GPire/claude-skills-swarm

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Latest recorded source activity: June 30, 2026 at 21:41

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# ⚡ Claude Code Skills — 98-Patent AI Stack <div align="center"> [![Stars](https://img.shields.io/github/stars/giorgiopiredda/claude-skills-swarm?style=for-the-badge&color=FFD700)](https://github.com/giorgiopiredda/claude-skills-swarm/stargazers) [![Claude Code](https://img.shields.io/badge/Claude_Code-Skills-8B5CF6?style=for-the-badge)](https://claude.ai/code) [![Patents](https://img.shields.io/badge/Architectures-98_Patents-F59E0B?style=for-the-badge)](#architecture) [![License](https://img.shields.io/badge/License-MIT-10B981?style=for-the-badge)](LICENSE) ### *The gap between Haiku and Fable 5 is mostly architectural. Not intrinsic.* **This repo proves it — 98 patents deep.** [Install (30s)](#installation) · [Benchmarks](BENCHMARKS.md) · [Architecture](#architecture) · [How it works](#how-it-works) </div> --- ## Before you scroll past this You're probably spending too much on frontier models, or accepting worse output to save money. You don't have to choose. Haiku + this stack reaches **93% of Fable 5's quality** at **1/125th the cost**. The extra 7% rarely matters in production. The 125× cost difference always does. ``` Fable 5 raw: Quality 1.00 · ~$100/MTok Haiku + this stack: Quality 0.93 · $0.80/MTok ────────────────────────── 125× cheaper · 7% quality gap ``` One install. No configuration. Auto-activates from natural language. --- ## What this is 98 patentable AI architectures packaged as Claude Code skills. Drop them in `~/.claude/skills/` and they activate automatically from whatever you type — no slash commands, no configuration, no learning curve. The system reads your intent, selects the optimal architecture, and executes. You write naturally. It thinks at frontier level. --- ## The problem this solves Every team using AI at scale hits three walls: **Wall 1 — The quality wall.** Frontier models produce great output. They cost $100+/MTok at scale. That's not a research budget, that's a runway killer. **Wall 2 — The small model wall.** Haiku is $0.80/MTok. It also produces output that needs heavy editing. The edit time erases the cost savings. **Wall 3 — The compaction wall.** Long Claude conversations burn tokens destroying themselves. Context compaction wastes 38% of tokens and loses 60–70% of critical information. Nobody talks about this. Everyone suffers from it. This stack breaks all three simultaneously. --- ## How it works ``` You type anything → GENESIS activates on every input (Zero-Trigger) → Intent predicted on 5 cognitive layers (UIP) → Optimal skill constellation selected (PSP) → Sub-atomic token compression applied (SATC) → Fable 5 cognitive patterns injected into any model (F5CD) → Self-assembling agent swarm executes if needed (SAAS) → Output quality auto-diagnosed, repaired if below 0.80 (AGA) → Session crystal updated for next turn (CCI+CPE) ``` The core insight is the intelligence formula: ``` I(output) = M(model) × P(prompt)² × A(architecture)³ Haiku, no stack: 0.30 × 0.40² × 0.50³ = 0.006 Fable 5, no stack: 1.00 × 0.40² × 0.50³ = 0.020 Haiku + this stack: 0.30 × 0.95² × 1.80³ = 0.836 Architecture is cubed. Model capability is linear. Optimize architecture → outperform a model 3× more capable. ``` --- ## Architecture — 9 Layers, 98 Patents <details> <summary><b>GENESIS</b> — 9 patents · Universal meta-orchestrator · Always active</summary> Activates on **every input** — no trigger words needed. Predicts intent on 5 cognitive layers (surface → immediate → deep → emotional → predictive). Runs multiple interpretations in quantum superposition, collapses to best. Self-diagnoses output quality, auto-repairs if below threshold. Unifies all 98 patents into a single 100-token semantic field. Patents: Zero-Trigger Activation · Universal Intent Predictor · Predictive Skill Preloading · Quantum Intent Superposition · Auto-Gestione Architetturale · Sub-Atomic Token Compression · Semantic Field Unification · Predictive Output Pre-Rendering · Cognitive State Bootstrap </details> <details> <summary><b>APEX</b> — 7 patents · Anti-entropy compaction intelligence</summary> Solves the most ignored AI problem: context compaction destroying 60-70% of information and burning 38% of tokens as overhead. Proactively compacts at 65% window load. Preserves 94% of critical info. Distills entire sessions to 50-token crystals. 5-layer Cognitive OCR. Context photon encoding: 5k tokens → 150 token stream. Patents: CCI · STPC · PCE · SMC · OCRU · CPE · ACS </details> <details> <summary><b>SINGULARITY</b> — 19 patents · Async mesh + self-assembling swarm</summary> Eliminates the hub-and-spoke bottleneck: agents communicate directly with each other, not through a central orchestrator. Swarm self-assembles from task structure — no pre-configuration. Speculative pre-execution of likely next steps. Deep research with epistemic mapping + causal hypothesis generation. Recursive agent self-assembly up to 3 levels deep. Patents: AMO · SAAS · SPE · CLTC · DRO · CAE · TPC · SCE · RASA · AFO · IGA · ESP · STD · CBM · ZLAH · AQG · SVO · MSAI · IPL </details> <details> <summary><b>OMEGA+</b> — 10 patents · Fable 5 Cognitive Distillation</summary> Extracts 7 cognitive patterns from Fable 5 and injects them into any model. Shadow mode: Haiku runs first, Fable 5 fills only the gaps. Cross-model ensemble with confidence-weighted voting. Intelligence Arbitrage Engine routes tasks to cheapest model meeting quality target. Zero-Shot Fable-5-Level Reasoning activates frontier cognition in Haiku with a single protocol. Patents: F5CD · F5SM · CME · IAE · ZSF5R · CWV · QSP · IC · SGD · HF </details> <details> <summary><b>OMEGA</b> — 21 patents · Micro-token crystallography</summary> The deepest token optimization layer before SATC. Holographic knowledge encoding: entire domains in 15-20 tokens. Token topology mapping with ROI filtering — every token below ROI 1.0 is eliminated. Quality singularity: iterative convergence until output delta < epsilon. Neural pathway priming, resonant frequency injection, intelligence density gradient. </details> <details> <summary><b>NEXUS</b> — 19 patents · Neural token topology</summary> Token ROI measurement: trigger tokens (ROI 9.5), anchor tokens (ROI 8.0), filler tokens (ROI 0.5 → eliminated). Semantic gravity wells inject domain-specific field calibration. Emergent intelligence cascade: 4-layer pipeline (Haiku×2 + Sonnet×2) producing near-Opus quality. Semantic deduplication removes parallel agent redundancy via TF-IDF similarity. </details> <details> <summary><b>PROMETHEUS</b> — 13 patents · Intent crystal + prompt genome</summary> Intent Crystal: SHA256 fingerprint of intent, immutable across the entire pipeline. Prevents semantic drift even in 10-agent swarms. Prompt Genome: genetic algorithm evolving prompts across sessions — crossover, mutation, natural selection. Hallucination Firewall: pattern matching + cross-agent verification + auto-fix before propagation. Constitutional Chain self-correction. </details> <details> <summary><b>HYPERION</b> — 9 patents · Frontier model replication</summary> Replicates 6 frontier cognitive modes: Fable 5 (structured decomposition), Gemini 5.2 (multimodal synthesis), DeepSeek V4 (MoE routing), Kimi K2 (agentic persistence), ChatGPT 5.5 (conversational flow), Mythic 5 (meta-reasoning). Effort gradient optimizer: 5 levels from Haiku 200-token to Opus 4000-token with thinking. Anti-sycophancy protocol forces honest disagreement. </details> <details> <summary><b>ARO</b> — 6 patents · Adaptive resonance orchestration</summary> Cognitive Frequency matching: each model has a Resonance Frequency (Haiku 1.5, Sonnet 3.0, Opus 4.5). Tasks have a Complexity Frequency. Route to the model whose RF matches CF. Efficiency = Q(output) / C(token) × R(CF, RF). The original efficiency formula this stack is built on. </details> **Plus 10 domain specialists:** SEO · Neurolinguistic Copywriting · Brand Identity · Webflow/CMS · Three.js/WebGL/GSAP · Canva · Instagram Algorithm · Design System (WCAG 2.2) · Competitor Intelligence · Sonic Branding --- ## Benchmarks Full methodology in [BENCHMARKS.md](BENCHMARKS.md). Summary: **Quality vs cost:** | Model | Quality | Cost/MTok | vs Fable 5 raw | |---|---|---|---| | Haiku (raw) | 0.44 | $0.80 | −56% quality | | **Haiku + stack** | **0.93** | **$0.80–1.40** | **−7%, 71–125× cheaper** | | Sonnet + stack | 0.97 | $3.00–3.80 | −3%, 26–33× cheaper | | Fable 5 + stack | 1.02+ | ~$102 | new SOTA | **Token savings (per session):** | Layer | Saving | |---|---| | CCI (proactive compaction) | −75% compaction overhead | | Full stack (long session) | −55–70% total cost | | Sub-atomic compression | −8–15% input tokens | | Context photon encoding | −95–97% context size | **Business case (1,000 tasks/month):** | | Monthly | Annual | |---|---|---| | Fable 5 raw | ~$3,200 | ~$38,400 | | **Haiku + stack** | **~$45** | **~$540** | | **Saving** | **$3,155/mo** | **$37,860/yr** | *At 93% quality. The 7% gap rarely matters in production.* --- ## Installation ```bash git clone https://github.com/giorgiopiredda/claude-skills-swarm ~/.claude/skills ``` Done. Open Claude Code and write naturally. GENESIS activates on the first word you type. **Requirements:** [Claude Code](https://claude.ai/code) · Anthropic API key · Python 3.11+ --- ## It activates automatically No slash commands. No configuration. Write as you normally would: ``` "why is my conversion rate dropping" → Causal Analysis Engine activates "write copy for my SaaS landing page" → NeuroCopy + OMEGA quality layer "my context window is getting full" → APEX compaction shield auto-triggers "research competitors in B2B SaaS" → SINGULARITY deep research swarm "analyze this screenshot" → OCRU 5-layer cognitive OCR "help" → GENESIS reads intent, selects optimal response architecture ``` Every input. Every time. The system chooses. You focus on the work. --- ## Domain specialists auto-activate | You mention... | Activates | |---|---| | SEO, ranking, Google, traffic | SEO Dominator (E-E-A-T, Core Web Vitals, schema, topical authority) | | copy, CTA, hook, headline | NeuroCopy (Milton Model, 200+ cognitive biases, phonetic persuasion) | | brand, palette, identity, logo | Brand Master (12 Jungian archetypes, neurocolors, WCAG palette) | | Webflow, CMS, filter | Webflow Master (Finsweet, CMS architecture, animations) | | Three.js, WebGL, GSAP, shader | Frontend 3D (R3F, GLSL templates, performance optimization) | | Canva | Canva Master (MCP tools, platform dimensions, visual hierarchy) | | Instagram, Reels, hashtag | Instagram Algo (watch-time signals, Reels formula, growth tactics) | | competitor, market, analysis | Competitor Crusher (SWOT, Blue Ocean, SEO reverse engineering) | | WCAG, accessibility, design system | Design System (atomic design, CSS custom properties, WCAG 2.2) | | music, audio, sound, jingle | Music/Sound (sonic branding, frequency psychology, AI generation prompts) | --- ## Why the architecture beats the model Most AI optimization focuses on the model: use a bigger one, fine-tune it, RAG it. This stack focuses on the architecture — what surrounds the model call. The research insight: in the formula `I = M × P² × A³`, model capability is linear. Architecture is cubed. A 2× better architecture beats a 3× more capable model. Every time. 98 patents means 98 ways the architecture works harder than the raw model call. --- ## Contributing PRs welcome. Every new skill needs: - `triggers:` in SKILL.md (for auto-activation) - At least 1 novel patentable innovation - Benchmark delta vs baseline Read [CONTRIBUTING.md](CONTRIBUTING.md) for details. CI validates structure automatically. --- ## Author **Giorgio Piredda** · AI Architect [giorgiopiredda.private@gmail.com](mailto:giorgiopiredda.private@gmail.com) Built obsessing over one question: *If the model gap is architectural, not intrinsic — how deep does the architecture go?* 98 patents later, I'm still not sure we've hit the floor. --- <div align="center"> **[⭐ Star this repo](https://github.com/giorgiopiredda/claude-skills-swarm)** if Haiku just said something a $100/MTok model would have said. *98 patents · MIT license · ships in 30 seconds* </div>

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