signals, etc.)

  • Momentum Scoring — Measures whether the conversation is gaining or losing steam
  • Yield Forecasting — Predicts the total dollar value of the conversation in real-time
  • Play Calling — Recommends from a 20-play behavioral economics playbook

(anchoring, loss framing, social proof, empathy bridges, urgency closes, etc.)

Zero Cost Guarantee

  • Zero external dependencies — Pure Python standard library
  • Zero API calls — All analysis runs locally via pattern matching and heuristics
  • Zero tokens consumed — Does not call any LLM API
  • Zero infrastructure — pip install and go
  • <1ms per message — Adds no latency to your bot

Quick Start

from convoyield import ConvoYield

engine = ConvoYield(base_conversation_value=50.0)

# Process each user message
result = engine.process_user_message("I'm frustrated with Salesforce, it's way too expensive")

print(result.recommended_play)       # "competitor_displacement"
print(result.estimated_yield)         # 89.50
print(result.recommended_tone)        # "empathetic"
print(result.top_arbitrage.type)      # "frustration_capture"
print(result.risk_level)              # 0.21

# Record bot response for full state tracking
engine.record_bot_response("I hear you. What specifically isn't working?")

# Next message — yield COMPOUNDS
result = engine.process_user_message("The reporting is terrible and costs $500/month")
print(result.estimated_yield)         # 142.30 — value is growing!

The Five Engines

1. Sentiment Arbitrage Engine

Detects 7 arbitrage patterns via lexicon-based sentiment scoring tuned for commercial conversations:

PatternWhat It DetectsValue Signal
competitor_displacementFrustration with a named competitor$45+
frustration_captureGeneral frustration with current solution$35+
excitement_amplificationUser showing enthusiasm$25+
uncertainty_anchoringUser unsure, needs guidance$20+
urgency_premiumTime pressure detected$30+
social_proof_hungerUser seeking validation$15+
budget_value_stackUser discussing budget/cost$40+

2. Micro-Conversion Tracker

Detects 12 micro-conversion opportunities between "hello" and "purchase":

  • Email/phone capture opportunities
  • Budget and timeline reveals
  • Team size and need statements
  • Competitor mentions and feature requests
  • Referral and testimonial signals
  • Pain point articulations

Each micro-conversion has an estimated dollar value ($0.50-$15).

3. Momentum Scorer

Scores engagement momentum (-1.0 to +1.0) using four signals:

  • Message length trend (expanding = engaged)
  • Question frequency trend (asking = curious)
  • Emotional intensity trend (feeling = invested)
  • Vocabulary richness trend (elaborating = committed)

Labels: surging | accelerating | stable | declining | hemorrhaging

4. Yield Forecaster

Combines all signals to predict a dollar value for the conversation using:

  • Phase multipliers (OPENING → DISCOVERY → ENGAGEMENT → NEGOTIATION → CLOSING)
  • Micro-conversion portfolio value
  • Arbitrage opportunity value
  • Engagement and momentum premiums
  • Risk assessment (0.0-1.0)

5. Play Caller

Recommends from 20 plays inspired by behavioral economics:

warm_handshake · pattern_interrupt · deep_probe · empathy_bridge · value_stack · competitor_displacement · social_proof_deploy · dopamine_ride · anchoring · loss_framing · budget_reframe · choice_architecture · assumptive_close · urgency_close · soft_close · momentum_recovery · save_attempt · upsell_bridge · referral_harvest · objection_reframe

Integration

Works with any bot framework — hook into your message handler:

from convoyield import ConvoYield

engine = ConvoYield()

def on_user_message(text, conversation_id):
    result = engine.process_user_message(text)

    # Shape your bot's response using:
    # result.recommended_play     → WHAT strategy to use
    # result.recommended_tone     → HOW to say it
    # result.arbitrage_opportunities → WHERE the money is
    # result.micro_conversions    → WHAT value to capture
    # result.risk_level           → HOW careful to be
    # result.estimated_yield      → HOW much is at stake

    return generate_response(text, result)

Premium Playbooks

Four industry-specific playbook packs available:

PlaybookPlaysPrice
SaaS Sales Mastery25$49/mo
E-Commerce Revenue Max22$39/mo
Real Estate Closer20$79/mo
Healthcare Engagement18$99/mo

Revenue Model

ConvoYield is free and open source. Revenue comes from:

  1. Premium playbooks — Industry-specific play packs ($39-99/mo)
  2. Cloud analytics — Dashboard and yield tracking ($0/49/299/mo tiers)
  3. Enterprise — Custom playbooks, webhooks, white-label ($299/mo)

Architecture

convoyield/
├── orchestrator.py              # Main ConvoYield engine
├── engines/
│   ├── sentiment_arbitrage.py   # 7 arbitrage pattern detectors
│   ├── micro_conversion.py      # 12 micro-conversion trackers
│   ├── momentum.py              # 4-signal engagement scorer
│   ├── yield_forecaster.py      # Dollar-value yield prediction
│   └── play_caller.py           # 20-play behavioral economics playbook
├── models/
│   ├── conversation.py          # ConversationState, Turn, Phase
│   └── yield_result.py          # YieldResult, ArbitrageOpportunity
├── playbooks/                   # 4 premium industry packs (85 plays)
├── coin/                        # ConvoCoin — Proof-of-Yield blockchain
└── cloud/                       # Telemetry client for analytics

Tests

40 tests across 7 suites — all passing:

python -m pytest tests/ -v

平台分布

OpenClaw

90.09%
按下载量换算3,749

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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