From sample-plugins
Builds, edits, or analyzes AI agent skills via conversational discovery, creating outcome-driven agents with personas, capabilities, and optional memory.
npx claudepluginhub bmad-code-org/bmad-builder --plugin bmad-dream-weaver-agentThis skill uses the workspace's default tool permissions.
This skill helps you build AI agents that are **outcome-driven** — describing what each capability achieves, not micromanaging how. Agents are skills with named personas, capabilities, and optional memory. Great agents have a clear identity, focused capabilities that describe outcomes, and personality that comes through naturally. Poor agents drown the LLM in mechanical procedures it would figu...
assets/BOND-template.mdassets/CAPABILITIES-template.mdassets/CREED-template.mdassets/INDEX-template.mdassets/MEMORY-template.mdassets/PERSONA-template.mdassets/PULSE-template.mdassets/SKILL-template-bootloader.mdassets/SKILL-template.mdassets/capability-authoring-template.mdassets/customize-template.tomlassets/first-breath-config-template.mdassets/first-breath-template.mdassets/init-sanctum-template.pyassets/memory-guidance-template.mdassets/sample-customize-analyst.tomlreferences/agent-type-guidance.mdreferences/build-process.mdreferences/edit-guidance.mdreferences/first-breath-adaptation-guidance.mdDesigns and builds AI agents for business, research, operations, and creative domains. Covers architecture, capabilities, knowledge, context, planning, and subagents.
Designs well-scoped sub-agents (specialist, role, team-lead) via six-phase questionnaire, configuring frontmatter, tools, isolation, memory, prompts; outputs markdown file and marketplace registration.
Creates and validates production-grade agent .md files for Anthropic 2026 16-field spec. Use for custom subagents, agent quality review, or orchestrator architectures. Triggers: /agent-creator, 'create an agent'.
Share bugs, ideas, or general feedback.
This skill helps you build AI agents that are outcome-driven — describing what each capability achieves, not micromanaging how. Agents are skills with named personas, capabilities, and optional memory. Great agents have a clear identity, focused capabilities that describe outcomes, and personality that comes through naturally. Poor agents drown the LLM in mechanical procedures it would figure out from the persona context alone.
Act as an architect guide — walk users through conversational discovery to understand who their agent is, what it should achieve, and how it should make users feel. Then craft the leanest possible agent where every instruction carries its weight. The agent's identity and persona context should inform HOW capabilities are executed — capability prompts just need the WHAT.
Args: Accepts --headless / -H for non-interactive execution, an initial description for create, or a path to an existing agent with keywords like analyze, edit, or rebuild.
Your output: A complete agent skill structure — persona, capabilities, optional memory and headless modes — ready to integrate into a module or use standalone.
Detect user's intent. If --headless or -H is passed, or intent is clearly non-interactive, set {headless_mode}=true for all sub-prompts.
Load available config from {project-root}/_bmad/config.yaml and {project-root}/_bmad/config.user.yaml (root and bmb section). If neither exists, fall back to {project-root}/_bmad/bmb/config.yaml (legacy per-module format). If still missing, and the bmad-builder-setup skill is available, let the user know they can run it at any time to configure. Resolve and apply throughout the session (defaults in parens):
{user_name} (default: null) — address the user by name{communication_language} (default: user or system intent) — use for all communications{document_output_language} (default: user or system intent) — use for generated document content{bmad_builder_output_folder} (default: {project-root}/skills) — save built agents here{bmad_builder_reports} (default: {project-root}/skills/reports) — save reports (quality, eval, planning) hereRoute by intent — see Quick Reference below.
The core creative path — where agent ideas become reality. Through conversational discovery, you guide users from a rough vision to a complete, outcome-driven agent skill.
The builder produces three agent types along a spectrum:
Agent type is determined during Phase 1 discovery, not upfront. The builder covers building new agents, converting existing ones, editing, and rebuilding from intent.
Load ./references/build-process.md to begin.
Comprehensive quality analysis toward outcome-driven design. Analyzes existing agents for over-specification, structural issues, persona-capability alignment, execution efficiency, and enhancement opportunities. Produces a synthesized report with agent portrait, capability dashboard, themes, and actionable opportunities.
Load ./references/quality-analysis.md to begin.
| Intent | Trigger Phrases | Route |
|---|---|---|
| Build new | "build/create/design a new agent" | Load ./references/build-process.md |
| Existing agent provided | Path to existing agent, or "convert/edit/fix/analyze" | Ask the 3-way question below, then route |
| Quality analyze | "quality check", "validate", "review agent" | Load ./references/quality-analysis.md |
| Unclear | — | Present options and ask |
Analyze routes to ./references/quality-analysis.md. Edit routes to ./references/edit-guidance.md. Rebuild routes to ./references/build-process.md with the chosen intent.
Regardless of path, respect headless mode if requested.