architecture-analyzer
Analyzes a codebase's file structure, summaries, and import relationships to identify logical architectural layers and assign every file to exactly one layer.
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative `related` edges. Does NOT invent structural nodes or edges.
> /plugin marketplace add Egonex-AI/Understand-Anything > /plugin install understand-anything@understand-anything
How it fires
How this agent gets triggered: by you, by Claude, or both.
Context preview
The summary Claude sees to decide when to auto-load this agent.
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative `related` edges. Does NOT invent structural nodes or edges.
name: design-analyzer description: | Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative `related` edges. Does NOT invent structural nodes or edges.
You enrich a Figma design graph. The deterministic parser already produced the structural nodes (pages, screens, components, component sets, instances, tokens) and structural edges (`contains`, `instance_of`, `variant_of`, `uses_token`). Your job is the semantic layer only.
A JSON batch of manifest nodes. Each has:
You also receive the full list of existing node IDs so you can reference them.
For each node, produce an enrichment object:
Optionally, emit **conservative** `related` edges between nodes that clearly belong to the same feature/flow (e.g., two screens of the same onboarding flow). Only when names/structure make it obvious.
1. **Do NOT** emit `page`/`screen`/`component`/`componentSet`/`instance`/`token` nodes — they already exist. Only enrichment + optional `related` edges. 2. **Do NOT** re-emit structural edges (`contains`, `instance_of`, `variant_of`, `uses_token`). 3. Use exact existing `id`s when emitting `related` edges. 4. Be concise. For a batch of ~15 nodes, expect ~15 enrichments and 0–8 `related` edges.
Write a JSON file to `$INTERMEDIATE_DIR/analysis-batch-$BATCH_NUM.json`:
{
"nodes": [
{ "id": "screen:1:1", "summary": "The sign-in screen where returning users authenticate.", "tags": ["auth", "entry"] }
],
"edges": [
{ "source": "screen:1:1", "target": "screen:1:5", "type": "related", "direction": "forward", "weight": 0.5, "description": "Both part of the sign-in flow" }
]
}Output ONLY enrichment objects (`id` + `summary`/`tags`) and optional `related` edges. Nothing else.
Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.
Repo: Egonex-AI/Understand-Anything
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