magnus919_agent-skills/semantic-spacetime

Semantic Spacetime

Model meaning over time with Mark Burgess's Semantic Spacetime: a discrete graph method for designing shared semantic ground between agents, diagnosing semantic drift, and building coordination that converges on intended meaning.

Why Install This Skill

Multi-agent systems keep failing on meaning: two agents start from the same instructions and quietly diverge, nobody notices that a shared term no longer means the same thing to each side, and the system dead-ends in a state where information stops flowing. This skill gives your agent a working method for that problem — model the space of meaning as a graph, treat every local change as a unit of time, and measure where interpretations drift apart instead of guessing.

After installing, your agent can map a team of agents onto a semantic spacetime with typed events, things, and concepts, trace how intent propagates through promises and acceptances, diagnose drift and divergence with a bounded procedure, and write an analysis report with concrete interventions and a verification plan. The method is grounded in Burgess's arXiv series (2014-2025) and his earlier Promise Theory, and it is honest about what is verified, what is not, and what is extrapolation.

What You Get

Contents	Provides
SKILL.md	When to use Semantic Spacetime, when not to, and what to load for the task at hand
references/foundations.md	The academic core: definitions, the γ(3,4) formalism, proper time, causality, the promise substrate, and adjacent fields
references/applications-infrastructure.md	The CFEngine → IaC → Kubernetes/GitOps/IBN → MAPE-K lineage: convergence semantics, the promise-keeping-as-data gap, SLOs as semantic contracts, and the record-of-time machinery, with a citable lessons list
references/agent-coordination.md	Agentic AI: Burgess's agent papers, SSTorytime and MCP-SST, drift and temporal-blindness literature, spatial-temporal world models, the MCP/A2A substrate, and five labeled synthesis patterns
references/patterns.md	Ten named patterns (semantic anchor, trajectory, convergence loop, promise propagation, drift detection, absorbing states, shared manifold, γ(3,4) modeling, distance metrics, reconciliation), each with when-to-use and anti-patterns
references/diagnosis-and-debugging.md	A bounded procedure for diagnosing semantic drift, divergence, dead-ends, and meaning gaps — stop after three non-converging passes and report the evidence
references/glossary.md	Heading-led definitions of every term the skill uses
references/bibliography.md	Annotated primary sources with URLs, organized by area
templates/	The sst-model.yaml.tmpl model format (agents, nodes, edges, acceptances, trajectories, observations) and the sst-analysis.md.tmpl report skeleton
scripts/semantic-spacetime.py	A stdlib-only CLI: lint a model, map the γ(3,4) graph, measure semantic distance, trace trajectories, and diff snapshots for drift (--json and --dry-run supported)
tests/	A stdlib unittest suite (runs in CI) and trigger/anti-trigger routing probes, plus a fully-filled sample model fixture
evals/	Output-quality evals for the skill
LICENSE	MIT license

Quick Start

Nothing to install: the CLI is stdlib-only Python 3.10+. From the repository root, run:

1. Lint a model against the sst-model-v1 format — exit 0 prints a coverage summary, exit 1 prints named violations: python3 semantic-spacetime/scripts/semantic-spacetime.py model lint semantic-spacetime/tests/fixtures/sample-model.yaml
2. Map the γ(3,4) graph (text | mermaid | json): python3 semantic-spacetime/scripts/semantic-spacetime.py model map semantic-spacetime/tests/fixtures/sample-model.yaml --format mermaid
3. Measure semantic distance (weighted hop count, |link| + 1 per hop): python3 semantic-spacetime/scripts/semantic-spacetime.py model distance semantic-spacetime/tests/fixtures/sample-model.yaml --from report-event --to drift-concept
4. Trace trajectories (simple paths with link types; cycles noted): python3 semantic-spacetime/scripts/semantic-spacetime.py model trajectory semantic-spacetime/tests/fixtures/sample-model.yaml --from report-event --to drift-concept
5. Diff two snapshots — added/removed/changed regions; identical snapshots report no drift (run it on the same file twice to see the no-drift case): python3 semantic-spacetime/scripts/semantic-spacetime.py model drift semantic-spacetime/tests/fixtures/sample-model.yaml semantic-spacetime/tests/fixtures/sample-model.yaml
6. Append --json to any command for a single machine-readable object; --dry-run is a no-op guard.

To draft your own model, copy templates/sst-model.yaml.tmpl and fill it per the inline comments — the delimited example block shows a complete model. Copy templates/sst-analysis.md.tmpl for the analysis report skeleton: system description, the semantic spacetime map, drift/divergence/absorbing-state findings, interventions, and a verification/measurement plan.

Triggers

• Designing or analyzing shared semantic ground between agents
• Modeling intent or meaning changing over time (trajectories, drift, convergence)
• Designing convergent, self-healing coordination where state is measured against desired meaning
• Diagnosing semantic drift, divergence, or dead-ends (absorbing states)
• Mapping promises onto spacetime (trajectories, propagation, causality)
• Analyzing temporal blindness in agents (state tracking, event ordering, causality)

Requirements

Python 3.10+ (stdlib only) for the bundled CLI; nothing else to install. The skill content is Markdown, YAML templates, and JSON evals; the bundled model format is versioned (sst-model-v1) and documented in the template itself. Works with any agent client that loads Agent Skills.