Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
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Process meeting recordings and notes into structured decisions, action items, and team dynamics with intelligent noise filtering
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:
agent_mode: team — use the full parallel agent execution strategy below (3 agents)agent_mode: solo — process sequentially: extract content first, then analyze dynamics, then enrich with context.Extract strategic insights from meeting recordings and notes with intelligent content filtering to focus on substantive, actionable content while removing noise and irrelevant information.
/meeting-transcriptUse parallel agents to process different aspects of the transcript simultaneously.
Launch ALL agents in a single message using Task tool with run_in_background: true:
Extract structured content from the meeting transcript.
1. Filter out side chats, technical difficulties, and irrelevant banter
2. Identify and extract: decisions made (with rationale), action items (with owners/deadlines), strategic themes
3. Capture key quotes and insights from participants
4. Note unresolved issues and follow-up needs
Return: Decisions list, action items list, strategic themes, key quotes, unresolved issues.
Analyze team dynamics and meeting effectiveness.
1. Assess communication patterns and participation levels
2. Identify leadership moments and collaboration quality
3. Evaluate decision-making process effectiveness
4. Assess meeting efficiency (time well spent vs wasted)
5. Note any tensions, disagreements, or alignment issues
Return: Team dynamics assessment, participation analysis, meeting effectiveness score.
Enrich meeting content with project/product context.
1. If project-related: read recent braindumps and notes for context
2. Check if discussed topics relate to known issues/PRs on GitHub
3. Connect decisions to existing strategic priorities
4. Identify competitive intelligence if competitors mentioned
Return: Contextual connections, related documents, strategic alignment notes.
---
type: meeting-transcript
domain: [personal|professional|project-specific]
project: [project-name] # Only for project-specific meetings
date: YYYY-MM-DD
created: YYYY-MM-DD HH:MM
meeting_type: [1-on-1|team-meeting|strategic-planning|project-review|other]
participants: [participant1, participant2, participant3]
duration: [minutes]
content_filtered: true
accuracy_verified: [true|false|pending]
action_items_count: [number]
decisions_count: [number]
tags:
- meeting
- transcript
- domain-tag
- project-tag
status: [processed|needs-follow-up|action-required]
---
[CUSTOMIZE: path/to/personal/]meetings/[CUSTOMIZE: path/to/professional/]meetings/[CUSTOMIZE: path/to/projects/][project-name]/meetings/Meeting notes processed by this command feed directly into the daily brief:
meeting-reviewer) scans for recent meeting filesMeeting Follow-Up Tracker) tracks commitment completionThis creates accountability: decisions and action items from meetings are automatically tracked against actual execution in code and project management tools.
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Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
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Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
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Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking for the closed loop, proper verification, or an evidence trail. Ordinary work does not run this.
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Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning
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Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing
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Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)
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