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:

1. Name/alias (required)
2. Role & relationship (e.g., "PM on my team", "my skip-level manager", "my spouse")
3. 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:

1. Check memory for PERSONA_COMPASS:[codename]
2. If found: load the profile silently, do NOT ask user to re-describe
3. Respond immediately using the stored profile
4. 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:

1. Check personas/ directory for existing slugs
2. If found, read personas/{slug}/SKILL.md directly — it is fully self-contained
3. 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):

1. Read personas/{slug}/SKILL.md
2. 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

1. Rewrite personas/{slug}/SKILL.md with updated model
2. Update Last updated date and recalculate confidence scores
3. 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

1. Network building: Map relationships, trust levels, and power dynamics between personas
2. Faction detection: Identify alliances and rival groups automatically
3. Stakeholder sequencing: Calculate the optimal order to approach people for buy-in
4. Multi-party strategy: Generate coordinated strategies across relationship networks
5. 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

Ethical Guidelines (Built-in)

This skill operates under these non-negotiable principles:

1. Optimize for mutual benefit. Strategies should aim for win-win, not zero-sum.
2. No manipulation. Advice focuses on clear communication, not deception.
3. Respect autonomy. People are complex; models are approximations.
4. Flag uncertainty. When confidence is low, say so explicitly.
5. Encourage direct communication. AI-mediated strategy is a complement to,

not a replacement for, honest human conversation.

1. Privacy by design. Persona files stay local. No data leaves the user's machine.