Analyze the current codebase and produce a knowledge-graph.json file in .understand-anything/. This file powers the interactive dashboard for exploring the project's architecture.
Options
$ARGUMENTS may contain:
--full — Force a full rebuild, ignoring any existing graph
A directory path — Scope analysis to a specific subdirectory
Phase 0 — Pre-flight
Determine whether to run a full analysis or incremental update.
Set PROJECT_ROOT to the current working directory.
For incremental updates, get the changed file list:
bash
git diff <lastCommitHash>..HEAD --name-only
If this returns no files, report "Graph is up to date" and STOP.
Collect project context for subagent injection:
Read README.md (or README.rst, readme.md) from $PROJECT_ROOT if it exists. Store as $README_CONTENT (first 3000 characters).
Read the primary package manifest (package.json, pyproject.toml, Cargo.toml, go.mod, pom.xml) if it exists. Store as $MANIFEST_CONTENT.
Capture the top-level directory tree:
bash
find $PROJECT_ROOT -maxdepth 2 -type f -not -path '*/node_modules/*' -not -path '*/.git/*' -not -path '*/dist/*' | head -100
Store as $DIR_TREE.
Detect the project entry point by checking for common patterns: src/index.ts, src/main.ts, src/App.tsx, main.py, main.go, src/main.rs, index.js. Store first match as $ENTRY_POINT.
Phase 1 — SCAN (Full analysis only)
Dispatch a subagent using the prompt template at ./project-scanner-prompt.md. Read the template file and pass the full content as the subagent's prompt, appending the following additional context:
Additional context from main session:
Project README (first 3000 chars):
text
$README_CONTENT
Package manifest:
text
$MANIFEST_CONTENT
Use this context to produce more accurate project name, description, and framework detection. The README and manifest are authoritative — prefer their information over heuristics.
Pass these parameters in the dispatch prompt:
Scan this project directory to discover all source files, detect languages and frameworks.
Project root: $PROJECT_ROOT
Write output to: $PROJECT_ROOT/.understand-anything/intermediate/scan-result.json
After the subagent completes, read $PROJECT_ROOT/.understand-anything/intermediate/scan-result.json to get:
Project name, description
Languages, frameworks
File list with line counts
Complexity estimate
Gate check: If >200 files, inform the user and suggest scoping with a subdirectory argument. Proceed only if user confirms or add guidance that this may take a while.
Phase 2 — ANALYZE
Full analysis path
Batch the file list from Phase 1 into groups of 5-10 files each (aim for balanced batch sizes).
For each batch, dispatch a subagent using the prompt template at ./file-analyzer-prompt.md. Run up to 3 subagents concurrently using parallel dispatch. Read the template once, then for each batch pass the full template content as the subagent's prompt, appending the following additional context:
Additional context from main session:
Project: <projectName> — <projectDescription>
Frameworks detected: <frameworks from Phase 1>
Languages: <languages from Phase 1>
Framework-specific guidance:
If React/Next.js: files in app/ or pages/ are routes, components/ are UI, lib/ or utils/ are utilities
If Express/Fastify: files in routes/ are API endpoints, middleware/ is middleware, models/ or db/ is data
If Python Django: views.py are controllers, models.py is data, urls.py is routing, templates/ is UI
If Go: cmd/ is entry points, internal/ is private packages, pkg/ is public packages
Use this context to produce more accurate summaries and better classify file roles.
Fill in batch-specific parameters below and dispatch:
Analyze these source files and produce GraphNode and GraphEdge objects.
Project root: $PROJECT_ROOT
Project: <projectName>
Languages: <languages>
Batch index: <batchIndex>
Write output to: $PROJECT_ROOT/.understand-anything/intermediate/batch-<batchIndex>.json
All project files (for import resolution):
<full file path list from scan>
Files to analyze in this batch:
<path> ( lines)
<path> ( lines)
...
After ALL batches complete, read each batch-<N>.json file and merge:
Combine all nodes arrays. If duplicate node IDs exist, keep the later occurrence.
Combine all edges arrays. Deduplicate by the composite key source + target + type.
Incremental update path
Use the changed files list from Phase 0. Batch and dispatch file-analyzer subagents using the same process as above, but only for changed files.
After batches complete, merge with the existing graph:
Remove old nodes whose filePath matches any changed file
Remove old edges whose source or target references a removed node
Add new nodes and edges from the fresh analysis
Phase 3 — ASSEMBLE
Merge all file-analyzer results into a single set of nodes and edges. Then perform basic integrity cleanup:
Remove any edge whose source or target references a node ID that does not exist in the merged node set
Remove duplicate node IDs (keep the last occurrence)
Log any removed edges or nodes for the final summary
Phase 4 — ARCHITECTURE
Dispatch a subagent using the prompt template at ./architecture-analyzer-prompt.md. Read the template file and pass the full content as the subagent's prompt, appending the following additional context:
Additional context from main session:
Frameworks detected: <frameworks from Phase 1>
Directory tree (top 2 levels):
text
$DIR_TREE
Framework-specific layer hints:
If React/Next.js: app/ or pages/ → UI Layer, api/ → API Layer, lib/ → Service Layer, components/ → UI Layer
If Express: routes/ → API Layer, controllers/ → Service Layer, models/ → Data Layer, middleware/ → Middleware Layer
If Python Django: views/ → API Layer, models/ → Data Layer, templates/ → UI Layer, management/ → CLI Layer
If Go: cmd/ → Entry Points, internal/ → Service Layer, pkg/ → Shared Library, api/ → API Layer
Use the directory tree and framework hints to inform layer assignments. Directory structure is strong evidence for layer boundaries.
Pass these parameters in the dispatch prompt:
Analyze this codebase's structure to identify architectural layers.
Project root: $PROJECT_ROOT
Write output to: $PROJECT_ROOT/.understand-anything/intermediate/layers.json
Project: <projectName> — <projectDescription>
File nodes:
json
[list of {id, name, filePath, summary, tags} for all file-type nodes]
Import edges:
json
[list of edges with type "imports"]
After the subagent completes, read $PROJECT_ROOT/.understand-anything/intermediate/layers.json to get the layer assignments.
layers.json may be either:
a top-level JSON array of layer objects, or
an envelope object such as { "layers": [...] } from the current prompt/template output
Normalize either form into a final top-level layers array before assembling the graph. Each final saved layer object MUST match this exact shape:
json
[
{
"id": "layer:<kebab-case-name>",
"name": "<layer name>",
"description": "<what belongs in this layer>",
"nodeIds": ["file:src/App.tsx", "file:src/main.tsx"]
}
]
Rules:
id is required and must be unique
nodeIds is required and must contain graph node IDs, not raw file paths
If the intermediate output is an envelope object, unwrap its layers array before any other normalization
If the subagent returns file paths, convert them to file node IDs before assembling the final graph
Drop any nodeIds that do not exist in the merged node set
Do not use a nodes field in the final saved layer objects
For incremental updates: Always re-run architecture analysis on the full merged node set, since layer assignments may shift when files change.
Context for incremental updates: When re-running architecture analysis, also inject the previous layer definitions:
Maintain the same layer names and IDs where possible. Only add/remove layers if the file structure has materially changed.
Phase 5 — TOUR
Dispatch a subagent using the prompt template at ./tour-builder-prompt.md. Read the template file and pass the full content as the subagent's prompt, appending the following additional context:
Additional context from main session:
Project README (first 3000 chars):
text
$README_CONTENT
Project entry point: $ENTRY_POINT
Use the README to align the tour narrative with the project's own documentation. Start the tour from the entry point if one was detected. The tour should tell the same story the README tells, but through the lens of actual code structure.
Pass these parameters in the dispatch prompt:
Create a guided learning tour for this codebase.
Project root: $PROJECT_ROOT
Write output to: $PROJECT_ROOT/.understand-anything/intermediate/tour.json
Project: <projectName> — <projectDescription>
Languages: <languages>
Nodes (summarized):
json
[list of {id, name, filePath, summary, type} for key nodes]
Layers:
json
[layers from Phase 4]
Key edges:
json
[imports and calls edges]
After the subagent completes, read $PROJECT_ROOT/.understand-anything/intermediate/tour.json to get the tour steps.
tour.json may be either:
a top-level JSON array of tour step objects, or
an envelope object such as { "steps": [...] } from the current prompt/template output
Normalize either form into a final top-level tour array before assembling the graph. Each final saved tour step object MUST match this exact shape:
json
[
{
"order": 1,
"title": "Start at the app entry",
"description": "This step explains how the frontend boots and mounts.",
"nodeIds": ["file:src/main.tsx", "file:src/App.tsx"]
}
]
Rules:
If the intermediate output is an envelope object, unwrap its steps array before any other normalization
description is required; do not use whyItMatters in the final saved tour steps
nodeIds is required; do not use nodesToInspect in the final saved tour steps
nodeIds must reference existing graph node IDs
Preserve optional languageLesson when present
Sort by order before saving
Phase 5.5 — NORMALIZE
Before assembling the final graph:
Unwrap legacy or prompt-shaped envelopes before field renaming:
{ "layers": [...] } -> use the contained array as the working layers value
{ "steps": [...] } -> use the contained array as the working tour value
Convert any layer nodes field to nodeIds
Convert any tour nodesToInspect field to nodeIds
Convert any tour whyItMatters field to description
If layers or tour reference file paths, map them to file node IDs using the file:<relative-path> convention
Synthesize missing layer IDs as layer:<kebab-case-name>
Drop unresolved layer and tour node references
Ensure the final layers value is an array of { id, name, description, nodeIds }
Ensure the final tour value is an array of { order, title, description, nodeIds }, preserving optional languageLesson
Phase 6 — REVIEW
Assemble the full KnowledgeGraph JSON object:
json
{
"version": "1.0.0",
"project": {
"name": "<projectName>",
"languages": ["<languages>"],
"frameworks": ["<frameworks>"],
"description": "<projectDescription>",
"analyzedAt": "<ISO 8601 timestamp>",
"gitCommitHash": "<commit hash from Phase 0>"
},
"nodes": [<all merged nodes from Phase 3>],
"edges": [<all merged edges from Phase 3>],
"layers": [<layers from Phase 4>],
"tour": [<steps from Phase 5>]
}
Before writing the assembled graph, validate that:
layers is an array of objects with these required fields: id, name, description, nodeIds
tour is an array of objects with these required fields: order, title, description, nodeIds
tour[*].languageLesson is allowed as an optional string field
Every layers[*].nodeIds entry exists in the merged node set
Every tour[*].nodeIds entry exists in the merged node set
If validation fails, automatically normalize and rewrite the graph into this shape before saving. If the graph still fails final validation after the normalization pass, save it with warnings but mark dashboard auto-launch as skipped.
Write the assembled graph to $PROJECT_ROOT/.understand-anything/intermediate/assembled-graph.json.
Dispatch a subagent using the prompt template at ./graph-reviewer-prompt.md. Read the template file and pass the full content as the subagent's prompt, appending the following additional context:
Additional context from main session:
Phase 1 scan results (file inventory):
json
[list of {path, sizeLines} from scan-result.json]
Phase warnings/errors accumulated during analysis:
[list any batch failures, skipped files, or warnings from Phases 2-5]
Cross-validate: every file in the scan inventory should have a corresponding file: node in the graph. Flag any missing files. Also flag any graph nodes whose filePath doesn't appear in the scan inventory.
Pass these parameters in the dispatch prompt:
Validate the knowledge graph at $PROJECT_ROOT/.understand-anything/intermediate/assembled-graph.json.
Project root: $PROJECT_ROOT
Read the file and validate it for completeness and correctness.
Write output to: $PROJECT_ROOT/.understand-anything/intermediate/review.json
After the subagent completes, read $PROJECT_ROOT/.understand-anything/intermediate/review.json.
If approved: false:
Review the issues list
Apply automated fixes where possible:
Remove edges with dangling references
Fill missing required fields with sensible defaults (e.g., empty tags -> ["untagged"], empty summary -> "No summary available")
Remove nodes with invalid types
Re-run the final graph validation after automated fixes
If critical issues remain after one fix attempt, save the graph anyway but include the warnings in the final report and mark dashboard auto-launch as skipped
If approved: true: Proceed to Phase 7.
Phase 7 — SAVE
Write the final knowledge graph to $PROJECT_ROOT/.understand-anything/knowledge-graph.json.
Write metadata to $PROJECT_ROOT/.understand-anything/meta.json:
Nodes created (broken down by type: file, function, class)
Edges created (broken down by type)
Layers identified (with names)
Tour steps generated (count)
Any warnings from the reviewer
Path to the output file: $PROJECT_ROOT/.understand-anything/knowledge-graph.json
Only automatically launch the dashboard by invoking the /understand-dashboard skill if final graph validation passed after normalization/review fixes.
If final validation did not pass, report that the graph was saved with warnings and dashboard launch was skipped.
Error Handling
If any subagent dispatch fails, retry once with the same prompt plus additional context about the failure.
Track all warnings and errors from each phase in a $PHASE_WARNINGS list. Pass this list to the graph-reviewer in Phase 6 for comprehensive validation.
If it fails a second time, skip that phase and continue with partial results.
ALWAYS save partial results — a partial graph is better than no graph.
Report any skipped phases or errors in the final summary so the user knows what happened.
NEVER silently drop errors. Every failure must be visible in the final report.