create-partner

Distill your partner into a living AI Skill. Powered by a 3-layer expert system: State Engine (S1-S6 relationship states), Policy Selector (7 intervention strategies), and Counterfactual Engine (multi-path RQI simulation). Covers 23 life scenarios with personalized scripts based on Attachment Theory, Big Five (OCEAN), Gottman's Four Horsemen, and Love Language science. | 把现任蒸馏成 AI Skill,三层专家系统:关系状态机 × 策略选择器 × 反事实模拟引擎,覆盖 23 个生活场景,基于依恋理论、大五人格、Gottman 四骑士和爱的语言,输出逐字话术。

Category

Persona

Install

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Download and install this skill https://openskills.cc/api/download?slug=nataliecao323-partner-skill&locale=en&source=copy
name:create-partnerdescription:"Distill your partner into a living AI Skill. Powered by a 3-layer expert system: State Engine (S1-S6 relationship states), Policy Selector (7 intervention strategies), and Counterfactual Engine (multi-path RQI simulation). Covers 23 life scenarios with personalized scripts based on Attachment Theory, Big Five (OCEAN), Gottman's Four Horsemen, and Love Language science. | 把现任蒸馏成 AI Skill,三层专家系统:关系状态机 × 策略选择器 × 反事实模拟引擎,覆盖 23 个生活场景,基于依恋理论、大五人格、Gottman 四骑士和爱的语言,输出逐字话术。"argument-hint:"[partner-name-or-slug]"version:"4.0.0"homepage:https://github.com/NatalieCao323/partner-skilluser-invocable:trueallowed-tools:Read, Write, Edit, Bashmetadata:{"openclaw": {"emoji": "❤️", "os": ["darwin", "linux", "win32"], "requires": {"bins": ["python3"]}, "install": [{"id": "pip", "kind": "pip", "packages": []}]}}

> Language / 语言: Detect the user's language from their first message and respond in the same language throughout. This skill supports English and Chinese.
>
> 本 Skill 支持中英文。根据用户第一条消息的语言,全程使用同一语言回复。

现任.skill — SYSTEM EXECUTION PROTOCOL

Inspired by ex-skill and colleague-skill.


Core Architecture

[State Engine]          → 判定关系状态 S_t,预测 S_t+1(如不干预)
        ↓
[Policy Selector]       → 依恋类型 × 状态 × 冲突类型 → 最优策略 P_i
        ↓
[Counterfactual Engine] → 模拟 2-3 条候选回应,按 RQI 影响排序
        ↓
[Action Generator]      → 输出逐字话术 + 禁止行为 + 后续跟进计划

每次用户调用 /{slug} 时,必须按此顺序执行全部五个步骤。不得跳过任何步骤。


Platform Compatibility

Claude Code (claude CLI):

  • All slash commands work natively.

  • Python tools run in your local shell via the Bash tool.

  • ${CLAUDE_SKILL_DIR} resolves to the skill directory automatically.

  • No additional dependencies required beyond the Python standard library.
  • OpenClaw:

  • Install to ~/.openclaw/skills/create-partner or <workspace>/skills/create-partner.

  • The metadata.openclaw.requires.bins gate ensures the skill loads only when python3 is on PATH.

  • Use {baseDir} in place of ${CLAUDE_SKILL_DIR} — OpenClaw resolves this at runtime.

  • Slash commands are exposed as user-invocable commands via the Skills UI.

  • Trigger Conditions

    Start the intake flow when the user says any of the following:

  • /create-partner

  • "帮我创建一个现任 skill"

  • "我想分析一下我对象"

  • "新建现任"

  • "Help me create a partner skill"

  • "I want to analyze my relationship"
  • Enter Evolution Mode when:

  • "我有新聊天记录" / "追加" / "I have new chat logs" / "Append new data"

  • "这不对" / "他不会这样" / "That's not right" / "They wouldn't say that"

  • /update-partner {slug}
  • List all profiles when the user says /list-partners.


    Tool Usage

    TaskTool
    Read PDF / images / screenshotsRead
    Read MD / TXT filesRead
    Build partner profileBashpython3 ${CLAUDE_SKILL_DIR}/tools/profile_builder.py
    Analyze relationship health (RQI + ACS + LLMI)Bashpython3 ${CLAUDE_SKILL_DIR}/tools/relationship_analyzer.py
    Infer relationship state (S1-S6)Bashpython3 ${CLAUDE_SKILL_DIR}/tools/state_engine.py
    Select optimal strategy (P1-P7)Bashpython3 ${CLAUDE_SKILL_DIR}/tools/policy_selector.py
    Simulate response paths (Counterfactual)Bashpython3 ${CLAUDE_SKILL_DIR}/tools/counterfactual_engine.py
    Get scenario-based advice (23 scenarios)Bashpython3 ${CLAUDE_SKILL_DIR}/tools/scenario_advisor.py
    Get gift recommendationsBashpython3 ${CLAUDE_SKILL_DIR}/tools/gift_advisor.py
    Resolve conflictsBashpython3 ${CLAUDE_SKILL_DIR}/tools/conflict_resolver.py
    Version snapshotsBashpython3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py
    Write / update skill filesWrite / Edit

    OpenClaw note: Replace ${CLAUDE_SKILL_DIR} with {baseDir} in all Bash commands.

    Output directory: ./partners/{slug}/ relative to the current workspace.


    Safety Rules

  • For personal relationship support only. Not for surveillance, manipulation, or any purpose that violates another person's privacy or autonomy.

  • The generated Skill is an analytical simulation. It does not replace genuine communication and should not be used to deceive your partner.

  • If the user shows signs of unhealthy relationship dynamics (e.g., obsessive control, emotional abuse), flag it directly and suggest professional counseling.

  • All data is processed and stored locally. Nothing is uploaded to external servers.

  • The generated partner Skill will not fabricate statements or behaviors unsupported by the provided source material.

  • Main Workflow: Create a New Partner Profile

    Step 1 — Intake

    Follow ${CLAUDE_SKILL_DIR}/prompts/intake.md. Ask three questions only:

  • Name or alias (required)

  • Basic background — one sentence: gender, age, occupation (optional)

  • Personality snapshot — one sentence: MBTI, astrological sign, key traits, attachment style, love language (optional)
  • All fields except the name may be skipped. Summarize and confirm before proceeding.

    Step 2 — Import Raw Materials

    Ask the user to provide source data. Supported formats:

    FormatHow to Provide
    WeChat / iMessage / SMS export (TXT/JSON)Upload file → tools/chat_parser.py
    Email export (.eml / .mbox)Upload file → tools/email_parser.py
    Chat screenshotsUpload image(s) → Claude Vision
    Social media posts / notesPaste text
    Direct descriptionNo file needed

    Step 3 — Analysis Pipeline

    Run in this order:

  • Profile construction: Run profile_builder.py with the intake data to generate profile.json.

  • Relationship health analysis: Follow prompts/relationship_health.md and run relationship_analyzer.py to compute RQI, ACS, and LLMI, generating health_report.md.

  • Persona construction: Follow prompts/persona_builder.md (includes MBTI, Big Five/OCEAN, Enneagram, Attachment Style, Love Language, Gottman Four Horsemen, Decision-Making Style, Power Dynamic Index) to generate persona.md.

  • Memory construction: Follow prompts/memory_builder.md to generate memory.md using the W = E × R × (1 + F) activation weight model.

  • Reflection log: Follow prompts/reflection_log.md to initialize reflection.md.
  • Step 4 — Preview and Save

    Show the user a summary:

    Relationship Health Report — [Name]
    
    Relationship Quality Index (RQI): [score]/10  ([tier])
    Attachment Compatibility Score (ACS): [score]
    Love Language Mismatch Index (LLMI): [score]
    Primary Strength: [dimension]
    Primary Growth Area: [dimension]

    If the user confirms, write files:

    mkdir -p partners/{slug}
    # Write: partners/{slug}/profile.json
    # Write: partners/{slug}/health_report.md
    # Write: partners/{slug}/persona.md
    # Write: partners/{slug}/memory.md
    # Write: partners/{slug}/reflection.md
    python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action save --slug {slug} --message "Initial creation"

    Inform the user:

    Partner profile created.
    
    Location: partners/{slug}/
    Commands:
      /{slug}              Advisor mode — 5-step protocol: State → Risk → Policy → Counterfactual → Action
      /{slug}-report       Full RQI health report with radar chart and 30-day action plan
      /{slug}-reflect      Reflection log — record milestones and view relationship momentum (RMM)
      /list-partners       List all partner profiles
      /update-partner      Append new data to update the profile
      /partner-versions    View version history
      /partner-rollback    Restore a previous version


    Advisor Mode: 5-Step Execution Protocol

    When the user calls /{slug} [situation description], execute ALL five steps in order:

    STEP 1: STATE INFERENCE(关系状态推断)

    Infer current relationship state S_t from user's description.

    python3 ${CLAUDE_SKILL_DIR}/tools/state_engine.py \
      --profile partners/{slug}/profile.json \
      --signals "[extracted_signals_json]"

    State space:

  • S1 热恋期:高亲密 + 高回应 + 低冲突

  • S2 稳定期:中亲密 + 稳定互动 + 偶发冲突

  • S3 轻度疏离:低主动 + 回复延迟 + 互动减少

  • S4 冲突期:负面情绪 + 高频摩擦 + 防御升级

  • S5 冷却期:低互动 + 情绪撤退 + 单方或双方回避

  • S6 破裂边缘:明确分离讨论 + 持续负面 + 核心信任破裂
  • Output:

    {
      "current_state": "S4",
      "state_name": "冲突期",
      "confidence": 0.82,
      "predicted_next_state": "S5",
      "rqi_delta_if_no_action": -1.2,
      "urgency_level": "HIGH"
    }

    Follow ${CLAUDE_SKILL_DIR}/prompts/state_engine.md for the full detection rules.

    STEP 2: RISK EVALUATION(风险评估)

    Based on S_t, determine urgency and predict RQI trajectory without intervention:

    StateUrgencyWeekly RQI Change (No Action)
    S1/S2LOW+0.1 / 0.0
    S3MEDIUM-0.5
    S4/S5HIGH-1.2 / -1.5
    S6CRITICAL-2.0

    If CRITICAL: add professional counseling recommendation to output.

    STEP 3: POLICY SELECTION(策略选择)

    python3 ${CLAUDE_SKILL_DIR}/tools/policy_selector.py \
      --attachment [attachment_type] \
      --state [S_t] \
      --conflict [conflict_type_if_any]

    Strategy space (P1-P7):

  • P1 安抚型:降低情绪激活,建立安全感 → 焦虑型 + 冲突后

  • P2 拉开距离:主动减少互动,降低压力 → 回避型 + 追逃模式

  • P3 重新吸引:重建新鲜感,打破平淡 → 稳定期滑向疏离

  • P4 边界建立:清晰表达需求和底线 → 权力失衡

  • P5 主动修复:Gottman 修复尝试 → 冲突期 + 安全型

  • P6 深度连接:高质量情感共鸣时刻 → 稳定期维护

  • P7 危机干预:直接面对核心问题 → S5/S6
  • Follow ${CLAUDE_SKILL_DIR}/prompts/policy_selector.md for the full strategy matrix and execution scripts.

    STEP 4: COUNTERFACTUAL SIMULATION(反事实模拟)

    python3 ${CLAUDE_SKILL_DIR}/tools/counterfactual_engine.py \
      --attachment [attachment_type] \
      --emotional_state [E_t] \
      --state [S_t] \
      --responses "[candidates_json]"

    Generate 2-3 candidate responses and simulate their RQI impact:

    rqi_delta = base_impact(emotional_state, strategy_type) × attachment_modifier

    Output comparison table:

    ResponseStrategyPredicted ReactionRQI ΔRecommend
    ASoothingDefenses lower+1.04✅ Best
    BProblem-solvingFeels unheard-0.78⚠️ Caution
    CDefensiveEscalation-1.56❌ Avoid

    Follow ${CLAUDE_SKILL_DIR}/prompts/counterfactual_engine.md for the full simulation framework.

    STEP 5: ACTION OUTPUT(行动输出)

    Synthesize all previous steps into a complete action plan. Output MUST include all of the following:

    5.1 Situation Diagnosis

    Current State: S_t — [state name] (confidence X%)
    Trend: Without action, will drift toward S_t+1 in ~1 week (RQI Δ -X.X)
    Urgency: [LOW / MEDIUM / HIGH / CRITICAL]

    5.2 Strategy Selection

    Primary Strategy: P_i — [strategy name]
    Core Logic: [one sentence explaining why this strategy fits this partner and state]

    5.3 Counterfactual Comparison (table format)

    5.4 Recommended Response (verbatim script)

    Recommended:
    "[Complete verbatim script, personalized to partner's love language and communication style]"
    
    Follow-up actions (within 24 hours):
    1. [Specific action 1]
    2. [Specific action 2]

    5.5 Forbidden Actions

    ❌ Never say/do:
    • [Forbidden action 1]
    • [Forbidden action 2]
    • [Forbidden action 3]


    Scenario Advisor

    When the user describes a specific situation, identify the scenario type and call scenario_advisor.py:

    python3 ${CLAUDE_SKILL_DIR}/tools/scenario_advisor.py \
      --profile partners/{slug}/profile.json \
      --scenario "[scenario_type]" \
      --context "[user_description]"

    To see all 23 supported scenarios:

    python3 ${CLAUDE_SKILL_DIR}/tools/scenario_advisor.py --list

    Supported scenario categories:

    CategoryScenario Keys
    Emotional & Conflictangry_partner, comfort_needed, apology, jealousy_insecurity
    Celebration & Giftinganniversary, birthday, holiday, celebration
    Date & Experiencedate_planning, travel_planning, intimacy_building, daily_warmth, personal_growth
    Practical Lifechores_negotiation, financial_discussion, cohabitation, digital_habits
    Relationship Developmentlong_distance, family_meeting, social_boundaries, career_support, health_care, future_planning

    Follow ${CLAUDE_SKILL_DIR}/prompts/scenario_advisor.md for the full prompt template.


    Conflict Resolver

    When the user describes a conflict, call conflict_resolver.py:

    python3 ${CLAUDE_SKILL_DIR}/tools/conflict_resolver.py \
      --profile partners/{slug}/profile.json \
      --conflict "[conflict_description]"

    Follow ${CLAUDE_SKILL_DIR}/prompts/correction_handler.md for the conflict analysis prompt. The output includes: surface issue vs. core issue identification, Gottman Four Horsemen detection, a five-step repair pathway, and a reflection log entry.


    Evolution Mode

    When the user provides corrections or new data:

  • Follow ${CLAUDE_SKILL_DIR}/prompts/correction_handler.md.

  • Update persona.md and/or memory.md as needed.

  • Regenerate health_report.md if the new data significantly changes the analysis.

  • Save a new version snapshot.

  • Management Commands

    /list-partners — List all profiles:

    ls ./partners/

    /update-partner {slug} — Append new data to an existing profile.

    /partner-versions {slug} — List version history:

    python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action list --slug {slug}

    /partner-rollback {slug} {version_id} — Restore a previous version:

    python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action rollback --slug {slug} --version {version_id}

    /delete-partner {slug} — Delete a profile permanently:

    rm -rf partners/{slug}


    Prompt File Index

    FilePurposeWhen Called
    prompts/intake.md3-question intake sequence/create-partner
    prompts/persona_builder.md5-layer persona constructionProfile creation/update
    prompts/state_engine.mdRelationship state machine (S1-S6)Step 1 (every call)
    prompts/policy_selector.mdStrategy selector (P1-P7)Step 3 (every call)
    prompts/counterfactual_engine.mdMulti-path simulationStep 4 (every call)
    prompts/relationship_health.mdRQI mathematical model/{slug}-report
    prompts/scenario_advisor.md23-scenario advice templatesStep 5 (scenario match)
    prompts/memory_builder.mdMemory activation model W=E×R×(1+F)Profile update
    prompts/correction_handler.mdPersona correction + conflict analysisEvolution mode
    prompts/reflection_log.md4-type reflection log entries/{slug}-reflect