bmad-help

name	bmad-help
description	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.

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