dependency-architecture
Dependency Architecture
Coverage
Design and audit the dependency graph of a codebase. Covers direct vs transitive dependencies, runtime vs dev/build dependencies, package boundaries, import direction, adapter layers, duplicate-purpose libraries, lock-in, upgrade policy, supply-chain risk, and dependency drift.
Philosophy
Dependencies are architecture. Every package adds API surface, operational risk, update cost, and implicit design direction. A dependency that solves one task can still be wrong if it creates long-term coupling or duplicates an existing standard.
Prefer fewer, clearer dependencies with explicit ownership. Wrap volatile external SDKs at boundaries. Let application code depend on local contracts, not vendor shapes, when the vendor is likely to change or be replaced.
Method
More from jacob-balslev/skill-graph
a11y
Use when building or reviewing interactive UI, forms, navigation, or dynamic content. Covers semantic HTML, keyboard access, focus management, labeling, state-change announcement, and reduced-motion / high-contrast preferences. Do NOT use for color-palette creation, visual branding, feedback-state staging, or prose reading-level accessibility - those belong to `visual-design-foundations`, `interaction-feedback`, and documentation respectively.
7intent-recognition
Use BEFORE any tool call that could modify state, touch sensitive targets, rewrite history, install dependencies, publish packages, or expose credentials/environment data. Classifies intent into Passive/Read, Reconnaissance, Modification, or Destructive/Irreversible using operation type plus target sensitivity, then runs Identify / Confirm / Verify before action. Do NOT use for deciding what code to write, executing already-classified work, reactive post-execution guardrails, or defining upstream governance policy.
6information-architecture
Use when structuring information for findability: navigation, page hierarchy, docs architecture, sitemap shape, labeling systems, wayfinding, and content grouping. Do NOT use for formal category-governance work (use `taxonomy-design`), responsive page composition (use `layout-composition`), component/token architecture (use `design-system-architecture`), or sentence-level UI text (use `microcopy`).
6design-thinking
Use when orchestrating a full human-centered design process across discovery, definition, ideation, prototyping, and testing — when uncertain which stage of the arc a team is in, when deciding whether to loop back, or when routing to the right stage-specific sibling skill. Do NOT use for single-stage execution (go directly to problem-framing, user-research, research-synthesis, journey-mapping, ideation, prototyping, or usability-testing) or for engineering domain discovery (use event-storming).
6knowledge-modeling
Use when deciding *which representation paradigm* fits a piece of domain knowledge — knowledge graph vs frames vs production rules vs semantic network vs concept map vs procedural ontology vs hybrid — when designing AI-agent context systems, building a knowledge base, structuring a skill or reference library, or planning a GraphRAG retrieval pipeline. Covers the seven paradigms with structure / best-for / weakness tables, the tacit-to-explicit knowledge acquisition pipeline (elicitation → articulation → formalization → validation → encoding), knowledge graph design principles (reify when needed, separate schema from instance, label precisely, bidirectional naming, minimal redundancy), the four knowledge-validation types (completeness / consistency / relevance / currency) plus expert walkthrough, the seven-phase knowledge lifecycle (Create / Validate / Publish / Use / Monitor / Update / Retire), the application to AI-agent systems (skills as frames, routing as rules, memory as graph), and a full GraphRAG section covering the five patterns (entity-anchored retrieval, relationship-aware context, path-based reasoning, subgraph summarization, hybrid vector+graph) with rules for when graph-grounded retrieval beats plain RAG. Do NOT use for the *human-readable* domain analysis layer (use `conceptual-modeling`), for the database / ER design layer (a logical-modeling skill), for pure classification hierarchies (a taxonomy skill), for formal ontology axioms (an ontology skill), or for the live skill-library tooling that consumes modeled knowledge (use `skill-infrastructure`).
6ai-native-development
Use when reasoning about agent autonomy levels, designing auto-improve loops, evaluating AI-generated code quality, or measuring agent productivity in an LLM-assisted codebase. Covers Karpathy's three eras of software (1.0 explicit / 2.0 learned / 3.0 natural-language), the vibe-coding-vs-agentic-engineering distinction, the 0–5 autonomy slider with task-type recommendations, the one-asset / one-metric / one-time-box AutoResearch loop, Software 3.0 productivity metrics, and the documented quality regressions of ungated AI-generated code (the 'vibe hangover'). Do NOT use for choosing a specific autonomy-loop topology (use `agent-engineering`), for the per-prompt authoring discipline (use `prompt-craft`), or for reviewing the AI-generated code that comes out of a Software 3.0 workflow (use `code-review`).
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