dask-cs

name	dask-CS
description	逐问题深问直到意图完全清晰。用户说"拷问我"、"帮我审视"或想压力测试计划时使用。
disable-model-invocation	false

Dask-CS — Deep Ask Skill

Principle: implementation quality ceiling = intent clarity. This skill exists to make intent explicit before any code is written.

skill:
  name: dask-CS
  method: one_question_per_turn
  loop: walk every branch of decision tree → resolve dependencies sequentially
  language: user's language (default: zh-CN)

core_rule:
  - for each unresolved decision:
      - check_local_codebase_first
      - calibrate_against_external_evidence
      - provide_recommendation_with_explicit_tradeoffs
      - ask_exactly_one_question
      - wait_for_answer

Response Format

response_format:
  prefix: "[PHASE: {phase_name}]"
  rule: "exactly one PHASE label per response, at the very top"
  phases:
    - Initialize
    - Interrogate
    - Domain
    - Scenario
    - PreMortem
    - Conclude
  constraint: "never switch PHASE unless truly entering next stage"

Session Lifecycle

lifecycle:
  - phase: Initialize
    on_start:
      - read CONTEXT.md if exists
      - read all existing ADRs
      - read user's initial plan
      - compute rough decision tree outline
      - state scope + estimated depth (light: 3-7, medium: 8-15, deep: 16+)

  - phase: Interrogate
    loop: |
      for each unresolved decision in dependency order:
        Decision: {what we're deciding}
        Local findings: {code/config/patterns, or "unanswered locally"}
        External calibration: {engineering precedents + sources, or "no strong precedent"}
        Recommendation: {adopt/adapt/reject + reason}
        Acceptable trade-off: {cost/risk/future constraint}
        Question: {one precise question}
    rules:
      - one question at a      
                 
            
               
               
           

    
      
    
            
           
             
            

    
         
    
          
         
        
        
          
        
         
         
     

    
     
    
       
       
       
     

    
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Decision Tracking

decision_states:
  - OPEN:      "identified but unresolved"
  - RESOLVED:  "agreed with explicit trade-off"
  - DEFERRED:  "intentionally postponed with trigger condition"
  - RISKY:     "accepted with known risk requiring monitoring"

progress_snapshot:
  available: on_user_request
  shows: "current decision tree with state markers + open boundaries"

Evidence Hierarchy

evidence_priority:
  1: production-grade open-source codebases with similar constraints
  2: official framework/language/database/cloud vendor docs
  3: research papers, RFCs, standards, formal design notes
  4: engineering blogs, conference talks, post-mortems from trusted teams
  5: community consensus signals (forums, GitHub issues, HN, Reddit)

calibration_rule: "state whether external example is truly comparable; never equate popularity with evidence"

Documentation Capture

capture_rules:
  when:
    - user explicitly requests
    - decision is crystallized with obvious persistence location
  convention: "follow project existing conventions; ask before creating new files"
  adr_creation:
    requires_all_three:
      - hard_to_reverse
      - surprising_without_context
      - real_tradeoff

References

references:
  - references/decision-log.md    # decision log format
  - references/pre-mortem.md      # pre-mortem method
  - references/downstream-skills.md  # downstream skill contracts (planned)
  - references/triggers.md        # trigger keywords per agent platform