Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
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Build frameworks from scattered insights across all braindumps and notes
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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。
Transform scattered insights from braindumps, daily briefs, and check-ins into coherent frameworks and "single source of truth" knowledge documents through pattern recognition and systematic synthesis.
Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:
agent_mode: team — delegate scanning and pattern extraction to parallel sub-agents (e.g., one per domain: personal braindumps, professional braindumps, project-specific content, daily briefs). Each agent identifies themes and patterns, then a synthesis agent combines findings into frameworks.agent_mode: solo (default) — handle all scanning, pattern recognition, and framework building directly. No delegation.Get current timestamp (REQUIRED before generating any files):
date '+%Y-%m-%d %H:%M' using Bash to get the actual current date and timecreated: frontmatter fielddate commandScan vault for unprocessed or partially processed content:
All braindumps since last consolidation:
02-personal/braindumps/03-professional/braindumps/04-projects/*/braindumps/00-inbox/braindump-*.md (mixed domain)Daily briefs and check-ins:
01-daily/briefs/01-daily/checkins/Any meeting transcripts or project documents in:
04-projects/*/planning/04-projects/*/resources/Determine scope:
status: "captured" or missing consolidation metadata)Gather statistics:
Apply systematic pattern detection across all content:
What comes up repeatedly?
What insights emerged together?
What patterns cross domains?
Where does thinking conflict?
Meta-patterns across all dimensions:
Synthesize patterns into actionable frameworks:
From scattered insights to fundamental truths:
Validate frameworks with source material:
When does framework apply/not apply?
How to use this framework:
Update and create knowledge base documents:
For each framework that needs updating, use the consolidated-knowledge template in references/templates.md.
Save to: 05-knowledge/consolidated/[framework-name]-framework.md
For newly identified frameworks, use the new-framework template in references/templates.md.
Save to: 05-knowledge/consolidated/[framework-name]-framework.md
Document the pattern using the pattern template in references/templates.md.
Save to: 05-knowledge/patterns/pattern-[name].md
Build the timeline entry using the thinking-evolution template in references/templates.md.
Save to: 05-knowledge/timeline/[topic]-evolution-YYYY-MM.md
Create the master consolidation document using the report template in references/templates.md.
Save to: 05-knowledge/consolidated/consolidation-YYYY-MM-DD.md
Mark processed braindumps: Update frontmatter in processed braindumps:
status: "consolidated"
consolidated_in: "[[consolidation-YYYY-MM-DD]]"
consolidated_date: "YYYY-MM-DD"
Archive outdated content:
Move superseded frameworks or insights to:
00-inbox/archive/[filename]-archived-YYYY-MM-DD.md
Add note explaining why archived and what supersedes it.
Maintain clean knowledge base:
After consolidation:
Consolidation is a loop-until-dry extraction with a completeness critic, not a single scan. See .claude/skills/loop-engineering/SKILL.md for the shared vocabulary.
The loop: scan a batch of in-scope documents → extract themes, patterns, and candidate framework principles → run the completeness critic ("any in-scope doc not yet read? any theme recurring across N+ docs that no framework captures yet?") → if the critic surfaces something new, run another extraction pass → stop when 2 passes in a row surface nothing new (dry). In agent_mode: team, the first scan fans out as one worker per domain (personal / professional / per-project / briefs); each returns its conclusions only, and a synthesis pass merges them.
The verifier (deterministic where it can be):
[[source]] is dropped, not published. This is mechanical and is COG's verification-first rule for consolidation.status: "consolidated" with a consolidated_in backlink.05-knowledge/consolidated/ so an existing framework is updated, not duplicated.Termination conditions (layered):
Patterns: loop-until-dry (the spine) + plan-execute-verify (each pass) + orchestrator-workers (team-mode domain scans) + completeness critic.
In-loop context: write the consolidation report incrementally and dedup new themes against the running set, not against the conversation. Externalizing to the report file is what keeps a large corpus from overflowing the window.
The knowledge consolidation skill embodies COG's self-evolving intelligence:
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Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
中文简介正在准备中。当前显示原文说明。
Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。
Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)
中文简介正在准备中。当前显示原文说明。