third-brain-v7-skills

Third Brain V8.1 Skills

Verification-first Agent Skills for Codex OS, with optional compatibility adapters for Claude Code, Gemini, Cursor, and Windsurf. Build a persistent knowledge operating system with Obsidian provenance, scheduled loops, bounded static dependency graphs, context management, and multi-agent orchestration.

Install 21 reusable agent skills for ingesting sources, auditing workflows, compiling interlinked wikis, running daily knowledge loops, verifying claims before shipping, managing token costs, engineering bounded loops and static dependency graphs, 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

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Quick Start

1. Clone & Install

git clone https://github.com/Mark393295827/third-brain-v5-skills.git
cd third-brain-v5-skills
bash install.sh codex  # Codex is the primary host/kernel

Windows PowerShell uses .\install.ps1 codex. Compatibility adapters require an explicit non-Codex target.

2. Try a Skill

Paste this into Codex:

Use wiki-ingest on this source. Stage an immutable source, author a governed concept candidate, plan navigation updates, and return a verification summary. Treat entity authoring as a separate reviewed capability.

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

Command centre and distribution: V8.1 Codex Agentic OS command centre. Start it with python tools/agentic_os_server.py --state-root .agentic-os-state; the host is localhost-only by default and requires T02 runtime receipts. Claude/Gemini/Cursor/Windsurf remain compatibility adapters, not the primary runtime.

中文历史进阶手册：V7 最大潜力使用手册（参考）

🔥 V8.1 多智能体 Worker 流水线手册 & Worker Flows 规范：事务式流水线（Stage → Author → Graph Plan → Govern → Commit/Postcheck → optional Deliverable），归档只发生在验证提交之后。

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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

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V8.1 Operating Model

V8.1 keeps the wiki as durable disk and governance, gives every skill a profile-aware execution contract, and makes python -m tools.worker_flow.cli the only canonical transactional runtime:

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

The upgrade adds seven hard defaults:

• Profiles match risk: one-shot, stateful, loop, and high-risk work load only the controls they need.
• 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 the V8.1 Worker Flow, the Agent Skills standard, and the historical V7 release notes.

V8.1 keeps graph-engineering bounded to static DAGs; dynamic expansion and cyclic graphs remain out of scope.

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Core Skills (Start Here)

🧠 Knowledge & Verification

Skill	What it does
wiki-ingest	Preserve an immutable source, compile linked understanding, enforce promotion boundaries, and verify every touched file.
verify-before-claim	Iron rule: no completion claim without fresh, scope-matched evidence; consequential actions also require independent verification, approval, and rollback.

📅 Daily Loop

Skill	What it does
daily-okr	Execute a 7-KR cycle: Input → Cognition → Wiki → Behavior → Creativity → Output → Feedback, with an artifact or receipt for every completed KR.

🎯 Behavior & Creativity

Skill	What it does
behavior-design	Turn outcomes into observable minimum behavior, cues, SOPs, recovery, evidence, and review.
creativity-engine	Generate mechanism-diverse options, rank evidence gaps, and define three bounded 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	Derive budgets at runtime, checkpoint state, compact with KEEP/SUMMARIZE/DROP/RETRIEVE, and route by capability.
loop-engineering	Control temporal depth: turn repeatable work into a bounded Trigger -> Execute -> Verify -> State loop with durable state, finite budgets, and explicit stop/recovery rules.
graph-engineering	Control dependency width: validate bounded static DAGs with explicit dependencies, independent branches, typed joins, and node-local recovery.
agentic-engineering	Design agent workflows as spec-driven macro actions with quality ceilings, verification gates, delegated-action boundaries, and state checkpoints.
harness-engineering	Provide the runtime scheduler, permissions, leases, tools, provenance, and observability needed to execute agent workflows safely.
agent-teams-command	Admit and command multi-agent processes with exclusive ownership, typed IPC, isolated writes, serial integration, evidence, and cleanup.

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Complete Skill List

📥 Ingestion & Knowledge

• wiki-ingest — Stage immutable sources, author governed concept candidates, plan graph updates, and hand off optional entity authoring for separate review
• 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 evidence cycle with minimum-day degradation and durable closeout
• cognitive-compile — 8-section framework: Question → Facts → Concepts → Pattern Recognition → Conflict Detection → Hypothesis Generation

🎨 Behavior & Creativity

• behavior-design — Outcome → minimum behavior → cue → SOP → evidence → review
• creativity-engine — Mechanism-diverse combinations, scoring, and falsifiable minimum tests

🔬 Research & Quality

• deep-research — STOW-compatible research harness, evidence trails, source/claim ledgers
• verify-before-claim — Fresh scope-matched evidence, independent checks, approval, rollback

🔄 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

• context-manager — Runtime budgets, checkpoint replay, compaction, retrieval, capability routing

Utility command: commands/token-cost-tracker.md estimates, logs, and reports token usage; it is not one of the 21 Agent Skills.

🏗️ Engineering

• loop-engineering — Temporal depth through bounded loop contracts, independent verification, finite budgets, and stop/recovery rules
• graph-engineering — Dependency width through bounded static DAGs, explicit branches, typed joins, and node-local recovery
• agentic-engineering — Spec-driven macro actions, quality ceilings, verification gates, state checks
• harness-engineering — Runtime scheduler, permissions, system-call tools, delegated gates, provenance, observability
• agent-teams-command — Multi-agent process ownership, IPC, integration, async budgets, and evidence gates

💼 Strategy & Operations

• startup-evaluation — Startup health: entrepreneurship, VC 5T, PMF, runway, team, unit economics
• anthropic-os — Governed operating-system experiments: Four-C, 3B, prediction errors, permission ladder
• ai-six-sigma-property-os — AI + Ontology + DMAIC for property work orders, dispatch, quotes, evidence

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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)
                             ↓
             Loop depth / Graph dependency width
                             ↓
                Multi-agent teams (agent-teams-command)

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

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Architecture & Design

Third Brain V8.1 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