Gather phase context through adaptive questioning before planning. Use --all to skip area selection and discuss all gray areas interactively. Use --auto to skip interactive questions (Claude picks recommended defaults). Use --chain for interactive discuss followed by automatic plan+execute. Use --power for bulk question generation into a file-based UI (answer at your own pace).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
How it works:
Output: {phase_num}-CONTEXT.md — decisions clear enough that downstream agents can act without asking the user again
</objective>
<execution_context> @${CLAUDE_PLUGIN_ROOT}/workflows/discuss-phase.md @${CLAUDE_PLUGIN_ROOT}/workflows/discuss-phase-assumptions.md @${CLAUDE_PLUGIN_ROOT}/workflows/discuss-phase-power.md @${CLAUDE_PLUGIN_ROOT}/templates/context.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.
</runtime_note>
Context files are resolved in-workflow using init phase-op and roadmap/state tool calls.
</context>
If DISCUSS_MODE is "assumptions": Read and execute @${CLAUDE_PLUGIN_ROOT}/workflows/discuss-phase-assumptions.md end-to-end.
If DISCUSS_MODE is "discuss" (or unset, or any other value): Read and execute @${CLAUDE_PLUGIN_ROOT}/workflows/discuss-phase.md end-to-end.
MANDATORY: The execution_context files listed above ARE the instructions. Read the workflow file BEFORE taking any action. The objective and success_criteria sections in this command file are summaries — the workflow file contains the complete step-by-step process with all required behaviors, config checks, and interaction patterns. Do not improvise from the summary. </process>
<success_criteria>
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.