Communication Coach OpenClaw Skill - ClawHub
Do you want your AI agent to automate Communication Coach workflows? This free skill from ClawHub helps with smart home & iot tasks without building custom tools from scratch.
What this skill does
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.
Install
npx clawhub@latest install communication-coachFull SKILL.md
Open original| name | description |
|---|---|
| communication-coach | 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:
| Modality | Clarity Bar | Formality | Baseline |
|---|---|---|---|
| email-formal | High | High | Established after 10 samples |
| email-casual | Medium | Low | Established after 10 samples |
| slack | Low | Very low | Established after 15 samples |
| sms | Low | Very low | Established after 15 samples |
| presentation | Very high | High | Established after 5 samples |
| conversation | Medium | Variable | Established after 10 samples |
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):
- Identify weakest dimension from state
- Select scenario from
references/scenarios.mdmatching dimension + current level - Deliver scenario with clear task
- 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