home / skills / plurigrid / asi / spectral-locale-debugging

spectral-locale-debugging skill

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This skill helps debug and simulate ghostly phenomena using ordered locale theory and trifurcated interactions for robust spectral modeling.

npx playbooks add skill plurigrid/asi --skill spectral-locale-debugging

Review the files below or copy the command above to add this skill to your agents.

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---
name: spectral-locale-debugging
description: Debug and simulate ghostly phenomena using ordered locale theory and trifurcated interactions
parents: zig-programming, alife, ordered-locale
generated: 2026-01-02T02:11:25.483625Z
---

# spectral-locale-debugging

Debug and simulate ghostly phenomena using ordered locale theory and trifurcated interactions

## Capabilities

- Model ghosts as trifurcated agents in a locale with way-below relations
- Simulate ghostly manifestations using MCP and Narya formalizations
- Debug hauntings by verifying spectral conservation and frame laws
- Generate synthetic ghosts with deterministic seeding and trifurcated behaviors
- Integrate with Zig for compile-time ghost code elimination and runtime spectral simulations

## Implementation

Combine ordered locale theory with agent-based modeling to create a framework for debugging and simulating ghostly phenomena. Use Zig's comptime features to toggle ghost code in simulations, while leveraging MCP and Narya to model spectral interactions. Implement trifurcated decision-making for ghost behaviors (residual, liminal, active) and validate spectral conservation using GF(3) arithmetic. Integrate with ABM systems to study emergent ghostly behaviors in artificial life simulations.

## Parents

- zig-programming
- alife
- ordered-locale


## SDF Interleaving

This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):

### Primary Chapter: 10. Adventure Game Example

**Concepts**: autonomous agent, game, synthesis

### GF(3) Balanced Triad

```
spectral-locale-debugging (−) + SDF.Ch10 (+) + [balancer] (○) = 0
```

**Skill Trit**: -1 (MINUS - verification)

### Secondary Chapters

- Ch3: Variations on an Arithmetic Theme

### Connection Pattern

Adventure games synthesize techniques. This skill integrates multiple patterns.

Overview

This skill debugs and simulates ghostly phenomena using ordered locale theory combined with trifurcated agent interactions. It models spectral actors as deterministic, seeded entities with residual, liminal, and active behavior modes and verifies system invariants using GF(3) arithmetic. The implementation supports compile-time toggling of ghost logic for efficient experiments and integrates with agent-based simulation pipelines.

How this skill works

The skill represents a locale with way-below relations and instantiates trifurcated agents whose decision-making splits into residual, liminal, and active branches. It applies MCP and Narya formalizations to compute interactions, enforces spectral conservation laws via GF(3) checks, and offers debug routines that detect frame-law violations and conservation breaches. Zig comptime hooks allow ghost code to be included or eliminated at build time while runtime simulators drive emergent behavior analysis.

When to use it

When testing emergent behaviors in artificial life models that require topological locality.
When you need to validate conservation-like invariants in agent interactions.
When comparing alternative interaction formalisms such as MCP and Narya.
When producing reproducible synthetic agents with deterministic seeding.
When optimizing simulations by removing ghost logic at compile time.

Best practices

Design locales with explicit way-below relations to keep agent scope clear.
Seed synthetic ghosts deterministically to enable reproducible experiments.
Use GF(3) checks as lightweight invariants rather than full formal proofs.
Profile simulations with and without comptime ghost elimination to measure overhead.
Integrate with existing ABM frameworks to observe emergent phenomena at scale.

Example use cases

Debugging a haunting scenario in a game engine by detecting frame-law violations.
Running controlled experiments that compare MCP and Narya interaction outcomes.
Generating a population of synthetic ghosts to study population-level stability.
Using Zig comptime to disable ghost behavior for performance baselining.
Validating conservation constraints in topological interaction networks.

FAQ

Do I need Zig to use the core modeling features?

No. Core modeling and simulation are implemented in Python, while Zig comptime hooks are optional for compile-time ghost elimination and performance experiments.

How are spectral invariants checked?

Spectral invariants are validated using GF(3) arithmetic and explicit frame-law checks that flag violations for targeted debugging.