Plan an MVP-mode phase — captures an "As a / I want to / So that" user story, runs SPIDR splitting, then delegates to plan-phase
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Orchestrator role: Parse phase argument, validate phase, gather user story, run SPIDR check, persist mode + story to ROADMAP.md, then route to plan-phase. </objective>
<execution_context> @${CLAUDE_PLUGIN_ROOT}/workflows/mvp-phase.md @${CLAUDE_PLUGIN_ROOT}/references/user-story-template.md @${CLAUDE_PLUGIN_ROOT}/references/spidr-splitting.md @${CLAUDE_PLUGIN_ROOT}/references/planner-mvp-mode.md </execution_context>
<runtime_note>
Copilot (VS Code): Use vscode_askquestions wherever this workflow calls AskUserQuestion. They are equivalent — vscode_askquestions is the VS Code Copilot implementation of the same interactive question API.
TEXT_MODE fallback: Set TEXT_MODE=true if --text is present in $ARGUMENTS OR text_mode from init JSON is true. When TEXT_MODE is active, replace every AskUserQuestion call with a plain-text numbered list and ask the user to type their choice number.
</runtime_note>
Flags:
--force — Allow operating on in_progress / completed phases--text — Use plain-text numbered lists instead of TUI menus
</context>
<output_format>
When MVP planning concludes (user story written, ROADMAP.md updated, plan-phase delegated), emit a Next Up continuation block following the pattern in references/continuation-format.md:
## ✓ Phase N MVP-Planned — vertical slice captured)## ▶ Next Up heading with /gsd:execute-phase N (TDD execution)`/clear` then: before the command/clear is safe — /gsd:resume-work restores position from HANDOFF.json if you change your mind)MVP planning is a deliberate slice-of-the-cake exercise; clearing context before execution keeps TDD focused on the captured story rather than the planning conversation. </output_format>
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Handle ActiveCampaign contact and marketing tasks through Rube MCP, a connection for agent tools. Find or create contacts, manage tags and subscriptions, enroll contacts in existing automated workflows, and create follow-up tasks.
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Research competitors’ live Meta and Google ads to identify opening hooks, offers, calls to action, and landing pages. Receive a sourced creative brief and up to three test ideas adapted to your product.
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Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
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Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
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