Claude Skills by Mark393295827

Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.

Use when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.

Use when a repeatable task must become a bounded Trigger -> Execute -> Verify -> State loop, scheduled automation, goal agent, or metric-driven research cycle.

Use when manual work, prior sessions, or an operator interview must be audited for repeatable skill, automation, or bounded-loop candidates.

Ender's Game approach to commanding Claude Code Agent Teams. Strategic multi-agent orchestration with Karpathy Agentic Engineering principles — plan, act, observe, iterate. L1-L5 commander progression.

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.

Cognitive Symbiont — Livewired self-evolving system. CASH + 3B creativity algorithms (Bending/Breaking/Blending). Predictive coding, collective intelligence, time-arrow diagnostics.

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.

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.

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.

Extract reusable knowledge from sessions. Scans for new concepts, entities, corrections, patterns, ideas, decisions, gaps — saves to wiki. Use at session end or "extract knowledge".

Evaluate startups using the 24-step disciplined entrepreneurship framework. Use when assessing a startup idea, conducting due diligence, or analyzing a business model.

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 sources (articles, PDFs, videos, notes) into a persistent interlinked knowledge wiki. Creates source notes, entity pages, concept pages, and updates navigation. Based on the STOW (Source → Think → Organize → Write) pattern.

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.