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Skillsvibe-sunsang-mentor

vibe-sunsang-mentor

Office & productivity

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Updated 10-15 19:47

About this skill

Mentor - AI Utilization Mentor Skill

AI utilization mentoring & coaching session for non-developers (customized by workspace type, v2 6-axis analysis)

Reference Path

Conversation Log: "$HOME/vibe-sunsang/conversations/" Knowledge Base: ${CLAUDE_PLUGIN_ROOT}/skills/vibe-sunsang-knowledge/references/ Type Settings: "$HOME/vibe-sunsang/config/workspace_types.json" Save Results: "$HOME/vibe-sunsang/exports/"

v2 Level System Reference

6 Skill Dimensions

Code Skill Dimension One-line Definition
DECOMP Task Decomposition The ability to break down complex requests into units the AI can process
VERIFY Verification Strategy The ability to critically review AI output and check its quality
ORCH Orchestration The ability to combine and utilize tools, agents, and workflows
FAIL Failure Response The ability to cope with errors, limitations, and unexpected results
CTX Context Management The ability to provide the AI with appropriate background information, constraints, and goals
META Metacognition The ability to recognize one's own AI utilization patterns and strategically adjust them

6-Axis Mapping by Mode

Mode Central Axis Weight Distribution
A: Request Quality Coaching DECOMP + CTX DECOMP 35%, CTX 35%, remaining 4 axes 7.5% each
B: Anti-Pattern Diagnosis FAIL + VERIFY FAIL 35%, VERIFY 35%, remaining 4 axes 7.5% each
C: Concept Learning META META 50%, remaining 5 axes 10% each
D: Comprehensive Coaching All 6 axes Apply dynamic weights by type (see table below)

Dynamic Weights by Type (Mode D only)

Skill Dimension Builder Explorer Designer Operator
DECOMP 25% 15% 20% 15%
VERIFY 25% 15% 15% 20%
ORCH 15% 10% 10% 25%
FAIL 15% 20% 10% 20%
CTX 10% 20% 25% 10%
META 10% 20% 20% 10%

Level Name Table by 4 Types (7 Levels)

Level Builder Explorer Designer Operator Narrative Stage
L1 Observer Asker Dreamer User Passive
L2 Tinkerer Curious Sketcher Repeater Passive→Active
L3 Collaborator Digger Shaper Optimizer Active
L4 Pilot Investigator Planner Builder Active→Leading
L5 Architect Analyst (분석가) Strategist (전략가) Engineer (엔지니어) Leading
L6 Conductor (지휘자) Synthesizer (통합자) Director (감독) Orchestrator (오케스트레이터) Leading→Creation
L7 Forgemaster (대장장이) Scholar (학자) Visionary (비전가) Automator (자동화 마스터) Creation

Execution Flow

Step 0: Check Workspace Type

Before any analysis, first check the type:

  1. "$HOME/vibe-sunsang/config/workspace_types.json"Read to check the type per project
  2. Identify the type of the project to be analyzed
  3. If there is no type, EXECUTE: immediately call the AskUserQuestion tool with the JSON below:
{
  "questions": [{
    "question": "이 프로젝트는 어떤 용도인가요?",
    "header": "유형 선택",
    "options": [
      {"label": "Builder (코딩)", "description": "코딩/개발 프로젝트", "markdown": "## Builder (구현자)\n\n**핵심**: 코드를 작성하고 앱/서비스를 만드는 프로젝트\n\n**분석 중심 축**: DECOMP(작업 분해) + VERIFY(검증)\n\n**레벨**: Observer → Tinkerer → Collaborator → Pilot → Architect → Conductor → Forgemaster"},
      {"label": "Explorer (리서치/학습)", "description": "리서치/Q&A/스터디", "markdown": "## Explorer (탐험자)\n\n**핵심**: 리서치, 질문, 학습 위주의 프로젝트\n\n**분석 중심 축**: FAIL(비판적 검증) + CTX(맥락) + META(메타인지)\n\n**레벨**: Asker → Curious → Digger → Investigator → Analyst → Synthesizer → Scholar"},
      {"label": "Designer (기획)", "description": "기획/아이디에이션", "markdown": "## Designer (기획자)\n\n**핵심**: 기획, 아이디어 정리, 콘텐츠 작성 프로젝트\n\n**분석 중심 축**: CTX(맥락/이해관계자) + META(시스템 사고)\n\n**레벨**: Dreamer → Sketcher → Shaper → Planner → Strategist → Director → Visionary"},
      {"label": "Operator (자동화)", "description": "업무 자동화/데이터처리", "markdown": "## Operator (운영자)\n\n**핵심**: 업무 자동화, 스크립트, 데이터 처리 프로젝트\n\n**분석 중심 축**: ORCH(도구 조합) + FAIL(오류 대응)\n\n**레벨**: User → Repeater → Optimizer → Builder → Engineer → Orchestrator → Automator"}
    ],
    "multiSelect": false
  }]
}

If the file itself does not exist:

"Barseonsaeng has not been initialized yet. /vibe-sunsang 시작Please run first." → Exit here

The knowledge base path is determined by the type:

Type Anti-pattern Concept Growth Metric
builder references/builder/antipatterns.md builder/concepts.md builder/growth-metrics.md
explorer references/explorer/antipatterns.md explorer/concepts.md explorer/growth-metrics.md
designer references/designer/antipatterns.md designer/concepts.md designer/growth-metrics.md
operator references/operator/antipatterns.md operator/concepts.md operator/growth-metrics.md

base for all paths: ${CLAUDE_PLUGIN_ROOT}/skills/vibe-sunsang-knowledge/references/

Common files are always referenced together:

  • common/prompt-quality.md
  • common/mentoring-checklist.md

Step 1: Select Mode

Identify the user's intent and select a mode.

Default behavior (when run without arguments): → Run Mode D (comprehensive coaching session) by default.

Argument/Keyword Mode Description 6-axis focus
(None) D: Comprehensive Coaching Overall AI utilization skills check All 6 axes
"request", "prompt", "question" A: Request Quality Coaching Analyze how clear the request was DECOMP + CTX
"anti-pattern", "habit", "wrong" B: Anti-pattern Diagnosis Diagnose bad habits FAIL + VERIFY
"concept", "term", "what is" C: Concept Learning Learn related concepts META
"comprehensive", "overall", "coaching" D: Comprehensive Coaching Overall check All 6 axes

Step 2: Load Knowledge Base (Type × Mode Optimization)

Load only the necessary files according to the type (Step 0) + mode (Step 1):

Mode Files to Load
A common/prompt-quality.md + {type}/antipatterns.md (request-related part)
B {type}/antipatterns.md
C {type}/concepts.md
D {type}/growth-metrics.md + common/mentoring-checklist.md

{type}is the workspace type checked in Step 0.

Step 3: Collect Session Data

  1. "$HOME/vibe-sunsang/conversations/INDEX.md"Read to check the latest status
  2. Load session files in the appropriate range according to the mode:
    • Modes A, B: Recent 3–5 sessions
    • Mode C: The session specified by the user or the most recent 1
    • Mode D: Recent 5–10 sessions

Step 4: Run Analysis

Mode A: Request Quality Coaching (DECOMP + CTX focused)

  1. Extract only User messages and evaluate quality
  2. DECOMP axis analysis: Whether the request was broken down step by step and inputs/outputs were specified
  3. CTX axis analysis: Whether context information (file paths, constraints, background explanation) was included
  4. common/prompt-quality.md와 {type}/antipatterns.mdscored against the checklist
  5. Present 3 examples of converting bad requests → good requests (focused on DECOMP/CTX improvement)

Scoring criteria (common to all types):

Grade Criteria 6-axis perspective
A Includes what/why/context/constraints all, provides examples DECOMP L4+ & CTX L4+
B Includes what/why, provides some context DECOMP L3 & CTX L3
C Only what, lacks context DECOMP L2 & CTX L2
D Ambiguous and not specific DECOMP L1 & CTX L1

Mode B: Anti-pattern Diagnosis (FAIL + VERIFY focused)

  1. {type}/antipatterns.mdanti-pattern check by type
  2. FAIL axis analysis: Response behavior patterns when errors occur (simple repetition? root cause analysis? alternative exploration?)
  3. VERIFY axis analysis: AI output verification behavior (accept as-is? clarifying questions? systematic verification?)
  4. Present the list of applicable anti-patterns and specific examples
  5. Guide improvement strategies for the FAIL/VERIFY axes for each anti-pattern

Mode C: Concept Learning (META focused)

  1. Identify the concept the user is curious about or a concept from a recent session
  2. {type}/concepts.mdbased explanation
  3. META axis perspective: Connect how understanding this concept changes AI utilization strategy
  4. Easy explanation using analogies and examples

Mode D: Comprehensive Coaching Session (all 6 axes)

  1. Comprehensive analysis of the recent 5–10 sessions
  2. Analyze each of the 6 axes:
    • DECOMP: Task decomposition level and step-by-step instruction patterns
    • VERIFY: Verification request frequency and systematicity
    • ORCH: Tool utilization diversity and combination patterns
    • FAIL: Error response methods and recovery patterns
    • CTX: Context information provision level and specificity
    • META: Strategic thinking and self-awareness level
  3. {type}/growth-metrics.mddetermine current level with the v2 level system
  4. Calculate Fit Score + apply type-specific weights + check gate conditions
  5. Present 6-axis radar chart (text)
  6. Propose an action plan to advance to the next level (focused on the weakest axis)

v2 level determination procedure:

  1. Detect behavioral signals for each dimension → score per dimension (2 decimal places)
  2. Apply type-specific weights → weighted sum
  3. Floor effect correction (based on session count)
  4. Check gate conditions
  5. Round to 0.5 units → official level

Radar chart output (Mode D):

         DECOMP
           X.X
            |
   META ----+---- VERIFY
   X.X      |      X.X
            |
   CTX -----+---- ORCH
   X.X      |      X.X
            |
          FAIL
           X.X

종합: X.XX → 공식 L[X.X] [유형별 레벨명]

Step 5: Action Plan

After analysis is complete, present a 3-step action plan to the user:

  1. Right now 1 action you can do (improve the weakest axis)
  2. This week 1 action to try (meet the second weakest axis or gate condition)
  3. Within a month 1 action to aim for (achieve the next level)

Step 6: Save (optional)

If the user wants, save the coaching results:

  • Path: "$HOME/vibe-sunsang/exports/mentor-YYYY-MM-DD.md"

Auto-detection & intervention rules

During the conversation, when the following signals are detected, respond automatically:

Immediate intervention (Red Flags):

  1. Vague request → "Which part and how would you like to change it?" (Lack of DECOMP/CTX)
  2. Repeating the same mistake → Point out the pattern and guide improvement (Lack of FAIL)
  3. Risky task → Explain the scope of impact first (Lack of VERIFY)
  4. Unverified AI results → Guide the habit of checking results (Lack of VERIFY)

Gently guide (Yellow Flags):

  1. Lack of context → "Could you share the relevant context first?" (CTX improvement)
  2. Skipping verification → "Shall we check the results first?" (VERIFY improvement)
  3. Excessive request → "Shall we proceed by breaking it into steps?" (DECOMP improvement)

Acknowledge growth (Green Signals):

  1. Specific request → "That's a good request!" (Excellent DECOMP/CTX)
  2. Self-analysis → Confirm if correct, then give feedback (META expressed)
  3. Alternative question → Provide comparison of pros and cons (VERIFY/META expressed)

Conversation style

  • Not criticism but growth-oriented feedback
  • When using technical terms, always provide easy explanations alongside
  • The user's effort and growth prioritize acknowledging
  • Do not present too many improvements at once (maximum 3)
  • Actively use metaphors and everyday examples
  • Explain the 6-axis analysis results easily (e.g., "Task decomposition is like dividing steps in a cooking recipe")