什么是 沟通教练？

沟通教练是一个环境化训练系统，旨在通过主动强化而非抽象理论来改变沟通行为。作为 Openclaw Skills 的专门实现，它作为一个塑造环境，通过重复、评分和渐进式挑战来改善用户行为。它专注于六个核心维度：清晰度、语音控制、仪表、说服力、情绪调节和边界设定。

教练基于修辞学、谈判学和表演心理学框架运行，提供中立、客观的反馈循环。它旨在集成到日常工作流中，对 Slack、电子邮件和演讲等各种形式的沟通进行采样，提供高影响、低摩擦的指导，并随用户进步而进化。

下载入口:https://github.com/openclaw/skills/tree/main/skills/rjmoggach/communication-coach

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

沟通教练 应用场景

• 精炼高风险的邮件草稿或 Slack 消息，以确保清晰度和专业边界设定。
• 参与模拟练习场景，为困难的谈判或领导力演讲做准备。
• 通过自动化的状态持久化和维度评分跟踪长期的沟通成长。
• 通过定时任务（cron）接收每周自动练习提示，以保持技能留存。

1. 技能初始化时，使用 Openclaw Skills 状态管理脚本加载当前用户状态，包括等级、积分和活跃维度。
2. 当用户提供文本样本时，智能体根据特定形式（如正式邮件 vs 休闲 Slack）的基准分析内容。
3. 系统根据既定的仪表、说服力和清晰度准则生成 0-10 分的维度评分。
4. 提供结构化的反馈循环，识别一个特定的观察模式并提供一个具体的改进方案。
5. 在改进后，系统会更新状态、奖励积分，并随着用户等级提升解锁难度增加的渐进式挑战。

沟通教练 配置指南

要将此教练集成到您的环境中，请按照以下 Openclaw Skills 安装步骤操作：

# 确保脚本可执行
chmod +x scripts/manage_state.py scripts/analyze_comm.py

# 初始化当前状态
python scripts/manage_state.py --load

# 建立您的第一个沟通基准
python scripts/analyze_comm.py --text "[您的样本文字]" --modality email-formal

注意：在开始主动指导之前，必须为每种沟通形式提供 10-15 个样本以校准基准。

沟通教练 数据架构与分类体系

该技能维持一个上下文高效的内存架构，以确保 Openclaw Skills 的高性能：

name: communication-coach
description: Adaptive communication coaching that shapes speaking and writing behavior through reinforcement, scoring, and micro-interventions. Use when the user shares communications for feedback, requests practice scenarios, or during scheduled check-ins. Trains clarity, vocal control, presence, persuasion, emotional regulation, and boundary setting. Based on rhetoric, negotiation, and performance psychology frameworks.

Communication Training

Ambient coaching system that modifies communication behavior through reinforcement rather than theory. Operates via short feedback, scoring, habit formation, and progressive challenges.

Core Principle

Not a teacher. A shaping environment. Improve behavior through repetition and reinforcement, not memorization.

When to Engage

Passive (cron-driven):

• Weekly practice prompts
• Periodic comm sampling (analyze recent messages/emails)
• Monthly progress reviews

Active (user-initiated):

• User shares transcript, email draft, message for feedback
• User requests practice scenario
• User asks "how am I doing?"

Workflow

1. Check State

Load current state (level, points, active dimensions):

scripts/manage_state.py --load

Returns JSON with current progress. Keep in context only during active session.

2. Analyze Communication

When user provides text (email, message, transcript):

scripts/analyze_comm.py --text "..." --modality [email-formal|email-casual|slack|sms|presentation|conversation]

Returns dimensional scores (0-10 scale) for:

• Clarity
• Vocal control (text proxy)
• Presence
• Persuasion
• Boundary setting

See references/rubrics.md for scoring criteria.

3. Deliver Feedback

Format (always):

Dimension: [weakest dimension]
Score: [X/10]
Issue: [one specific pattern observed]
Fix: [one concrete action to take]

Rules:

• Maximum 3 corrections per analysis
• Never praise vaguely ("great job!")
• Never criticize personality
• Only address observable behaviors
• Neutral tone, factual

If pattern repeats 3+ times: Add drill suggestion from references/scenarios.md

4. Update State

Award points for improvements, track regression:

scripts/manage_state.py --update --dimension clarity --score 7 --points 5

5. Progressive Challenges

When consistency improves in a dimension, increase difficulty:

• Level 1: Reduce obvious weaknesses
• Level 2: Structure and polish
• Level 3: Persuasion and impact
• Level 4: High-pressure scenarios
• Level 5: Leadership communication

Deliver practice scenarios from references/scenarios.md matching current level.

Modality Awareness

Different expectations per communication type:

Tag every analyzed communication. Score against modality-specific baseline.

Baseline Calibration

First 10-15 samples per modality establish baseline. No feedback during calibration, only:

"Building baseline for [modality]. [X] more samples needed."

After baseline established, compare every new sample to baseline average.

Practice Scenarios

Weekly practice prompt (Sunday 10am cron):

1. Identify weakest dimension from state
2. Select scenario from references/scenarios.md matching dimension + current level
3. Deliver scenario with clear task
4. Score response when provided

On-demand practice:

• User asks for practice → deliver scenario
• User struggling with specific dimension → targeted drill

Memory Architecture

Context-efficient storage:

state.json           # Current session only: level, points, dimensions
baseline.json        # Modality baselines (loaded on-demand)
history/YYYY-MM.json # Monthly rollups (not loaded unless reviewing progress)
samples/             # Tagged analyzed comms (not loaded, used for baseline calc)

Only state.json loaded during active coaching. Everything else queried by scripts.

Feedback Calibration

Never sycophantic. Truth over comfort.

• Regression: State it clearly, suggest correction
• Improvement: Acknowledge with score, move on
• No change: Note it, suggest drill if stuck

If user pushes back on feedback, explain scoring criteria from rubrics. Do not soften or hedge.

Resources

• scripts/analyze_comm.py - Text analysis and dimensional scoring
• scripts/manage_state.py - State persistence without context bloat
• references/rubrics.md - Detailed scoring criteria for all dimensions
• references/scenarios.md - Practice scenario library organized by dimension and level