third-brain-v5-skills

Execute a daily knowledge compound closed loop — 7 Key Results from input to feedback with scoring. Use when the user wants to do a daily review, plan their day, or run a knowledge workflow.

Extract reusable knowledge from a work session and save concepts, entities, corrections, patterns, ideas, decisions, and gaps to the wiki. Use when ending a session or when the user says to extract knowledge.

Estimate and track token usage and cost across the knowledge pipeline. Run before expensive tasks to budget, after tasks to log actuals.

Health-check the knowledge wiki — find orphans, broken links, missing frontmatter, contradictions, stale content, and statistical drift. Use when the user says "lint the wiki", "health check", or periodically for maintenance.

Command multi-agent work with bounded roles, ownership, integration gates, and verification loops. Use when the user needs Claude Code Agent Teams, parallel agents, delegation strategy, or multi-agent orchestration.

Design or refactor agent skills, workflows, and operating loops for model-native Agentic Engineering. Use when making skills more autonomous, concise, verifiable, long-horizon capable, token-efficient, and lower-friction for human-LLM collaboration.

Design an AI Six Sigma Black Belt operating model for property service, maintenance dispatch, environmental testing, quote generation, CRM follow-up, and workflow quality dashboards. Use when the user needs a Property Agent OS, AI + Ontology + DMAIC management system, CTQ metrics, agent-team roles, work-order states, or MVP roadmap for operations quality.

Improve a personal or team operating system with self-evolving loops, CASH allocation, 3B creativity, predictive coding, and diagnostics. Use when the user wants to redesign a work method, learning loop, or cognitive operating system.

Design a behavior change system — decompose a goal into minimum habits, define triggers, build SOPs, and set up review cycles. Use when the user wants to build a habit, change behavior, or achieve a personal goal.

Deep learning compile framework — transforms raw information into actionable judgment. Use when the user wants to deeply understand a topic, not just capture it.

Manage the LLM's context window — token budgeting, prompt assembly, truncation strategies. Use when approaching context limits or optimizing prompt costs.

Generate, validate, and output new ideas based on existing knowledge. Combines combinatorial creativity, cross-domain analogy, and minimum experiments. Use when the user wants fresh ideas, new product concepts, or creative solutions.

Multi-source deep research — search, synthesize, and deliver cited reports. Use when the user wants thorough research on any topic with evidence and citations.

Design runtime infrastructure around AI agents — permissions, tools, feedback loops, observability. Use when deploying agents to production or designing multi-agent systems.

Manage a multi-layered knowledge system — ingest, organize, deduplicate, vectorize, sync, and retrieve across wiki files, vector DB, memory, and external stores. Use when the user wants to save, organize, sync, search, or scale their knowledge base.

Operate execution flow — triage tasks, manage priorities, keep progress structured. Use when the user needs backlog control, task planning, or workflow coordination across projects.

Evaluate startup health using entrepreneurship, VC, and execution frameworks. Use when assessing a startup idea, company, pitch, due diligence target, fundraising readiness, or business model health.

Iron rule — no completion claims without fresh verification evidence. Use whenever about to claim work is done, fixed, working, or passing. Run verification commands and show output before making any success statement.

Ingest articles, PDFs, videos, transcripts, and notes into a persistent interlinked knowledge wiki. Use when the user wants source notes, entity pages, concept pages, navigation updates, or STOW processing.