
Autonomous legal reasoning engine for THEMANBEARPIG. Six-layer pipeline (intake→classify→analyze→reason→argue→validate) converts raw evidence into court-ready IRAC arguments with authority chains, impeachment ammo, counter-argument anticipation, and filing assembly. 20+ Michigan authority templates, argument chain builder, gap analysis, confidence scoring. The litigation autopilot.
GPU-accelerated graph rendering for THEMANBEARPIG. WebGPU compute shaders for force simulation, instanced SDF node rendering, LOD viewport culling, 100K+ node performance at 60fps. Falls back to Canvas2D. Integrates with D3 force layout.
Evidence density and semantic clustering for THEMANBEARPIG: heat density mapping, t-SNE/UMAP projection of evidence embeddings, gap detection visualization, evidence-to-filing coverage analysis. Transforms raw evidence into spatial intelligence patterns.
Visual effects for THEMANBEARPIG: GLSL shaders, particle systems, glow/aura rendering, CRT scanlines, glass morphism, fog of war. Covers WebGL shader pipeline, SVG filters, CSS backdrop effects, animated transitions, and post-processing compositing for the litigation mega-visualization.
14-engine bridge architecture for THEMANBEARPIG: pywebview expose, engine status dashboard, data flow visualization, MEEK lane overlays, FRED scoring, Delta999 agent status, DuckDB analytics, LanceDB semantic search, tantivy full-text, lazy initialization, error recovery, and engine configuration.
HUD gauges, EGCP quad-display, minimap with click-to-navigate, alert system, filing readiness bars, FPS counter, separation day counter, node/link stats, active filter indicator, connection status, threat level, quick-action buttons, and responsive layout for THEMANBEARPIG 13-layer mega-visualization.
Story mode, narrative generation, guided walkthroughs, jury presentation mode, breadcrumb trails, cinematic camera, chapter system, evidence spotlight, progressive reveal, presentation export, and voice-over hooks for THEMANBEARPIG 13-layer litigation intelligence mega-visualization. Pywebview desktop, D3.js force graph, PyInstaller bundled.
Audio sonification for THEMANBEARPIG: Web Audio API, threat→pitch mapping, ambient soundscapes, spatial audio for graph navigation, timeline playback audio, impeachment chord system, evidence density pulse. Transforms litigation data into auditory intelligence within the pywebview desktop app.
Transcendent litigation warfare system for LitigationOS. Use when: evidence hunting, adversary profiling, custody analysis, MCL 722.23 factors, impeachment prep, contradiction detection, deadline tracking, docket management, case operations, best interest analysis, parental alienation documentation, false allegation rebuttal, credibility assessment, witness preparation, deposition strategy.
Master manifest, activation matrix, quality gates, and orchestration protocol for all 24 THEMANBEARPIG visualization skills across 7 tiers. Coordinates skill selection, multi-skill activation, build pipeline, and deployment readiness.
MAX-TIER adversary-scoped skill: TOTAL DESTRUCTION of the Shady Oaks / Homes of America / Alden Global Capital / Partridge Securities / Cricklewood MHP housing cartel. Scope-locked to Lane B (2025-002760-CZ) and all associated housing case entities and individuals. When this skill is active: NO other adversary, case, lane, or claim exists. ONLY the housing cartel, ONLY its destruction. Trigger keywords: Shady Oaks, Homes of America, Alden, Partridge, Cricklewood, Kim Davis, Nicole Browley, Cassandra VanDam, Shelly Przybalek, Henry Brandell, Jeremy Brown, Aaron Cox, Joseph Khalil, eviction, water shutoff, dissolved LLC, ultra vires, title theft, EGLE, VN-017235, Lane B, 2025-002760-CZ.
Transcendent court filing and authority system. Use when: drafting motions, writing briefs, creating complaints, filing packages, MCR compliance, IRAC analysis, certificate of service, Bates stamping, authority chains, pin cites, MCR/MCL/MRE citation, appellate briefs, COA practice, MSC original actions, superintending control, mandamus, habeas corpus, 42 USC §1983, federal complaints, emergency applications, Typst PDF generation.
Transcendent data engineering and database mastery for LitigationOS. Use when: SQL queries, DuckDB analytics, LanceDB vectors, Polars DataFrames, FTS5 search, RAG pipelines, SQLite optimization, schema design, data migration, cross-database federation, vector embeddings, semantic search, indexing strategy, query optimization, connection pooling, WAL mode, PRAGMA tuning, batch operations.
Real-time court docket monitoring and deadline intelligence for LitigationOS. MiCOURT API, CourtListener webhooks, PACER integration, auto-deadline computation from Michigan Court Rules, push notifications, filing confirmation tracking, judge assignment monitoring.
Graph Neural Networks for legal reasoning in THEMANBEARPIG. Pure NumPy GAT layers, TransE link prediction, Louvain community detection, reasoning path scoring, evidence gap discovery. CPU-only, no PyG/DGL. Turns litigation_context.db into a reasoning engine that predicts hidden connections and identifies evidence gaps.
Tri-layer litigation memory architecture for THEMANBEARPIG: working memory (active investigation context with attention/decay), episodic memory (temporal event retrieval from timeline_events), semantic memory (legal knowledge from authority_chains_v2). Includes A-MEM Zettelkasten cross-reference discovery for auto-linking evidence atoms, Mem0-inspired consolidation/decay/retrieval orchestration, and D3.js memory visualization bridge. Cognitive science grounded: Atkinson-Shiffrin multi-store model, Ebbinghaus forgetting curves, capacity-limited attention. Persists across sessions via SQLite WAL + LanceDB vectors. Performance: working memory <5ms, episodic retrieval <50ms, semantic lookup <30ms, auto-linking <200ms.
Adversary network intelligence for THEMANBEARPIG: PageRank centrality, Louvain community detection, ego-network extraction, threat scoring, coordinated action detection. Transforms litigation adversaries into analyzable graph structures.
Authority hierarchy visualization for THEMANBEARPIG 13-layer graph. Citation PageRank from authority_chains_v2 (167K chains) and master_citations (72K). Chain completeness scoring (primary to supporting to pin cite). Hierarchical court-level rendering (SCOTUS, 6th Circuit, MSC, COA, Circuit, District). Shepardization status tracking (good_law, questioned, overruled). Authority coverage heatmap per filing lane. Missing authority gap detection with acquisition task generation. Interactive drill-down from citation node to all filings referencing it.
Impeachment combat layer for THEMANBEARPIG. Renders credibility gauges, contradiction spider charts, cross-exam COMMIT-PIN-CONFRONT-EXHIBIT sequences, MRE 613 prior-inconsistent-statement chains, impeachment heat-map overlay on the 13-layer graph, and interactive drill-down dossier panels. Data pipeline from impeachment_matrix (5.1K+ rows) and contradiction_map (2.5K+ rows). Exports impeachment packages as structured PDF outlines.
Judicial cartel intelligence overlay for THEMANBEARPIG: McNeill-Hoopes-Ladas triangle visualization, violation heatmaps, ex parte pattern detection, benchbook deviation tracking, JTC exhibit generation. Maps judicial misconduct as analyzable graph patterns.
Force simulation, custom litigation forces, collision detection, layout algorithms, and physics optimization for THEMANBEARPIG. Use when: tuning force parameters, creating custom forces (orbital, lane gravity, temporal, conspiracy), switching layouts (radial, hierarchical, timeline, swimlane), Barnes-Hut optimization, Web Worker simulation, constraint systems, node dragging, multi-layout transitions, physics presets, simulation lifecycle.
Tier-0 architectural DNA for THEMANBEARPIG 13-layer D3.js litigation intelligence mega-visualization. Node taxonomy (20+ types), link taxonomy (17 types), layer architecture (0-12), LAYER_META force config, graph construction from litigation_context.db, node sizing/coloring, performance constraints for 2500+ nodes with viewport culling and LOD rendering.
Filing pipeline F1-F10 Kanban visualization, EGCP scoring radar, deadline timeline with urgency alerts, packet assembly status, and filing readiness dashboard for THEMANBEARPIG 13-layer graph.
Interaction layer for THEMANBEARPIG 13-layer graph. Click/double-click/right-click handlers, Fuse.js fuzzy search across all layers, multi-select filter panel, keyboard shortcuts (1-6 rooms, Ctrl+F, Esc), PNG/SVG/JSON/CSV export, drag-drop node rearrangement, lasso selection, zoom/pan controls, context menus, tooltip system, selection state management, and WCAG accessibility.
Transcendent system design and engine architecture for LitigationOS. Use when: designing engines, Go concurrent systems, Rust CLI tools, performance optimization, clean code practices, SOLID principles, architecture decisions, system design patterns, engine fleet management, daemon architecture, connection pooling, thread safety, error recovery, circuit breakers, graceful degradation.
Master manifest for all 33 THEMANBEARPIG skills across 8 tiers. Activation matrix, cross-skill dependencies, quality gates, upgrade paths, build pipeline, tier architecture. The unified command center for the complete MBP skill taxonomy.
Self-evolving agent architecture for THEMANBEARPIG litigation graph. Act-Observe-Reflect-Plan loops, population-based strategy evolution, critic/verifier sub-agents, hybrid neural+symbolic legal reasoning, cross-session learning persistence, self-calibrating confidence, fleet orchestration. The graph that gets smarter every session.
Document AI and OCR pipeline for litigation evidence. PaddleOCR multimodal extraction, Surya layout-aware OCR, PP-StructureV3 table and form detection, court form recognition, handwriting OCR, multi-engine ensemble with confidence voting, legal entity extraction from scanned documents, Bates stamp detection, exhibit classification, Michigan court form template matching.
Weapon chain visualization for THEMANBEARPIG: 9 litigation weapon types, doctrine-to-remedy-to-filing chains, PPO weaponization tracking, false allegation mapping, contempt abuse patterns. Renders offensive and defensive legal arsenals as directed acyclic graphs.
Data pipeline for THEMANBEARPIG: SQLite→graph transforms, DuckDB analytical aggregations, LanceDB vector enrichment, Polars DataFrame ops, FTS5 search integration, incremental updates, Neo4j/GraphML/D3 export. Transforms 183+ DB tables (175K evidence, 167K authorities, 16K timeline, 5K impeachment) into 13-layer graph-ready data with quality gates, dedup, schema verification, and delta merge.
Brain network visualization for THEMANBEARPIG: 23+ brain DB topology, inter-brain data flows, health monitoring, learning loop spiral, knowledge density heatmap, version timeline.
Temporal control layer for THEMANBEARPIG 13-layer graph. Horizontal timeline scrubber (2023-2026), temporal node filtering, play/pause/speed playback, keyframe snapshots at critical dates, milestone markers, actor swimlanes, density heatmap strip, escalation sparkline, date-range brushing, fade animations, and dynamic separation counter integration.
3D graph visualization for THEMANBEARPIG: Three.js rendering, VR/WebXR support, t-SNE/UMAP projections, parallax depth, stereoscopic viewing, 3D force layout, camera flythrough, fog and atmosphere. Extends the 2D mega-visualization into immersive 3D within pywebview.
Transcendent agent architecture and fleet orchestration for LitigationOS. Use when: designing agents, multi-agent systems, fleet management, tool integration, agent evaluation, NEXUS daemon operations, extension development, parallel dispatch, agent lifecycle, error recovery, agent memory, planning strategies, CrewAI patterns, agent communication, genetic memory, MCP replacement architecture.
Transcendent judicial intelligence and misconduct documentation system. Use when: judge profiling, McNeill analysis, Hoopes analysis, misconduct patterns, bias indicators, ex parte violations, JTC complaints, judicial violation tracking, benchbook deviations, canon violations, ruling pattern analysis, cartel intelligence, Berry-McNeill connections, recusal grounds, DuckDB analytics on judicial data.