context-graph
Context Graph
Coverage
The architectural model behind navigable context in an AI-coding workspace. Names the four interconnected graphs that any mature workspace accumulates — Skill Knowledge Graph, Document Routing Graph, Memory Index, Script / Command Registry — and the cross-graph edges that connect them (skill → script, skill → memory, doc-routing → doc, script → command). Specifies the three skill-graph edge types (adjacent, boundary, verify_with) and their per-edge-type caps. Defines orphan detection (a node with zero or near-zero incoming edges that agents cannot find by traversal) and the priority order for remediation (security skills first, then financial, integration, infrastructure, then UX). Specifies graph-connectivity metrics with healthy / unhealthy bands: connectivity, average degree, orphan rate, max degree, cluster count, hub-spoke ratio. Names the five deterministic signals that should drive graph synthesis (explicit prose references, manual relations frontmatter, bundle co-membership, shared routing labels, keyword overlap) — never an LLM at synthesis time. Walks the change-propagation checklist that traces a single edit across all four graphs. Catalogs the anti-patterns that quietly destroy graph quality: edge inflation, one-way edges, optional-metadata mindset, AI-inferred edges that drift on rebuild, ignoring cross-graph edges.
Philosophy
Without a navigable graph, agents cannot discover context they did not already know existed. The original failure mode looks like this: a skill exists, the agent doesn't reference it by name in the current prompt, and the routing layer has no edge to find it from — so the skill might as well not exist. A workspace can ship hundreds of skills and still operate as if it had ten, because the other 290 are unreachable from any traversal an agent actually performs.
Context discovery is therefore a precondition for context quality. If the right skill, doc, or memory file cannot be found by following edges from the current task, content quality is irrelevant. Graph maintenance — adding edges, fixing orphans, capping inflation, keeping cross-graph references current — is a quality gate, not optional metadata. Every new skill enters the system with a question attached: who reaches this from where, by which edges?
The deterministic-signal discipline is the second non-negotiable. Graph synthesis must be a deterministic function of the authored artifacts (frontmatter relations, bundle membership, prose references, shared routing labels, keyword overlap) — not an LLM inference. If the graph drifts on rebuild, agents lose the one stable surface they have. Use AI to suggest edges during authoring; never to generate the live graph at runtime.
1. The Four Context Graphs
A mature AI-coding workspace converges on four interconnected graphs:
Graph 1 — Skill Knowledge Graph
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