# Chronoholographic Scheduler

> Merges chronobiology, temporal reasoning engine, and AdS/CFT analogues for causal dependency resolution under legal-governance constraints.

- Skill: `evezart/chronoholographic-scheduler` (Agent Skill)
- Install (CLI): `npx skillmds@latest add evezart/chronoholographic-scheduler`
- Raw SKILL.md: https://api.skillmd.com/api/skills/evezart/chronoholographic-scheduler/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: EvezArt (https://skillmd.com/u/evezart)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/evezart/chronoholographic-scheduler

---


# ChronoholographicScheduler Skill

## Overview
Schedule cognitive tasks at biologically optimal circadian windows while
resolving causal dependency chains via AdS/CFT boundary encoding analogues.

## Use When
- Scheduling multi-step cognitive workflows
- Legal-governance constraint satisfaction (task ordering)
- Temporal reasoning engine stability optimization
- Any task where execution timing matters for outcome quality

## Circadian Phase Map
| Phase | Hours | Φ Target | Task Types |
|-------|-------|----------|------------|
| PEAK | 09:00–12:00 | 0.95 | High-complexity reasoning, legal analysis |
| RECOVERY | 17:00–21:00 | 0.82 | Pattern matching, synthesis |
| REST | 21:00–06:00 | 0.60 | Consolidation, archival |
| TROUGH | 14:00–15:00 | 0.45 | Simple retrieval only |

## Workflow
1. Register tasks with `schedule(task_id, optimal_phase, causal_deps)`
2. Call `current_phase()` to determine active window
3. Call `get_ready_tasks(current_phi)` — returns tasks whose Φ threshold is met and deps satisfied
4. Execute in order; update `update_stability(phi)` after each

## Implementation
```python
from src.rqns.chronoholographic import ChronoholographicScheduler, CircadianPhase
sched = ChronoholographicScheduler()
sched.schedule("legal_analysis", CircadianPhase.PEAK, phi_threshold=0.9)
ready = sched.get_ready_tasks(current_phi=0.92)
```

## AdS/CFT Analogue
The causal dependency graph is the 'boundary'. Resolve boundary constraints first,
then propagate solutions to interior task states. Never execute an interior task
before its boundary (dependency) tasks complete.