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Knowledge Consolidation

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huytieu
knowledge-consolidation

Build frameworks from scattered insights across all braindumps and notes

Overview

Publisherhuytieu
RepositoryCOG-second-brain
Skill nameknowledge-consolidation
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1.2K
Forks
138
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huytieu on GitHub. Read the source before you install it.

Installation

Install the Knowledge Consolidation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huytieu/COG-second-brain.git /tmp/COG-second-brain
mkdir -p .claude/skills
cp -r /tmp/COG-second-brain/skills/knowledge-consolidation .claude/skills/knowledge-consolidation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Knowledge Consolidation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Knowledge Consolidation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Knowledge Consolidation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

COG Knowledge Consolidation Skill

Purpose

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.

When to Invoke

  • User wants to consolidate their insights
  • User says "consolidate knowledge", "build frameworks", "synthesize insights"
  • Time for periodic knowledge base maintenance (weekly, monthly, quarterly)
  • User wants to extract patterns from accumulated braindumps
  • Before major decisions that could benefit from framework consultation

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If 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.
  • If agent_mode: solo (default) — handle all scanning, pattern recognition, and framework building directly. No delegation.

Pre-Flight Check

Get current timestamp (REQUIRED before generating any files):

  1. Run date '+%Y-%m-%d %H:%M' using Bash to get the actual current date and time
  2. Store this value and use it for the created: frontmatter field
  3. NEVER guess or fabricate the time — always use the value returned by the date command

Process Flow

1. Data Gathering

Scan 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:

  • Ask user: "What time period should I analyze? (last week, last month, last quarter, all time, or custom range?)"
  • Identify unprocessed content (check for status: "captured" or missing consolidation metadata)

Gather statistics:

  • Total documents to analyze
  • Breakdown by domain and type
  • Date range coverage

2. Pattern Recognition

Apply systematic pattern detection across all content:

Frequency Analysis

What comes up repeatedly?

  • Identify themes mentioned across multiple documents
  • Track topic frequency and clustering
  • Recognize persistent questions or concerns
  • Spot recurring action items or decisions
Temporal Clustering

What insights emerged together?

  • Group related insights by time period
  • Identify how thinking evolved over time
  • Recognize inflection points where thinking shifted
  • Map catalysts that triggered changes
Domain Correlation

What patterns cross domains?

  • Personal insights affecting professional thinking
  • Professional learnings applied to projects
  • Project experiences informing personal growth
  • Strategic themes spanning all domains
Contradiction Analysis

Where does thinking conflict?

  • Identify contradictory thoughts or approaches
  • Recognize evolution vs. inconsistency
  • Understand resolution or ongoing tension
  • Track perspective shifts over time
Cross-Cutting Patterns

Meta-patterns across all dimensions:

  • Decision-making approaches
  • Problem-solving strategies
  • Learning patterns
  • Emotional/energy patterns
  • Relationship patterns
  • Creative processes

3. Framework Development

Synthesize patterns into actionable frameworks:

Identify Core Principles

From scattered insights to fundamental truths:

  • What patterns reveal deeper principles?
  • What rules or heuristics emerge?
  • What mental models are forming?
  • What strategies are proving effective?
Test Against Evidence

Validate frameworks with source material:

  • Do source insights support these principles?
  • Are there counter-examples or exceptions?
  • How confident can we be in this framework?
  • What are the boundary conditions?
Define Boundaries

When does framework apply/not apply?

  • What contexts does this framework serve?
  • What are its limitations?
  • When should it NOT be used?
  • What assumptions does it rely on?
Create Applications

How to use this framework:

  • Specific use cases
  • Decision-making applications
  • Problem-solving templates
  • Practical implementation steps

4. Knowledge Integration

Update and create knowledge base documents:

Update Existing Frameworks

For each framework that needs updating, use the consolidated-knowledge template in references/templates.md.

Save to: 05-knowledge/consolidated/[framework-name]-framework.md

Create New Frameworks

For newly identified frameworks, use the new-framework template in references/templates.md.

Save to: 05-knowledge/consolidated/[framework-name]-framework.md

Update Pattern Documentation

Document the pattern using the pattern template in references/templates.md.

Save to: 05-knowledge/patterns/pattern-[name].md

Create Timeline Entries

Build the timeline entry using the thinking-evolution template in references/templates.md.

Save to: 05-knowledge/timeline/[topic]-evolution-YYYY-MM.md

5. Generate Consolidation Report

Create the master consolidation document using the report template in references/templates.md.

Save to: 05-knowledge/consolidated/consolidation-YYYY-MM-DD.md

6. Cleanup and Archival

Mark processed braindumps: Update frontmatter in processed braindumps:

yaml
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:

  • Remove redundancy while preserving important context
  • Update cross-references
  • Fix broken links
  • Ensure consistent tagging

7. Confirm Completion

After consolidation:

  • Show user: "Knowledge consolidation complete! Processed [X] documents"
  • Highlight: "[X] frameworks updated, [X] new frameworks created"
  • Show: "Consolidation report saved to [file path]"
  • Suggest reviewing key frameworks created/updated
  • Offer to explain any specific framework in detail

Loop Engineering

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):

  • Traceability: every framework principle links at least one source document. A principle with no [[source]] is dropped, not published. This is mechanical and is COG's verification-first rule for consolidation.
  • Coverage: every in-scope document ends marked status: "consolidated" with a consolidated_in backlink.
  • Dedup: before creating a framework, check 05-knowledge/consolidated/ so an existing framework is updated, not duplicated.
  • The completeness critic ("did we miss a theme?") is the one judgment-based check; keep it explicit and evidence-linked.

Termination conditions (layered):

  • Dry: K=2 consecutive passes find no new theme or document.
  • Coverage complete: all in-scope documents marked consolidated.
  • Hard cap: a max number of extraction passes, so a noisy corpus cannot loop forever.

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.

Consolidation Guidelines

Quality Over Quantity

  • Don't force insights that aren't mature enough
  • Let patterns emerge naturally from evidence
  • Be patient with incomplete thinking
  • Quality frameworks require time and evidence
  • Mark frameworks as "emerging" vs "working" vs "stable"

Preserve Nuance

  • Don't over-simplify complex insights
  • Maintain important context and conditions
  • Note when frameworks have limitations
  • Preserve contradictions that haven't resolved yet
  • Acknowledge uncertainty explicitly

Maintain Traceability

  • Always link back to source documents
  • Show evidence trail for frameworks
  • Document evolution of thinking
  • Enable future validation or revision
  • Make it easy to audit framework claims

Living Documents

  • Frameworks should evolve with new insights
  • Regular updates better than perfect first draft
  • Clear status indicators (emerging/working/stable)
  • Encourage iteration and refinement
  • Version history through Git

Analysis Techniques Reference

Pattern Detection Methods

  1. Frequency Analysis: Count mentions, cluster topics
  2. Temporal Clustering: Group by time, track evolution
  3. Domain Correlation: Cross-domain connections
  4. Contradiction Analysis: Identify conflicts, track resolution
  5. Energy Pattern Detection: Emotional and practical patterns

Framework Synthesis Process

  1. Identify Core Principles: Extract fundamental truths
  2. Test Against Evidence: Validate with sources
  3. Define Boundaries: Establish applicability
  4. Create Applications: Develop use cases
  5. Document Evolution: Track development over time

Timeline Construction Method

  1. Mark Inflection Points: When thinking shifted
  2. Identify Catalysts: What triggered changes
  3. Document Evolution: How understanding developed
  4. Extract Learnings: What evolution teaches

Success Metrics

  • Completeness: All relevant insights processed
  • Coherence: Frameworks logically consistent
  • Traceability: Clear links to source material
  • Actionability: Frameworks applicable to decisions
  • Evolution: Documented thinking progression
  • User Value: Frameworks actually used in practice

Common Use Cases

  • Weekly Consolidation: Process week's insights into patterns
  • Monthly Framework Development: Build strategic frameworks
  • Quarterly Strategic Synthesis: Big-picture consolidation
  • Annual Knowledge Base Cleanup: Maintain quality and relevance
  • Pre-Decision Framework Consultation: Apply frameworks to major decisions
  • Project Retrospective: Extract learnings for frameworks

Philosophy

The knowledge consolidation skill embodies COG's self-evolving intelligence:

  • Transforms scattered thoughts into strategic frameworks
  • Honors the evolution of thinking over time
  • Builds "single source of truth" living documents
  • Maintains traceability and evidence-based reasoning
  • Creates actionable knowledge for better decision-making
  • Respects nuance while seeking patterns
  • Values iteration and continuous refinement

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Knowledge Consolidation AI skill do?

Build frameworks from scattered insights across all braindumps and notes

Why use Knowledge Consolidation on TypingMind?

Because you install it once and use it with any model. Knowledge Consolidation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Knowledge Consolidation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huytieu/COG-second-brain/tree/main/skills/knowledge-consolidation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Knowledge Consolidation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Knowledge Consolidation?

As many as you like. As long as a model supports skills, you can use Knowledge Consolidation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Knowledge Consolidation AI skill free?

Yes. It is published on GitHub by huytieu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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