assess-covariance-method
Provides a protocol for implementing, validating, benchmarking, and statistically evaluating new covariance, correlation, precision estimators, and covariance scoring rules.
Investigación
semantic-spacetime
Enables agents to model and analyze Semantic Spacetime graphs, including semantic distance, trajectories, drift, causality, convergence, and absorbing states.
★ 88
GitHub Stars
91
Puntuación de calidad
18
Archivos
Sobre esta Skill
Model and analyze Semantic Spacetime (SST) graphs, distances, trajectories, drift, and model files. Do not use this skill for promise-theory vocabulary and fundamentals without SST modeling; use `promise-theory` for the substrate concepts.
Casos de uso
- Model shared meaning between agents
- Trace intent propagation and semantic trajectories
- Measure distance between interpretations
- Diagnose semantic drift and absorbing states
- Analyze event ordering without a global clock
Funciones
- Validates SST model files
- Renders γ(3,4) graphs as text, Mermaid, or JSON
- Calculates weighted semantic distance
- Enumerates graph trajectories
- Diffs snapshots to identify drift
Requisitos
- Python 3
Agents compatibles
Archivos de Skill
Other
evals/evals.json
JSON
Other
LICENSE
Other
README.md
Markdown
Other
templates/sst-analysis.md.tmpl
Other
templates/sst-model.yaml.tmpl
Other
tests/fixtures/invalid-model.yaml
YAML
Other
tests/fixtures/sample-model.yaml
YAML
Other
tests/test_semantic_spacetime.py
Python
Other
tests/trigger-probes.md
Markdown
Reference
references/agent-coordination.md
Markdown
Reference
references/applications-infrastructure.md
Markdown
Reference
references/bibliography.md
Markdown
Reference
references/diagnosis-and-debugging.md
Markdown
Reference
references/foundations.md
Markdown
Reference
references/glossary.md
Markdown
Reference
references/patterns.md
Markdown
Script
scripts/semantic-spacetime.py
Python
SKILL.md
SKILL.md
Markdown
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