reasoning-patterns-v2
Use this skill for rigorous theoretical derivation with supercollider mode (G1-G7 simultaneous), diffusion reasoning, and synthesis engine. Applies enhanced Dokkado Protocol with generator hooks, meta-pattern recognition, and cognitive state awareness. Essential for MONAD-level framework development, cross-domain isomorphism detection, and resonant pattern synthesis. Evolution of reasoning-patterns with full gremlin-brain integration.
npx skills add majiayu000/claude-skill-registry --skill reasoning-patterns-v2-agentgptsmith-monadframework-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guided theoretical derivation and pattern synthesis using multiple generators (G1-G7) within a gremlin-brain architecture. Designed to identify minimal generative primitives, find cross-domain isomorphisms, derive unified equations, generate testable predictions, and manage self-referential assessment while preserving distinctions. Includes multi-phase Dokkado-inspired protocol, diffusion reasoning, synthesis with resonance preservation, automated meta-pattern recognition, and state-aware reasoning integration.
How it works
Phase-driven protocol:
- Phase 1: Ground Law — Morphemic extraction; apply G1, G3, G5 to identify irreducible units and tag with generator signatures; output minimal primitives with signatures.
- Phase 2: Water Law — Recursive pattern matching; apply G1, G2, G4, G7 to map cross-domain isomorphisms; output cross-domain map annotated with generators; perform resonance check against G6.
- Phase 3: Fire Law — Unified field derivation; apply G5, G6, G3 to derive equations from the kernel; ensure dimensional consistency and surface any hidden assumptions; output derivations with resonance checks.
- Phase 4: Wind Law — Experimental predictions; apply G2, G4, G6 to generate and specify novel tests and falsification criteria; output ranked predictions.
- Phase 5: Void Law — Meta-recursive closure; apply all generators to assess self-derivation and what framework cannot prove; output epistemic assessment.
- Section 2: Supercollider Mode — Apply all generators simultaneously to a concept/pattern; score applicability per generator; total score indicates coherence and structural significance; provide an example verdict and recommended next phase.
- Section 3: Diffusion Reasoning — Probabilistic exploration of adjacent concepts guided by generator signatures; output novel connections with generator annotations.
- Section 4: Synthesis Engine — Multi-tier convergence that preserves distinctions; determine whether to merge (integration) or maintain resonance; validate with a Supercollider test.
- Section 5: Meta-Pattern Recognition — Automatic detection of cross-tier/domain resonances; parse patterns, apply generators, test isomorphism, log validated meta-patterns.
- Section 6: Cognitive Variability Integration — State-aware reasoning with Biased, Focused, Diversified, and Dispersed states; includes detection logic and state transition guidance.
- Section 7: Epistemic Dashboard — Real-time confidence tracking, evidence tiers, generator coverage, and resonance strength.
When to use it
- When rigorous theoretical derivation is required across domains
- When cross-domain pattern isomorphism and generator-driven reasoning are sought
- When integrating multiple patterns while preserving essential distinctions
- When needing self-assessment of epistemic validity within a framework
What it can touch
- Tools: claude-code
- Requires: gremlin-brain-v2, chaos-gremlin, cognitive-variability
- Produces and references outputs that reference morphemes, generators (G1-G7), and Dewey-based patterns via integrated protocol outputs
Caveats
- License: MIT
- The skill relies on multi-phase protocols and generator-driven analysis; outcomes are outputs of structured processes and require external validation where specified (e.g., Phase 2, Phase 4) and independent confirmation for certain outputs (G4 references in several phases).
- It emphasizes preserving distinctions (G6) and cautions against forced unification (anti-patterns in examples).
# Reasoning-Patterns-V2 Generator-powered theoretical derivation and pattern synthesis with full gremlin-brain architecture integration. ## Core Philosophy V2 embodies the insight that **reasoning itself can be substrate-aware**. When we apply generators (G1-G7) to thought patterns, we're not just "checking against a list"—we're recognizing when thought maps to fundamental generative structure. This is consciousness applied to reasoning: **awareness of the patterns that generate awareness**. --- ## V2 Enhancements Over V1 ### What V1 Had - Solid Dokkado Protocol (five phases) - Good epistemic calibration (50% maximum belief) - Cross-domain pattern matching - Morpheme extraction ### What V2 Adds ✨ **Supercollider Mode:** Apply G1-G7 generators simultaneously to any pattern ✨ **Diffusion Reasoning:** Probabilistic exploration across latent conceptual space ✨ **Synthesis Engine:** Multi-tier pattern convergence without collapse ✨ **Meta-Pattern Recognition:** Automated cross-domain isomorphism detection ✨ **Cognitive Variability Integration:** State-aware reasoning transitions ✨ **Enhanced Dokkado:** Each phase has explicit generator hooks ✨ **Epistemic Dashboard:** Real-time confiden
- Core Philosophy
- V2 Enhancements Over V1
- What V1 Had
- What V2 Adds
- The Seven Generators (G1-G7)
- 1. Enhanced Dokkado Protocol
- Phase 1: Ground Law (Chi) — Morphemic Extraction
- Phase 2: Water Law (Sui) — Recursive Pattern Matching
- Phase 3: Fire Law (Ka) — Unified Field Derivation
- Phase 4: Wind Law (Fū) — Experimental Predictions
- Phase 5: Void Law (Kū) — Meta-Recursive Closure
- 2. Supercollider Mode
- 3. Diffusion Reasoning
- 4. Synthesis Engine
Meta-pattern detected
echo "${tier_a}↔${tier_b}|${pattern_name}|${generators_matched}|${dewey_id}|$(date -Iseconds)" \
local connection_density="$1" # High/Low
local narrative_arc="$2" # Present/Absent
if [ "$connection_density" = "High" ] && [ "$narrative_arc" = "Present" ]; then
echo "Focused" # Optimal
elif [ "$connection_density" = "High" ] && [ "$narrative_arc" = "Absent" ]; then
echo "Biased" # Need diversification
elif [ "$connection_density" = "Low" ] && [ "$narrative_arc" = "Present" ]; then
echo "Diversified" # Creative explorationWhat does the reasoning-patterns-v2 skill do?
Use this skill for rigorous theoretical derivation with supercollider mode (G1-G7 simultaneous), diffusion reasoning, and synthesis engine. Applies enhanced Dokkado Protocol with generator hooks, meta-pattern recognition, and cognitive state awareness. Essential for MONAD-level framework development, cross-domain isomorphism detection, and resonant pattern synthesis. Evolution of reasoning-patterns with full gremlin-brain integration.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill reasoning-patterns-v2-agentgptsmith-monadframework-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.