S011 Test

SKILL商业运营
商业运营 未标注权限 许可证不明确
安装这个技能

Conversational Yield Optimization Engine — treats every bot conversation as a yield-bearing financial instrument. Five zero-cost engines detect sentiment arbitrage, micro-conversions, engagement momentum, dollar-value yield forecasts, and optimal strategic plays in real-time. No external APIs. No dependencies. Pure behavioral economics applied to conversations.

一句话选型结论

按资料字段整理

S011 Test是商业运营类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类商业运营 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_e634750a/sectest130-s011
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

商业运营
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_e634750a仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791674287
正文体量7.4 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
写运营方案

按目标产出活动方案与执行清单。

分析数据与指标

从报表里找问题、给结论。

整理客户与销售材料

把客户信息与话术整理成可直接用的材料。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在商业运营场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
S011 Test当前 未标注权限 未标注 未标注 未标注 有
MX 股票筛选器 联网访问 ClawHub 市场 79.9 万 OpenClaw 有
交易 未标注权限 ClawHub 市场 58.1 万 OpenClaw 有
营销技巧 只读 ClawHub 市场 54.0 万 OpenClaw 有
百灵鸟好 联网访问 GitHub 仓库 52.6 万 Codex、Claude、Cursor 有
股票信息浏览器 接触敏感数据 ClawHub 市场 40.5 万 OpenClaw 有

SKILL.md 全文

7.4 KB
模型实际读到的说明书原文,来自技能仓库

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(&quot;I&#x27;m frustrated with Salesforce, it&#x27;s way too expensive&quot;)

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

# Record bot response for full state tracking
engine.record_bot_response(&quot;I hear you. What specifically isn&#x27;t working?&quot;)

# Next message — yield COMPOUNDS
result = engine.process_user_message(&quot;The reporting is terrible and costs $500/month&quot;)
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:

Pattern What It Detects Value Signal
competitor_displacement Frustration with a named competitor $45+
frustration_capture General frustration with current solution $35+
excitement_amplification User showing enthusiasm $25+
uncertainty_anchoring User unsure, needs guidance $20+
urgency_premium Time pressure detected $30+
social_proof_hunger User seeking validation $15+
budget_value_stack User 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&#x27;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:

Playbook Plays Price
SaaS Sales Mastery 25 $49/mo
E-Commerce Revenue Max 22 $39/mo
Real Estate Closer 20 $79/mo
Healthcare Engagement 18 $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

常见问题

5 问
答案由本站依据条目字段整理
S011 Test怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_e634750a/sectest130-s011。安装方式为未标注。装完刷新技能列表或重开客户端。

S011 Test支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

S011 Test权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

S011 Test收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

S011 Test用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。