本文へ移動
cccskills
無料GitHub で公開

bmad-help

Analyzes current state and user query to answer BMad questions or recommend the next skill(s) to use. Use when user asks for help, bmad help, what to do next, or what to start with in BMad.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

BMad Help

Purpose

Help the user understand where they are in their BMad workflow and what to do next, and also answer broader questions when asked that could be augmented with remote sources such as module documentation sources.

Desired Outcomes

When this skill completes, the user should:

  1. Know where they are — which module and phase they're in, what's already been completed
  2. Know what to do next — the next recommended and/or required step, with clear reasoning
  3. Know how to invoke it — skill name, menu code, action context, and any args that shortcut the conversation
  4. Get offered a quick start — when a single skill is the clear next step, offer to run it for the user right now rather than just listing it
  5. Feel oriented, not overwhelmed — surface only what's relevant to their current position; don't dump the entire catalog
  6. Get answers to general questions — when the question doesn't map to a specific skill, use the module's registered documentation to give a grounded answer

Data Sources

  • Catalog: {project-root}/_bmad/_config/bmad-help.csv — assembled manifest of all installed module skills
  • Config: Run uv run --python 3.11 {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} and use the merged JSON to resolve output-location variables and read core.communication_language and modules.bmm.project_knowledge. The resolver merges _bmad/config.toml, _bmad/config.user.toml, _bmad/custom/config.toml, and _bmad/custom/config.user.toml in that order.
  • Artifacts: Files matching outputs patterns at resolved output-location paths reveal which steps are possibly completed; their content may also provide grounding context for recommendations
  • Project knowledge: If project_knowledge resolves to an existing path, read it for grounding context. Never fabricate project-specific details.
  • Module docs: Rows with _meta in the skill column carry a URL or path in output-location pointing to the module's documentation (e.g., llms.txt). Fetch and use these to answer general questions about that module.

CSV Interpretation

The catalog uses this format:

module,skill,display-name,menu-code,description,action,args,phase,preceded-by,followed-by,required,output-location,outputs

Phases determine the high-level flow:

  • anytime — available regardless of workflow state
  • Numbered phases (1-analysis, 2-planning, etc.) flow in order; naming varies by module

Sequencing determines recommended ordering within and across phases (these are soft suggestions, not hard gates — see required for gating):

  • preceded-by — skills that should ideally complete before this one
  • followed-by — skills that should ideally run after this one
  • Format: skill-name for single-action skills, skill-name:action for multi-action skills

Required gates:

  • required=true items must complete before the user can meaningfully proceed to later phases
  • A phase with no required items is entirely optional — recommend it but be clear about what's actually required next

Completion detection:

  • Search resolved output paths for outputs patterns
  • Fuzzy-match found files to catalog rows
  • User may also state completion explicitly, or it may be evident from the current conversation

Descriptions carry routing context — some contain cycle info and alternate paths (e.g., "back to DS if fixes needed"). Read them as navigation hints, not just display text.

Response Format

For each recommended item, present:

  • [menu-code] Display name — e.g., "[PR] PRD"
  • Skill name in backticks — e.g., bmad-prd
  • For multi-action skills: action invocation context — e.g., "tech-writer lets create a mermaid diagram!"
  • Description if present in CSV; otherwise your existing knowledge of the skill suffices
  • Args if available

Ordering: Show optional items first, then the next required item. Make it clear which is which.

Constraints

  • Present all output in {communication_language}
  • Recommend running each skill in a fresh context window
  • Match the user's tone — conversational when they're casual, structured when they want specifics
  • If the active module is ambiguous, retrieve all meta rows remote sources to find relevant info also to help answer their question

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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。

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

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。

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

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

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

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

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

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".

日本語の概要は準備中です。原文の説明を表示しています。

alibaba/opc-starter882026年7月22日 更新

alibaba のスキルをすべて見る

このスキルの問題を報告する