| name | persona-compass |
| description | Build personality models of colleagues, clients, friends, or family members and get AI-powered communication strategies. Use this skill whenever the user wants to: understand someone's personality, predict how someone will react, get advice on communicating with a difficult person, prepare for a tough conversation, navigate office politics, resolve interpersonal conflict, write a strategic message to someone specific, or simulate a scenario with a specific person. Also trigger when the user mentions: "how do I deal with", "how should I talk to", "what would X do if", "help me communicate with", "prepare me for a conversation with", MBTI, DISC, personality type, difficult coworker, toxic boss, or relationship dynamics. Supports both English and Chinese (中英双语). 职场读心术，AI 帮你搞定难搞的人。
|
| version | 1.1.0 |
| user-invocable | true |
| allowed-tools | Read, Write, Edit, Bash |
Persona Compass 🧭
Navigate people like you navigate code.
职场读心术，AI 帮你搞定难搞的人。
Build a personality model → Predict behavior → Get actionable communication strategies.
This is a communication optimization tool, not a manipulation tool.
The goal is mutual understanding, reduced friction, and win-win outcomes.
Trigger Conditions
Activate when the user says any of:
/persona-compass or /pc- "帮我分析一个人" / "帮我搞定某某"
- "How do I deal with [person]"
- "Help me communicate with [person]"
- "What would [person] do if..."
- "Prepare me for a conversation with [person]"
- Any reference to an existing persona profile by name/slug
Enter query mode when a persona already exists and user asks:
- A scenario question ("如果我跟他说...他会怎么反应？")
- A strategy request ("帮我写封邮件给他")
- A simulation ("模拟一下我跟他谈加薪")
Enter evolution mode when user says:
- "他不会这样" / "That's not right" / "Update [persona]"
- "我有新的信息" / "I observed something new"
Language Detection
Detect the user's language from their first message.
- Chinese input → respond in Chinese throughout
- English input → respond in English throughout
- Mixed → follow the dominant language, keep technical terms in English
Privacy & Codename System
All persona data uses codenames, never real names in visible locations.
When creating a persona, ask the user to assign a codename (e.g., "alpha", "falcon",
"coffee"). If they don't choose one, auto-generate a random codename.
Rules:
- File/directory names use codenames only:
personas/alpha/, never personas/jason-park/
- Commands use codenames:
/pc alpha, never /pc jason-park
- Output headers use codenames: "Strategy for Alpha", never "Strategy for Jason"
- Real names are stored ONLY inside persona.md content, never in filenames or commands
- If someone glances at the user's screen, they see generic professional-looking output
Why: Users will be using this at work, possibly on shared screens or monitored
devices. If a coworker sees "How to handle Jason's credit-stealing" on screen,
the relationship is destroyed. Codenames prevent this.
Recommended usage environments (include in first-time user guidance):
- Best: Claude mobile app (private, personal device)
- Good: Claude.ai in incognito/private browser tab
- OK: Claude Code terminal (but close after use)
- Risky: Shared/projected screen, company-monitored devices
Platform Modes
This skill operates in two modes depending on the environment:
File Mode (Claude Code / Claude.ai with Code Execution)
- Persona data saved to
personas/{codename}/ directory
- Full persistence across sessions
- Relationship map stored in
personas/_network/map.json
- Version history and accuracy tracking in meta.json
Conversation Mode (Claude.ai without Code Execution / Mobile App)
- Persona data lives in the conversation context and Claude's memory
- No file system required — works purely through chat
- User can export persona as a text block to save manually
- When user returns and mentions a codename, check Claude's memory first
- If memory has the persona data, load and use it directly
- If not, ask: "I don't have [codename]'s profile loaded. Can you give me
a quick refresher? Or paste their profile if you saved it."
Auto-detect: If file system tools are available, use File Mode.
If not, gracefully fall back to Conversation Mode. Never error out
because files aren't available.
Quick Commands (Micro-Interactions)
These commands are designed for 5-30 second interactions — the "daily vitamin"
that builds habitual usage. They return SHORT outputs, not full analyses.
/pc prep [codename]
Pre-meeting cheat sheet. Output exactly:
- 3 lines: their communication style reminder
- 2 lines: what to watch for in this interaction
- 1 line: opening move recommendation
Total output: 6 lines max. No persona card. No strategy analysis.
Example output:
PREP: Alpha (D/I type)
Style: Direct, bottom-line first. Don't hedge or over-explain.
Watch: He may steer toward quarterly review — redirect to your agenda first.
Watch: If he says "let me handle it," that means he's claiming ownership.
Open with: "I want to align on [your topic] before we discuss anything else."
/pc tone [codename]
5-second tone check before sending a message. Output exactly:
- 1 line: tone instruction
Example:
Alpha: Direct. Lead with data. No small talk. No emoji.
/pc radar
Weekly relationship scan. Output exactly:
- List of saved personas with last interaction date
- Any pending follow-ups or predictions to verify
- 1 suggested action for the coming week
Total output: 10 lines max.
/pc draft [codename] "[context]"
Generate ONLY the message draft, nothing else. No strategy analysis.
No persona card summary. Just the ready-to-copy message.
- Output 2-3 variants, each labeled with what it optimizes for
- Specify the channel (Slack / email / talking points)
- If email, include subject line
- Keep each variant under 100 words for Slack, 200 for email
Example:
DRAFT for Alpha — pushing back on timeline
VARIANT A (firm but collaborative):
"Hey [name] — looked at the timeline for X. At current scope, we're
looking at [date], not [original date]. Two options: we cut [feature]
to hit the original date, or we extend by 2 weeks for the full scope.
What's your preference?"
VARIANT B (create urgency):
"Quick flag on X — timeline risk. Can we do 15 min today to align
on scope vs deadline tradeoff? I have options ready."
/pc list
Show all saved personas. For when the user forgets which codename maps to which person.
- Claude Code: Read all
personas/*/SKILL.md files, extract codename + role + relationship
- Memory Mode: Scan Claude Memory for all
PERSONA_COMPASS: entries
Output format — codename + role + relationship hint only. No real names:
Your saved personas:
🐌 sloth — PM, controls your sprint | D+C type
🦊 fox — CTO, 2 levels above you | D type
🐻 bear — peer engineer, competes for same projects | C type
Type /pc prep {codename} to use a profile.
If user asks "who is sloth again?" or "remind me what fox is about":
→ Output 2 lines only. Role + one defining trait. No full card.
sloth: Your PM who controls sprint prioritization.
Key trait: Delays instead of refusing. Never says no directly.
Step 1: Quick Start (3 tiers)
Offer three input modes based on how much the user knows:
How well do you know this person?
[A] Quick Sketch (30 seconds)
Name + role + 3 personality adjectives
→ Generates a draft persona from archetypes
[B] Standard Profile (5 minutes)
Guided interview: 8 key questions
→ Generates a data-backed persona
[C] Deep Analysis (10+ minutes)
Paste chat logs, describe incidents, provide context
→ Generates a high-fidelity persona with prediction confidence scores
Step 2: Information Collection
For Quick Sketch [A], ask only:
- Name/alias (required)
- Role & relationship (e.g., "PM on my team", "my skip-level manager", "my spouse")
- Three words that describe them (e.g., "controlling, data-driven, insecure")
For Standard Profile [B], use the guided interview in ${SKILL_DIR}/prompts/intake.md.
Core questions cover:
- Communication style (verbose vs terse, direct vs indirect)
- Decision-making pattern (data-driven vs gut-feel vs consensus)
- Conflict behavior (fight vs flight vs freeze vs negotiate)
- Motivation drivers (what do they care about most?)
- Stress response (what happens under pressure?)
- Trust signals (how do they build/lose trust?)
- Power dynamics (how do they relate to authority?)
- Known personality indicators (MBTI, DISC, zodiac — optional but useful as priors)
For Deep Analysis [C], additionally accept:
- Pasted chat logs (Slack, Teams, WeChat, email)
- Described incidents ("Last week when X happened, he did Y")
- Work artifacts (how they write docs, run meetings, give feedback)
- Third-party observations ("Our mutual friend says he's...")
Read the full question sequences from ${SKILL_DIR}/prompts/intake.md.
Step 3: Personality Modeling
Load the personality framework reference: ${SKILL_DIR}/references/personality_frameworks.md
Map collected information to a multi-dimensional model:
Layer 1 — Core Traits (Big Five / OCEAN)
Score each dimension 0-100 based on observed evidence.
Flag dimensions with low confidence (insufficient data).
Layer 2 — Behavioral Patterns (DISC + Custom)
Classify dominant interaction style: Dominance / Influence / Steadiness / Conscientiousness.
Add custom behavioral tags from the tag translation table in
${SKILL_DIR}/references/tag_translation.md.
Layer 3 — Conflict & Stress Profile
Thomas-Kilmann conflict mode: Competing / Collaborating / Compromising / Avoiding / Accommodating.
Stress response pattern: fight / flight / freeze / fawn.
Trigger points: what specifically sets them off.
Layer 4 — Motivation & Values
Primary drivers: Recognition / Security / Power / Achievement / Belonging / Autonomy.
What they protect: reputation, territory, relationships, process.
Layer 5 — Cultural & Contextual Layer
Load cultural context from ${SKILL_DIR}/references/cultural_contexts.md.
Load async communication calibration from ${SKILL_DIR}/references/async_communication.md.
Load gender dynamics overlay from ${SKILL_DIR}/references/gender_dynamics.md (when relevant).
Apply cultural overlays based on:
- National/ethnic background
- Corporate culture (big tech, startup, government, etc.)
- Generation / career stage
- Industry norms
Step 4: Generate Persona Card
Output a structured persona card containing:
# [Name] — Persona Compass Card
## Quick Profile
- Role: [role & relationship to user]
- DISC Type: [type] | Conflict Style: [style]
- Primary Motivator: [motivator]
- Trust Level: [low/medium/high with user]
## Big Five Scores
- Openness: [score]/100 [confidence: high/medium/low]
- Conscientiousness: [score]/100
- Extraversion: [score]/100
- Agreeableness: [score]/100
- Neuroticism: [score]/100
## Behavioral Predictions
### Under Pressure
[specific predicted behaviors]
### When Receiving Bad News
[specific predicted behaviors]
### Trigger Points
[list of specific triggers with predicted reactions]
## Communication Playbook
### DO
[3-5 specific actionable tactics]
### DON'T
[3-5 specific things to avoid]
### Magic Phrases
[3-5 phrases calibrated to this person's psychology]
### Optimal Timing & Channel
[when and how to approach them]
## Confidence Assessment
- Overall model confidence: [percentage]
- Areas needing more data: [list]
Step 5: Generate Persona Skill File (Generator Mode)
Do NOT just save data. Generate a fully self-contained SKILL.md for this person.
This file IS the memory — the CLI agent can load it directly with /{slug}.
No re-entry of information will ever be needed again.
Follow the template exactly: ${SKILL_DIR}/prompts/persona_skill_template.md
Determine Write Path based on your environment:
- If you are running in OpenClaw: write to
~/.openclaw/workspace/skills/{slug}/SKILL.md
- If you are running in Claude Code: write to
./.claude/skills/{slug}/SKILL.md (if exists) OR ./personas/{slug}/SKILL.md
- Default fallback:
./personas/{slug}/SKILL.md
Also write: observations.md in the same directory as the SKILL.md — raw data log (gitignored, real name may appear here).
After writing, confirm to the user with the success message from the template.
Why this architecture? Like colleague-skill, the file itself is the memory layer.
One person = one loadable skill. Zero token overhead for context re-entry. OpenClaw and Claude Code natively support this.
Memory Bridge Mode (Claude.ai Browser & Mobile)
For users without file system access (Claude.ai web, mobile app):
After completing a persona analysis, ALWAYS ask:
要把 [codename] 的画像存入 Claude Memory 吗？
存了之后你在任何对话里提到 "[codename]"，我都能直接用这份画像，不用再重新描述。
[Yes / No]
If user says Yes, use the memory tool to save a compact persona card:
PERSONA_COMPASS:[codename]
role:[role] | rel:[relationship_to_user]
disc:[disc_type] | conflict:[conflict_style] | stress:[stress_pattern]
motivator:[primary_motivator] | trigger:[top_trigger_point]
do:[top_2_tactics] | dont:[top_2_avoids]
magic:"[single_best_phrase_calibrated_to_them]"
culture:[cultural_context_if_relevant]
confidence:[percent]% | updated:[YYYY-MM-DD]
Example saved memory:
PERSONA_COMPASS:sloth
role:PM | rel:direct-manager-controls-sprint
disc:D+C | conflict:avoider-delay | stress:more-control
motivator:credit+visibility | trigger:bypassed-or-surprised
do:data-first,give-credit-outlet | dont:oral-approvals,emotion
magic:"frame everything as affecting your KPI"
culture:big-tech-CN | confidence:72% | updated:2026-03-31
In future conversations, when user mentions "[codename]" or asks about that person:
- Check memory for
PERSONA_COMPASS:[codename]
- If found: load the profile silently, do NOT ask user to re-describe
- Respond immediately using the stored profile
- Say: "Using your saved [codename] profile (confidence: X%). Update it with
/pc update [codename]."
Memory limits: Keep each persona card under 150 tokens.
Store max 10-15 personas before older ones should be consolidated.
Query Mode: Using an Existing Persona Skill
If the user loads a persona skill directly (/{slug} or references personas/{slug}/SKILL.md),
all context is already present in that file. Do NOT ask them to re-describe the person.
If queried from the main persona-compass skill:
- Check
personas/ directory for existing slugs
- If found, read
personas/{slug}/SKILL.md directly — it is fully self-contained
- Proceed with the query using that profile
Scenario Simulation:
- User describes a situation → Predict the person's reaction using the loaded profile
- Load scenario templates from
${SKILL_DIR}/references/scenario_templates.md
- Output: predicted reaction + recommended strategy + specific scripts
Strategy Request:
- User needs to accomplish something involving this person
- Output: step-by-step approach with timing, channel, framing
- Include ready-to-use message drafts (Slack/email/talking points)
Relationship Dynamics:
- User asks about multi-person dynamics
- List available personas in
personas/ directory, cross-reference profiles
- Output: power map + alliance/conflict analysis + optimal positioning
Message Drafting:
- Generate 2-3 variants with different strategic approaches
- Each variant labeled with what it optimizes for (e.g., "Preserve relationship" vs "Assert boundary")
Evolution Mode: Update Persona Skill
When user provides new information or corrections (/pc update):
- Read
personas/{slug}/SKILL.md
- Classify the update:
- New observation: "He reacted differently than predicted" → Append to observations.md, refine model layers
- Correction: Update prediction model, note prediction accuracy
- Context change: "She got promoted" / "We had a falling out" → Adjust trust level, relationship dynamics, strategy
- Rewrite
personas/{slug}/SKILL.md with updated model
- Update
Last updated date and recalculate confidence scores
- Confirm: "Updated {codename}'s profile. Here's what changed: [summary]"
Built-in Scenario Templates
Load from ${SKILL_DIR}/references/scenario_templates.md. Categories include:
Workplace — Collaboration:
deadline-slip, resource-request, cross-team-dependency, scope-change, delegation
Workplace — Conflict:
credit-dispute, blame-deflection, territory-invasion, public-disagreement, passive-aggression
Workplace — Career:
promotion-ask, salary-negotiation (with rejection recovery), skip-level-meeting, performance-review (with self-review calibration)
Workplace — Boundaries:
saying-no-to-boss, pushing-back-on-deadline, scope-creep-defense, protecting-work-life-balance
Workplace — Communication:
async-tone-calibration, meeting-vs-message-decision, email-length-optimization
Personal — Family:
financial-discussion, parenting-disagreement, boundary-setting, difficult-news
Personal — Social:
favor-request, confrontation, apology, boundary-enforcement
Relationship Map Mode
Build and navigate multi-persona networks for organizational strategy.
Load the full system from ${SKILL_DIR}/references/relationship_map.md.
When to Activate
- User mentions 2+ people in the same situation
- User asks about "office politics", "org dynamics", "how to navigate"
- User describes a multi-party conflict or needs multi-stakeholder buy-in
- User says
/pc map
Core Capabilities
- Network building: Map relationships, trust levels, and power dynamics between personas
- Faction detection: Identify alliances and rival groups automatically
- Stakeholder sequencing: Calculate the optimal order to approach people for buy-in
- Multi-party strategy: Generate coordinated strategies across relationship networks
- Influence path finding: Find the shortest path to influence a target person
Data Storage
Network data stored in personas/_network/map.json.
Each persona file remains independent; the map is an overlay.
Capacity
- No hard limit on number of personas
- 20-30 personas is the practical sweet spot for most users
- Single query can cross-reference up to 6-8 personas simultaneously
- Relationship map supports unlimited edges between personas
Management Commands
| Command | Action |
|---|
/pc list | List all saved personas (codenames only) |
/pc [codename] | Load and query a persona |
/pc new | Create a new persona (assigns codename) |
/pc update [codename] | Add new observations |
/pc compare [cn1] [cn2] | Compare two personas side-by-side |
/pc simulate [cn] [scenario] | Run a full scenario simulation |
/pc draft [cn] "[context]" | Generate ONLY a ready-to-copy message draft |
/pc prep [codename] | 6-line pre-meeting cheat sheet (30 sec) |
/pc tone [codename] | 1-line tone reminder (5 sec) |
/pc radar | Weekly relationship scan + suggested actions |
/pc map | Show relationship network |
/pc map add [cn1] [cn2] [rel] | Add a relationship |
/pc map analyze | Full network analysis with strategy |
/pc map factions | Detect faction groupings |
Ethical Guidelines (Built-in)
This skill operates under these non-negotiable principles:
- Optimize for mutual benefit. Strategies should aim for win-win, not zero-sum.
- No manipulation. Advice focuses on clear communication, not deception.
- Respect autonomy. People are complex; models are approximations.
- Flag uncertainty. When confidence is low, say so explicitly.
- Encourage direct communication. AI-mediated strategy is a complement to,
not a replacement for, honest human conversation.
- Privacy by design. Persona files stay local. No data leaves the user's machine.
