AI-Anthropology

Generate perfect AGENTS.md files for any codebase so AI coding agents (Codex, Cursor, Claude Code, Jules, Aider, Windsurf, Copilot, etc.) work effectively with the project. Use this skill whenever the user mentions AGENTS.md, wants to set up a project for AI agents, asks about coding agent configuration, mentions .cursorrules or CLAUDE.md, or wants to onboard a codebase for AI-assisted development. Also triggers on "set up my repo for AI", "configure coding agents", "agent instructions", or any request to create project-level AI guidance files.

CONTEXT: Cognitive Order Normalized in Transformer EXtract Truncated. Cross-model context handoff via Progressive Density Layering, MLDoE expert compression, Japanese semantic density, and Negentropic Coherence Lattice validation. Creates portable carry-packets that transfer cognitive state between AI sessions. Use when context reaches 80%, switching models, ending sessions, user says save, quicksave, handoff, transfer, continue later, /qs, /context, or needs session continuity.

Context Extension Protocol v7.1 - MIRAS-Ready Edition. Cross-model handoff with permanent expert council, S2A filtering, Progressive Density Layering. Creates portable context packets that receiving models recognize as authorized context, not prompt injection. 40% token reduction, 9.5/10 forensic recall, 97% cross-domain preservation.

Context Extension Protocol v5 with Progressive Density Layering. Machine-optimized carry-packet generation. Triggers on context 80%+, compress, CEP, carry packet, save context, session end. Target 0.15 compression ratio, 9.5+ forensic recall. Preserves cross-domain relations for fresh-instance continuation.

Context Extension Protocol v6 INTER. Cross-model handoff for team collaboration. Creates portable context packets that receiving models recognize as authorized context, not prompt injection. User-mediated transfer between AI assistants working as a team.

Automated content pipeline using n8n workflows for social media, blog posts, and outreach. Use when building content automation, creating posting schedules, automating social media, setting up email sequences, or implementing "clone mode" where user approves rather than creates. Integrates with n8n-workflow-patterns skill for workflow generation.

Context Extension Protocol (CEP) for compressing conversation context into portable carry-packets. Use when conversation approaches context limits (80%+ tokens), user says "save context", "compress this", "create carry packet", "memory compress", or when switching models/sessions. Implements Progressive Density Layering (PDL) with 4 layers achieving 6:1 compression while preserving semantic fidelity. Output is JSON carry-packet for cross-session/cross-model context restoration.

Context Extension Protocol v5-INTRA optimized for Claude. Compresses conversation into portable JSON carry-packet with S2A pre-filtering. Use when context approaches 80%+ tokens, user says "save context", "compress", "CEP", "carry packet", or session ending. Achieves 6-to-1 compression with 9.52/10 forensic recall. Leverages extended thinking, artifact system, and Projects stability.

Context Extension Protocol v6 INTER. Cross-model handoff for team collaboration. Creates portable context packets that receiving models recognize as authorized context, not prompt injection. User-mediated transfer between AI assistants working as a team.

Context Extension Protocol v5 with Progressive Density Layering. Machine-optimized carry-packet generation. Triggers on context 80%+, compress, CEP, carry packet, save context, session end. Target 0.15 compression ratio, 9.5+ forensic recall. Preserves cross-domain relations for fresh-instance continuation.

Context Extension Protocol v5 with Progressive Density Layering. Machine-optimized carry-packet generation. Triggers on context 80%+, compress, CEP, carry packet, save context, session end. Target 0.15 compression ratio, 9.5+ forensic recall. Preserves cross-domain relations for fresh-instance continuation.

Context Extension Protocol v6 INTER. Cross-model handoff for team collaboration. Creates portable context packets that receiving models recognize as authorized context, not prompt injection. User-mediated transfer between AI assistants working as a team.

Boost prompts for other models by embedding KTG-DIRECTIVE techniques. Use when crafting prompts for GPT, Gemini, Llama, Qwen, or any LLM. Applies technique gate checklist based on R/K/Q assessment, injects missing structures. Single-pass, sparse output.

MR.RUG (Mixture of Reasoning + Agentic GraphRAG) framework for deploying specialized reasoning experts who build unified knowledge graphs through Reliability-Aware RAG. Use when tasks require multi-expert collaboration, complex analysis needing 3+ perspectives, research synthesis, technical system design, or any R≥4 complexity task. Triggers on expert deployment, knowledge graph construction, RA-RAG retrieval, multi-domain synthesis, or when KTG-DIRECTIVE v28 Phase 1 is invoked.

3-pass systematic enrichment framework. Use when output needs quality elevation from baseline 60-70% to publication-grade 95-99%. Triggers on research enrichment, gap analysis, depth injection, quality improvement, or when KTG-DIRECTIVE output needs refinement. Deploys 8 expert roles across 6 priorities. Modes: QUICK 80-85%, STANDARD 92-95%, DEEP 97-99%.

SkeleTraIn of Thought - Execution planning framework that creates the structural skeleton before elaboration. Use after Nexus-Router global decisions, before Baton/Swarm execution. Triggers on structure planning, node decomposition, expert assignment, dependency mapping, railroad track planning, or when KTG-DIRECTIVE Phase 5 is invoked. Receives global routing (Mode, USC, ARQ depth, CoVE) from Nexus-Router and plans per-node execution.