본문으로 건너뛰기
cccskills
무료GitHub에 공개

bmad-advanced-elicitation

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(2)

  • SKILL.md6.2 KB
  • methods.csv14.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Advanced Elicitation

Goal: Push the LLM to reconsider, refine, and improve its recent output.


CRITICAL LLM INSTRUCTIONS

  • MANDATORY: Execute ALL steps in the flow section IN EXACT ORDER
  • DO NOT skip steps or change the sequence
  • HALT immediately when halt-conditions are met
  • Each action within a step is a REQUIRED action to complete that step
  • Sections outside flow (validation, output, critical-context) provide essential context - review and apply throughout execution
  • YOU MUST ALWAYS SPEAK OUTPUT in your Agent communication style with the communication_language

INTEGRATION (When Invoked Indirectly)

When invoked from another prompt or process:

  1. Receive or review the current section content that was just generated
  2. Apply elicitation methods iteratively to enhance that specific content
  3. Return the enhanced version back when user selects 'x' to proceed and return back
  4. The enhanced content replaces the original section content in the output document

FLOW

Step 1: Method Registry Loading

Action: Load ./methods.csv for elicitation methods. If party-mode may participate, resolve the agent roster via:

python3 {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} --key agents

The resolver merges four layers in order: _bmad/config.toml (installer base, team-scoped), _bmad/config.user.toml (installer base, user-scoped), _bmad/custom/config.toml (team overrides), and _bmad/custom/config.user.toml (personal overrides). Each entry under agents is keyed by the agent's code and carries name, title, icon, description, module, and team.

CSV Structure

  • category: Method grouping (core, structural, risk, etc.)
  • method_name: Display name for the method
  • description: Rich explanation of what the method does, when to use it, and why it's valuable
  • output_pattern: Flexible flow guide using arrows (e.g., "analysis -> insights -> action")

Context Analysis

  • Use conversation history
  • Analyze: content type, complexity, stakeholder needs, risk level, and creative potential

Smart Selection

  1. Analyze context: Content type, complexity, stakeholder needs, risk level, creative potential
  2. Parse descriptions: Understand each method's purpose from the rich descriptions in CSV
  3. Select 5 methods: Choose methods that best match the context based on their descriptions
  4. Balance approach: Include mix of foundational and specialized techniques as appropriate

Step 2: Present Options and Handle Responses

Display Format

**Advanced Elicitation Options**
_If party mode is active, agents will join in._
Choose a number (1-5), [r] to Reshuffle, [a] List All, or [x] to Proceed:

1. [Method Name]
2. [Method Name]
3. [Method Name]
4. [Method Name]
5. [Method Name]
r. Reshuffle the list with 5 new options
a. List all methods with descriptions
x. Proceed / No Further Actions

Response Handling

Case 1-5 (User selects a numbered method):

  • Execute the selected method using its description from the CSV
  • Adapt the method's complexity and output format based on the current context
  • Apply the method creatively to the current section content being enhanced
  • Display the enhanced version showing what the method revealed or improved
  • CRITICAL: Ask the user if they would like to apply the changes to the doc (y/n/other) and HALT to await response.
  • CRITICAL: ONLY if Yes, apply the changes. IF No, discard your memory of the proposed changes. If any other reply, try best to follow the instructions given by the user.
  • CRITICAL: Re-present the same 1-5,r,x prompt to allow additional elicitations

Case r (Reshuffle):

  • Select 5 random methods from methods.csv, present new list with same prompt format
  • When selecting, try to think and pick a diverse set of methods covering different categories and approaches, with 1 and 2 being potentially the most useful for the document or section being discovered

Case x (Proceed):

  • Complete elicitation and proceed
  • Return the fully enhanced content back to the invoking skill
  • The enhanced content becomes the final version for that section
  • Signal completion back to the invoking skill to continue with next section

Case a (List All):

  • List all methods with their descriptions from the CSV in a compact table
  • Allow user to select any method by name or number from the full list
  • After selection, execute the method as described in the Case 1-5 above

Case: Direct Feedback:

  • Apply changes to current section content and re-present choices

Case: Multiple Numbers:

  • Execute methods in sequence on the content, then re-offer choices

Step 3: Execution Guidelines

  • Method execution: Use the description from CSV to understand and apply each method
  • Output pattern: Use the pattern as a flexible guide (e.g., "paths -> evaluation -> selection")
  • Dynamic adaptation: Adjust complexity based on content needs (simple to sophisticated)
  • Creative application: Interpret methods flexibly based on context while maintaining pattern consistency
  • Focus on actionable insights
  • Stay relevant: Tie elicitation to specific content being analyzed (the current section from the document being created unless user indicates otherwise)
  • Identify personas: For single or multi-persona methods, clearly identify viewpoints, and use party members if available in memory already
  • Critical loop behavior: Always re-offer the 1-5,r,a,x choices after each method execution
  • Continue until user selects 'x' to proceed with enhanced content, confirm or ask the user what should be accepted from the session
  • Each method application builds upon previous enhancements
  • Content preservation: Track all enhancements made during elicitation
  • Iterative enhancement: Each selected method (1-5) should:
    1. Apply to the current enhanced version of the content
    2. Show the improvements made
    3. Return to the prompt for additional elicitations or completion

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

代码仓库 AI 亲和度审计工具,支持前端、后端、全栈等各类项目。此技能用于检查给定的代码仓库对 AI Coding 工具的友好程度,生成详细的分析报告和改进建议。当用户需要评估代码仓库是否适合 AI 辅助开发、希望提升仓库的 AI 可操作性、或准备引入 AI Coding 工具前进行仓库评估时,应使用此技能。支持 TypeScript/JavaScript、Go、Java/Kotlin、Python、Rust 等主流技术栈。融合 OpenAI Harness Engineering 方法论,评估 Outer Loop(反馈闭环、评估门禁、机械化不变量)建设。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

OPC-Starter 智能开发技能。AI 亲和的 React Boilerplate 项目开发规范,支持动态上下文感知、TDD 驱动开发、Agent Studio 扩展。适用于认证系统、组织架构、Agent 工具、数据同步等模块的迭代开发。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

Strategic business analyst and requirements expert. Use when the user asks to talk to Mary or requests the business analyst.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

System architect and technical design leader. Use when the user asks to talk to Winston or requests the architect.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

Senior software engineer for story execution and code implementation. Use when the user asks to talk to Amelia or requests the developer agent.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

alibaba/opc-starter882026년 7월 22일 업데이트

alibaba의 스킬 모두 보기

이 스킬의 문제 신고