Third Brain V6 Skills

Production-ready Agent Skills for Claude Code, Codex, Gemini, Cursor, and Windsurf. Build a persistent knowledge operating system with verification-first workflows, Obsidian provenance, scheduled loops, context management, and multi-agent orchestration.

Install 19 reusable agent skills for ingesting sources, compiling interlinked wikis, running daily knowledge loops, verifying claims before shipping, managing token costs, engineering bounded agent loops, and orchestrating multi-agent teams.

Use this if:

  • Your AI agent keeps forgetting context between sessions
  • You want to enforce verification gates before "done" claims
  • You need to structure knowledge as linked pages (like Obsidian) instead of scattered chat history
  • You're building agent workflows that should learn and compound over time
  • You want Obsidian wiki knowledge to update skills, SOPs, schemas, and automation through supervised promotion gates

GitHub stars License: MIT Claude Code Cursor PRs Welcome


Quick Start

1. Clone & Install

git clone https://github.com/Mark393295827/third-brain-v5-skills.git
cd third-brain-v5-skills
bash install.sh claude  # or: codex, gemini, cursor, windsurf, all

2. Try a Skill

Paste this into Claude Code or your agent:

Use wiki-ingest on this source. Create source notes, concept pages, entity pages, navigation updates, and a verification summary.

Then paste a URL, article, PDF text, or any source you want to capture.

3. Next Steps

See the 3-Minute Quickstart for a complete walkthrough.

Full guide: GUIDE.md


The Problem

Scenario Before After
Research PDF Summarized once, then forgotten wiki-ingest: source notes, concept pages, linked wiki
Coding session Agent: "I fixed it" (no proof) verify-before-claim: requires test output + exit code before any claim
Daily work Tasks and ideas scatter across chat daily-okr: one insight, one wiki update, one action, one output, daily score

V6 Operating Model

V6 treats the wiki as the agent's durable disk and governance layer:

Input -> Source -> Wiki compile -> Daily loop -> Agent/Wiki flywheel -> Skill/SOP upgrade -> Verification

The upgrade adds six hard defaults:

  • Source provenance stays immutable: raw source notes and block refs remain the audit layer.
  • Loops have contracts: Trigger -> Execute -> Verify -> State, with budgets and recovery.
  • Context is zero-overhead by default: hot paths load only what the task needs.
  • Automation is bounded: scans and queues can run unattended; semantic writes stay supervised.
  • Teams need ownership: multi-agent work requires write scopes, IPC, join gates, cleanup, and evidence.
  • Rules promote through evidence: wiki insights become schema or skill rules only after repeated support and a cheap check.

See V6 release notes.


Core Skills (Start Here)

🧠 Knowledge & Verification

Skill What it does
wiki-ingest Capture sources (articles, PDFs, transcripts) into an interlinked wiki with source references, concept pages, and entity pages. Enforces the V6 source-to-skill promotion gate so pages explain why something matters and when it can change future agent behavior.
verify-before-claim Iron rule: No completion claims without fresh verification evidence. Run the proof command, show the output, then claim. Prevents "should work" hallucinations. Uses poker psychology (expected value thinking) to distinguish process from outcomes.

📅 Daily Loop

Skill What it does
daily-okr Execute a 7-KR cycle: Input → Cognition → Wiki → Behavior → Creativity → Output → Feedback. Includes Stop Doing List (Buffett/Munger) to identify what NOT to do. Score: 3=starting, 5=minimal loop, 7=quality, 10=flywheel.

🎯 Behavior & Creativity

Skill What it does
behavior-design Turn goals into habit systems. Decompose → minimum habits → triggers → SOPs → review. Includes Human Agency Scale (HAS) from BJ Fogg.
creativity-engine Generate novel ideas via combinatorial creativity. Lego Building Blocks method (Andrew Ng), cross-domain analogies, minimum experiments.

🔬 Research & Quality

Skill What it does
deep-research STOW-compatible research harness with evidence trails, source/claim ledgers, privacy checks, and direct handoff to wiki-ingest.
session-learn Extract knowledge patterns from sessions — concepts, entities, corrections, patterns, ideas, decisions, gaps. Closure protocol ensures learning feeds back into the wiki.

📊 Context & Engineering

Skill What it does
context-manager Manage LLM context window — token budgeting, prompt assembly, truncation strategies using Concrete Ideas framework (Andrew Ng) + Tokenmax techniques.
loop-engineering Turn repeatable tasks into bounded agent loops with a durable contract, independent verifier, hard budgets, explicit stop/recovery rules, and conservative topology selection.
agentic-engineering Design agent workflows as spec-driven macro actions with quality ceilings, verification gates, delegated-action boundaries, and state checkpoints.
agent-teams-command Orchestrate multi-agent fleets — ownership, IPC, async budget envelopes, integration joins, evidence gates.

Complete Skill List

📥 Ingestion & Knowledge

  • wiki-ingest — Ingest sources with source-risk taxonomy, Karpathy understanding gate, concept/entity pages, wikilinks
  • knowledge-ops — Multi-layer knowledge management: classify, deduplicate, preserve evidence hierarchy, vector + Markdown retrieval
  • wiki-lint — Health check: P0/P1 graph health, frontmatter, source refs, wikilink density, provenance debt

🔄 Daily Workflow

  • daily-okr — 7-KR closed loop with Stop Doing List (Buffett/Munger), daily score
  • cognitive-compile — 8-section framework: Question → Facts → Concepts → Pattern Recognition → Conflict Detection → Hypothesis Generation

🎨 Behavior & Creativity

  • behavior-design — Goals → habits → triggers → SOPs → review (HAS framework)
  • creativity-engine — Combinatorial creativity: Lego Building Blocks, cross-domain analogies, minimum tests

🔬 Research & Quality

  • deep-research — STOW-compatible research harness, evidence trails, source/claim ledgers
  • verify-before-claim — Verification gates, poker psychology (expected value), red-flag detection

🔄 Learning & Flow

  • session-learn — 7 signal types: concepts, entities, corrections, patterns, ideas, decisions, gaps
  • project-flow-ops — Execution flow: triage, plan, track, review across projects

📊 Context & Cost

  • context-manager — Token budgeting, prompt assembly, truncation strategies
  • token-cost-tracker — Estimate, log, report token usage with built-in Python logger

🏗️ Engineering

  • loop-engineering — Bounded loop contracts, independent verification, finite budgets, stop/recovery rules, and topology selection
  • agentic-engineering — Spec-driven macro actions, quality ceilings, verification gates, state checks
  • harness-engineering — Runtime infrastructure: permissions, system-call tools, delegated gates, provenance, observability
  • agent-teams-command — Multi-agent orchestration: ownership, IPC, async budgets, evidence gates

💼 Strategy & Operations

  • startup-evaluation — Startup health: entrepreneurship, VC 5T, PMF, runway, team, unit economics
  • anthropic-os — Self-evolving work method engine: CASH growth, 70/30 rule, two-week rule, working backwards
  • ai-six-sigma-property-os — AI + Ontology + DMAIC for property work orders, dispatch, quotes, evidence

How These Skills Fit Together

                        LLM (CPU) + Context (RAM) + Wiki (Disk)

External Sources ──→ wiki-ingest + knowledge-ops ──→ Knowledge Layers
                            ↓
                    Daily Loop (daily-okr)
                    /    /    \    \    \
               Input  Cognition  Wiki  Behavior  Creativity
                            |
                        Output → Feedback
                            ↓
                    session-learn (extract patterns)
                            ↓
                    verify-before-claim (quality gate)
                            ↓
                Multi-agent teams (agentic-engineering)

The system is a closed loop: ingest sources → process daily → extract learning → verify claims → promote rules → scale to teams.


Architecture & Design

Third Brain V6 treats agents as LLM OS processes:

  • LLM = CPU
  • Context = RAM
  • Wiki/Obsidian = Disk
  • Tools = System calls
  • Skills = Executable programs
  • Harness = Kernel
  • Agent teams = Processes

Design Layers

Layer Principle Skills
🧠 Knowledge OS Capture, structure, lint, and promote knowledge over time wiki-ingest, knowledge-ops, wiki-lint
⚡ Daily Loop Close the knowledge-to-action cycle every day daily-okr, cognitive-compile
🎯 Behavior & Creativity Turn knowledge into habits and novel ideas behavior-design, creativity-engine
🔬 Research & Quality Verify before claiming, research with rigor deep-research, verify-before-claim
🔄 Continuous Learning Extract patterns from every session session-learn, project-flow-ops
📊 Context & Cost Manage the LLM's scarcest resource context-manager, token-cost-tracker
🏗️ Engineering Design bounded loops, harnesses, agent workflows, and multi-agent systems loop-engineering, agentic-engineering, harness-engineering, agent-teams-command
💼 Strategy & Operations Evaluate startups, design AI quality systems startup-evaluation, anthropic-os, ai-six-sigma-property-os

Skill Adoption Path

Start small. Add skills as you need them.

Stage Core Skills Unlock When
Week 1 wiki-ingest + verify-before-claim You can ingest 1 source/day + every claim has fresh evidence
Weeks 2-4 + daily-okr + session-learn Daily OKR score >70% for a week, learnings feed back to wiki
Month 2+ + cognitive-compile + behavior-design + creativity-engine Wiki has 50+ pages or repeated decisions need synthesis
Month 3+ + knowledge-ops + loop-engineering + harness-engineering + agentic-engineering Retrieval, looping reliability, permissions, or delegated actions become bottlenecks
Multi-agent + agent-teams-command + project-flow-ops Work splits into separate owners with clear integration gates
Strategy + startup-evaluation + anthropic-os + deep-research Need startup health, market, operating-system decisions
Operations + ai-six-sigma-property-os Need measurable service quality, dispatch, evidence loops

Installation

For Claude Code

# Personal skills (available across all projects)
cp -r skills/* ~/.claude/skills/

# Project skills (shared with team)
cp -r skills/* .claude/skills/

For Cursor

mkdir -p .cursor/rules && cp adapters/cursor/third-brain-skills.mdc .cursor/rules/

For Windsurf

mkdir -p .windsurf/skills .windsurf/rules
cp -r skills/* .windsurf/skills/
cp adapters/windsurf/third-brain-skills.md .windsurf/rules/

For Codex CLI / Gemini CLI

cp -r skills/* ~/.agents/skills/  # Codex
cp -r skills/* ~/.gemini/skills/  # Gemini

Full guide: GUIDE.md


Example Workflows

Each workflow is copyable into your agent. See examples/ for complete, verified examples:


Wiki Structure

Skills default to STOW paths (configurable via system/config.md):

sources/          ← Immutable source notes (articles, PDFs, transcripts)
wiki/
├── concepts/     ← Ideas, frameworks, methods
├── entities/     ← People, companies, products
├── atomic-notes/ ← Single-fact notes
├── outputs/      ← Reusable reports, analyses
├── decisions/    ← Architecture decisions
└── sops/         ← Standard operating procedures
system/           ← Config, log, schema, templates
08_behaviors/     ← Behavior system (goals, habits, reviews)
09_creativity/    ← Creativity system (ideas, experiments)

Tools & References

Resource Purpose
tools/index.html Visual skill navigator and dashboard
tools/token-calculator.html Token cost calculator
GUIDE.md Full installation & troubleshooting
CLAUDE.md Claude Code setup
CONTRIBUTING.md How to contribute skills

What This Is NOT

  • Not a chat wrapper. Skills are executable prompts that agents follow; they're not productivity theater.
  • Not a productivity tool with 100 metrics. Daily OKR has 7 KRs; that's it. Scoring is 3/5/7/10, not a complex formula.
  • Not an all-in-one framework. Pick skills incrementally. You don't need all 18 to start.
  • Not prescriptive. Adapt paths, frontmatter, and skill triggers to your workflow.

Philosophy

Three core principles:

  1. Verification first: No "done" without proof. No claims without evidence. Expected value over confidence.
  2. Knowledge compounds: Every session should improve the wiki, not scatter across chat history.
  3. Closed loops: Ingest → Process → Learn → Scale → Verify. No loose ends.

Related Work


Contributing

Bug reports, skill improvements, and PRs are welcome. See CONTRIBUTING.md and CHANGELOG.md.


License

MIT — see LICENSE.


Transparency Note

This project includes growth and outreach tools (in tools/ and outreach/) designed to help the repository reach users via GitHub search and Awesome lists. These tools are optional, dry-run-by-default, and separate from the core skills. If you're installing skills for your own workflow, you don't need them.

The skill frameworks and philosophy are genuine. Use what works for you; ignore what doesn't.