graphify-sf 0.3.0

Turn any Salesforce SFDX project into a queryable knowledge graph — fully offline, no org connection required.

graphify-sf

Turn any Salesforce SFDX project into a queryable knowledge graph — fully offline, no org connection required.

graphify-sf parses your local SFDX source directory, extracts every metadata entity (Apex classes, triggers, Flows, Custom Objects, Fields, LWC/Aura components, Profiles, Permission Sets, Layouts, Validation Rules, and more), maps the real relationships between them, and produces an interactive knowledge graph with community detection.

Built for developers and architects who need to understand large Salesforce orgs without clicking through Setup.

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Features

• Fully offline — pure XML/source parsing, no Salesforce CLI, no org connection, no API calls
• Honest provenance — every edge is tagged EXTRACTED (from explicit XML) or INFERRED (from source patterns)
• Reference file support — indexes .md, .txt, .pdf, .xlsx, .docx, and images alongside SF metadata; headings become sub-nodes, SF component mentions create cross-reference edges
• Community detection — Louvain/Leiden clustering surfaces cross-metadata couplings you wouldn't think to ask about
• Interactive HTML visualization — force-directed graph with search, community filtering, and node inspector
• Multiple export formats — HTML, SVG, Mermaid call-flow, D3 tree, Obsidian vault, GraphML, Cypher/Neo4j, JSON, Markdown wiki
• Incremental updates — re-extract only changed files with --update; dry-run diff with check-update
• Agentic IDE integration — install a /graphify-sf skill into Claude Code, Cursor, Codex, Kiro, Gemini, and 10+ more IDEs
• MCP server — expose graph query tools to Claude Desktop and other MCP clients over stdio
• File watcher — auto-rebuild on metadata changes with watch
• Git-native — post-commit hook for automatic rebuilds; merge driver for conflict-free graph.json merges

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Quick Start

# Install
pip install graphify-sf

# Or with uv (recommended)
uv tool install graphify-sf

# Build the knowledge graph from your SFDX project
graphify-sf /path/to/sfdx-project

# Open the interactive visualization
open graphify-sf-out/graph.html

On the first run you'll see:

[graphify-sf] scanning /path/to/sfdx-project
[graphify-sf] 847 metadata files found, 0 skipped
[graphify-sf] extracting metadata...
[graphify-sf] extracted 1243 nodes, 3891 edges
[graphify-sf] clustering...
[graphify-sf] 7 communities found
[graphify-sf] wrote graphify-sf-out/GRAPH_REPORT.md
[graphify-sf] wrote graphify-sf-out/graph.json
[graphify-sf] wrote graphify-sf-out/graph.html

[graphify-sf] done
  1243 nodes · 3891 edges · 7 communities

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Output Files

File	Description
graphify-sf-out/graph.html	Interactive force-directed graph — search, filter by community, inspect nodes
graphify-sf-out/graph.json	GraphRAG-ready JSON with node/edge provenance, community assignments
graphify-sf-out/GRAPH_REPORT.md	Plain-language report: god nodes, surprising connections, suggested questions
graphify-sf-out/manifest.json	File hash manifest for incremental updates
graphify-sf-out/.graphify_sf_labels.json	Community label sidecar

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Command Reference

Main Pipeline

# Build graph from SFDX project (full scan)
graphify-sf <path>

# Custom output directory
graphify-sf <path> --out my-graph-out

# Build directed graph (edges have source→target direction)
graphify-sf <path> --directed

# Skip HTML visualization (faster, report + JSON only)
graphify-sf <path> --no-viz

# Overwrite graph.json even if new graph has fewer nodes
graphify-sf <path> --force

# Parallel extraction with N workers
graphify-sf <path> --max-workers 8

# Add AI semantic layer (finds business-rule duplication, dead metadata, etc.)
graphify-sf <path> --backend claude
graphify-sf <path> --backend gemini
graphify-sf <path> --backend auto     # auto-detect from available API keys

# Control LLM chunk size (tokens per API call, default: 40000)
graphify-sf <path> --backend gemini --token-budget 20000

Incremental Updates

# Re-extract only changed/new files, merge into existing graph
graphify-sf <path> --update

# Dry-run: show what --update would change without doing it
# Exits 1 if changes exist (useful for CI)
graphify-sf check-update <path>

# Re-run community detection on an existing graph (no re-extraction)
graphify-sf cluster-only <path>
graphify-sf cluster-only --no-viz --graph path/to/graph.json

Exploration

# BFS traversal — broad context around a question
graphify-sf query "What fires when an Account is updated?"

# DFS — trace a specific dependency chain
graphify-sf query "AccountTrigger dependencies" --dfs

# Cap response at N tokens (default: 2000)
graphify-sf query "..." --budget 4000

# Shortest path between two metadata nodes
graphify-sf path "AccountService" "Account__c"

# Full node details: type, file, community, degree, all connections
graphify-sf explain "AccountTrigger"

# Filter connections by relation type (shows all matches, no cap)
graphify-sf explain "Account" --relation triggers
graphify-sf explain "Account" --relation queries
graphify-sf explain "Account" --relation invokes

# Detailed graph statistics: type distribution, edge relations, density
graphify-sf stats
graphify-sf stats --graph path/to/graph.json

All exploration commands accept --graph <path> to target a specific graph.json.

Export Formats

# Regenerate the interactive HTML visualization
graphify-sf export html

# D3 v7 collapsible tree — organized by community, hover tooltips
graphify-sf export tree

# Mermaid call-flow diagram — Apex→Object→Trigger dependency chains
graphify-sf export callflow-html

# Static SVG graph image (dark theme, community colors, degree-sized nodes)
# Requires: pip install graphify-sf[svg]
graphify-sf export svg

# Obsidian vault — one note per node + community overview notes
graphify-sf export obsidian

# GraphML — open in Gephi, yEd, Cytoscape
graphify-sf export graphml

# Neo4j Cypher statements (writes cypher.txt)
graphify-sf export cypher

# Push directly to a running Neo4j instance
# Requires: pip install graphify-sf[neo4j]
graphify-sf export neo4j --push \
  --uri bolt://localhost:7687 \
  --user neo4j \
  --password mypassword

# Agent-crawlable Markdown wiki (one .md per community)
graphify-sf export wiki

# Re-export clean graph.json
graphify-sf export json

# All formats accept --graph and --out:
graphify-sf export html --graph path/to/graph.json --out output-dir/

Merging Graphs

# Merge two or more graph.json files into one
# Deduplicates nodes by label, re-clusters, regenerates report + HTML
graphify-sf merge-graphs a/graph.json b/graph.json --out merged/graph.json
graphify-sf merge-graphs g1.json g2.json g3.json --out combined.json --no-viz

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LLM Semantic Extraction

The --backend flag adds an AI-powered extraction layer on top of the static XML/source parser. The LLM finds relationships that static parsing cannot see:

What it finds	Example
Business rule duplication	Same validation in Apex class AND Flow (both enforce a required field)
Semantic equivalence	Two Profiles granting nearly identical Object/Field access
Implicit data couplings	Apex writes to Object A; Flow reads from Object A (no XML edge between them)
Dead metadata	Profile grants access to Apex class that is never called by any Trigger/Flow
Trigger-bypass risk	Bulk utility using Database.insert outside trigger context

Supported Backends

Backend	Flag	API Key env var	Estimated cost (1k files)
Claude	--backend claude	ANTHROPIC_API_KEY	~$0.50
Gemini	--backend gemini	GEMINI_API_KEY or GOOGLE_API_KEY	~$0.05
Kimi K2	--backend kimi	MOONSHOT_API_KEY	~$0.30
OpenAI	--backend openai	OPENAI_API_KEY	~$0.20
AWS Bedrock	--backend bedrock	AWS_PROFILE / AWS_REGION	~$0.50
Ollama (local)	--backend ollama	OLLAMA_BASE_URL	free
Auto-detect	--backend auto	(first found)	varies

Install the SDK

# Claude
pip install graphify-sf[claude]

# Gemini, Kimi, OpenAI, Ollama (all use the openai SDK)
pip install graphify-sf[gemini]   # or [kimi], [openai], [ollama]

# AWS Bedrock
pip install graphify-sf[bedrock]

# Claude + all OpenAI-compat backends
pip install graphify-sf[llm]

Environment Variables

Variable	Description
ANTHROPIC_API_KEY	Claude API key
GEMINI_API_KEY / GOOGLE_API_KEY	Gemini API key
MOONSHOT_API_KEY	Kimi K2 API key
OPENAI_API_KEY	OpenAI API key
AWS_PROFILE / AWS_REGION	AWS credentials for Bedrock
OLLAMA_BASE_URL	Ollama server URL (default: http://localhost:11434/v1)
OLLAMA_MODEL	Ollama model name (default: qwen2.5-coder:7b)
GRAPHIFY_SF_GEMINI_MODEL	Override Gemini model
GRAPHIFY_SF_OPENAI_MODEL	Override OpenAI model
GRAPHIFY_SF_BEDROCK_MODEL	Override Bedrock model
GRAPHIFY_SF_MAX_OUTPUT_TOKENS	Override max output tokens per LLM call
GRAPHIFY_SF_API_TIMEOUT	Override API timeout in seconds (default: 600)

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Agentic IDE Integration

Install a /graphify-sf skill that lets any supported AI coding assistant run the full pipeline and explore the graph through natural language.

Zero-dependency install via npx (no Python required)

Uses the open agent skills ecosystem — works across 55+ AI coding agents.

# Install globally (all projects)
npx skills add raykuonz/graphify-sf

# Install for this project only
npx skills add raykuonz/graphify-sf --project

The skill instructs your agent to run graphify-sf via uvx or pipx run on demand — no permanent Python install needed.

Install via Python CLI

graphify-sf install
# Installs to ~/.claude/skills/graphify-sf/SKILL.md

Install for a specific platform

graphify-sf install --platform cursor    # Cursor → .cursor/skills/graphify-sf/SKILL.md
graphify-sf install --platform codex     # OpenAI Codex
graphify-sf install --platform kiro      # AWS Kiro
graphify-sf install --platform gemini    # Google Gemini CLI
graphify-sf install --platform aider     # aider
graphify-sf install --platform copilot   # GitHub Copilot
graphify-sf install --platform opencode  # OpenCode

Supported platforms: antigravity, aider, claude, codex, copilot, cursor, droid, gemini, hermes, kimi, kiro, opencode, pi, trae, trae-cn

Project-scoped install (this project only)

graphify-sf install --scope project

Install with symlink — one canonical file, many IDEs (--link)

Use --link to write a single canonical skill file to .agents/skills/graphify-sf/SKILL.md and create a symlink from the platform-specific path to it. This means upgrading graphify-sf and re-running install --link once updates every symlinked IDE at the same time — no per-platform reinstalls needed.

# Write canonical to .agents/skills/graphify-sf/SKILL.md,
# symlink from ~/.claude/skills/graphify-sf/SKILL.md
graphify-sf install --link

# Same for another IDE
graphify-sf install --link --platform gemini
graphify-sf install --link --platform kiro --scope project

Symlink strategy:

• Project scope (--scope project): relative symlink — stays valid if the project directory is moved
• Global scope (default): absolute symlink — stays valid regardless of working directory

Platforms that natively use .agents/ (codex, antigravity): no symlink is created — .agents/skills/graphify-sf/SKILL.md is already their install target, so they share the canonical file directly.

Cursor note: Cursor discovers skills from both .cursor/skills/ and .agents/skills/. When --link is used, the canonical file lands in .agents/skills/graphify-sf/SKILL.md, and Cursor picks it up from there automatically — the symlink to .cursor/skills/ is an optional convenience if you want an explicit entry there too.

Uninstall

graphify-sf uninstall
graphify-sf uninstall --platform cursor --scope project

Update / reinstall after upgrading graphify-sf

The install command is idempotent — re-running it always overwrites the existing skill file with the latest version bundled in the package:

# 1. Upgrade the package
pip install --upgrade graphify-sf
# or: uv tool upgrade graphify-sf

# 2. Refresh the installed skill
graphify-sf install

For a specific platform or scope, pass the same flags used during the original install:

graphify-sf install --platform cursor --scope project

If you used --link when installing, a single re-run updates the canonical file and all symlinked IDEs pick it up automatically — no per-platform reinstalls needed:

graphify-sf install --link          # re-writes .agents/skills/graphify-sf/SKILL.md; all symlinks follow

Always-on: register in AGENTS.md (all agents)

Write mandatory enforcement rules to AGENTS.md so every AI coding agent (Claude Code, Codex, Cursor, Kiro, Gemini CLI, aider, Copilot, etc.) automatically uses the graph instead of grep:

# Write ./AGENTS.md enforcement block (always project-scoped)
graphify-sf agents install

# Remove
graphify-sf agents uninstall

The written block instructs agents to:

• Read graphify-sf-out/GRAPH_REPORT.md before answering metadata questions
• Navigate the wiki instead of reading raw metadata files
• Use graphify-sf query instead of grep/find on metadata files
• Run graphify-sf . --update --no-viz after modifying metadata files

Always-on: register in CLAUDE.md (Claude Code only)

# Global ~/.claude/CLAUDE.md
graphify-sf claude install

# Project-level ./CLAUDE.md
graphify-sf claude install --scope project

# Remove
graphify-sf claude uninstall

Using the skill

Once installed, type /graphify-sf in your AI coding assistant. The skill will:

1. Detect your SFDX metadata files
2. Extract, build, cluster, and report
3. Present god nodes, surprising connections, and suggested questions
4. Offer to trace the most interesting question through the graph

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Git Integration

Post-commit hook — auto-rebuild on commit

# Install: rebuilds graph after any commit that touches metadata files
graphify-sf hook install

# Remove
graphify-sf hook uninstall

# Check status
graphify-sf hook status

The hook installs both post-commit and post-checkout hooks:

• post-commit — diffs HEAD~1..HEAD, triggers rebuild only when .cls/.trigger/.flow-meta.xml/*-meta.xml files changed
• post-checkout — triggers rebuild on branch switches (when graphify-sf-out/ exists)

Both hooks run rebuilds in the background (nohup ... & disown) so git commit and git checkout return immediately. Rebuild log: ~/.cache/graphify-sf-rebuild.log.

Both hooks skip during rebase, merge, and cherry-pick. The _hooks_dir() respects core.hooksPath (Husky/Lefthook compatibility).

Merge driver — conflict-free graph.json merges

When two branches both modify graph.json, a normal git merge produces a JSON conflict. The graphify-sf merge driver performs a union merge instead (all nodes + all edges from both branches, deduplicated and re-clustered):

# Install (configures .gitattributes + .git/config)
graphify-sf merge-driver install

# Uninstall
graphify-sf merge-driver uninstall

After installation, git calls graphify-sf merge-driver run %O %A %B automatically on graph.json conflicts.

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File Watcher

Auto-rebuild the graph whenever metadata files change:

# Watch current directory, rebuild on any .cls/.trigger/.flow-meta.xml/... change
graphify-sf watch .

# Custom output directory and debounce (seconds to wait after last change)
graphify-sf watch /path/to/project --out my-out --debounce 5

# Also regenerate graph.html on each rebuild (slower)
graphify-sf watch . --viz

# Build directed graph on rebuild
graphify-sf watch . --directed

If watchdog is installed (pip install graphify-sf[watch]), uses efficient FS events. Otherwise falls back to polling every 5 seconds with a notice.

The watcher runs an initial full build if no graph.json exists, then incremental --update rebuilds on each detected change.

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MCP Server

Expose the graph as a set of tools to any MCP-compatible client (Claude Desktop, Cursor MCP, etc.) over stdio JSON-RPC 2.0:

graphify-sf serve
graphify-sf serve --graph path/to/graph.json

Available tools:

Tool	Description
graph_stats	Node/edge/community counts, community labels, top sf_types
query	BFS or DFS traversal for a natural-language question
get_node	Full details for a single node (type, file, degree, all neighbors)
get_neighbors	Connections of a node (up to N, sorted by degree, optional relation_filter)
shortest_path	Shortest path between two named metadata nodes
god_nodes	Highest-degree nodes — the most central metadata in the org
list_communities	All communities with their labels and member counts
get_community	All members of a specific community (by id or label)

Available resources (via resources/list and resources/read):

Resource URI	Description
graphify-sf://report	Full GRAPH_REPORT.md as text/markdown
graphify-sf://stats	Graph statistics JSON
graphify-sf://god-nodes	Top 20 god nodes JSON
graphify-sf://surprises	Surprising cross-community connections JSON
graphify-sf://audit	EXTRACTED/INFERRED/AMBIGUOUS edge breakdown
graphify-sf://questions	Suggested exploration questions JSON

Claude Desktop configuration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "graphify-sf": {
      "command": "graphify-sf",
      "args": ["serve", "--graph", "/path/to/your/project/graphify-sf-out/graph.json"]
    }
  }
}

---

CI/CD Integration

Check for stale graph before a build

# GitHub Actions example
- name: Check if graph needs update
  run: graphify-sf check-update . --out graphify-sf-out
  # Exits 0 = graph is current, exits 1 = changes detected

Rebuild on metadata changes

- name: Rebuild knowledge graph
  run: graphify-sf . --update --no-viz --force

Check-update pattern (conditional rebuild)

graphify-sf check-update . || graphify-sf . --update --no-viz

---

Supported Metadata Types

sf_type	Source	What it extracts
ApexClass	.cls	Class name, methods, SOQL queries, DML, callouts, extends/implements
ApexTrigger	.trigger	Trigger name, target object, DML events, handler class references
ApexMethod	.cls	Individual method signatures within a class
Flow	.flow-meta.xml	Flow name, referenced objects, fields, called subflows
CustomObject	.object-meta.xml	Object name, fields, validation rules, record types
CustomField	.field-meta.xml	Field name, type, lookup targets
ValidationRule	*-meta.xml	Rule name, parent object, formula references
RecordType	*-meta.xml	Record type name, parent object
Layout	.layout-meta.xml	Layout name, referenced fields and objects
LWCBundle	lwc/*/	Component name, imported Apex controllers, child components
AuraBundle	aura/*/	Component name, Apex controller references
Profile	.profile-meta.xml	Profile name, object/field/class permissions
PermissionSet	.permissionset-meta.xml	Permission set name, granted permissions
CustomLabel	.labels-meta.xml	Label names
NamedCredential	.namedCredential-meta.xml	Credential name, endpoint
ExternalService	.externalService-meta.xml	Service name, referenced schema
Bot	.bot-meta.xml	Agentforce agent definition — label, bot user
BotVersion	.botVersion-meta.xml	Agent version — links to orchestrator flow, topics, planner
GenAiPlugin	.genAiPlugin-meta.xml	Agentforce Topic — groups actions, contains GenAiFunctions
GenAiFunction	.genAiFunction-meta.xml	Agentforce Action — invokes an Apex class or Flow
GenAiPlannerBundle	.genAiPlannerBundle-meta.xml	Agent planner — maps to sub-agent topics
AiAuthoringBundle	.aiAuthoringBundle-meta.xml	Authoring container linking Bot to BotVersion
PromptTemplate	.promptTemplate-meta.xml	AI prompt template — primary object, flex action references

Edge Relations

relation	Meaning
triggers	Apex trigger fires on DML events for this object
queries	Apex performs SOQL query on this sObject
dml	Apex performs insert/update/delete/upsert on this object
calls	Apex class/method calls another class/method
references	Flow, Layout, or Profile references this metadata
contains	Parent contains child (Object→Field, Class→Method)
extends	Apex class extends a superclass
implements	Apex class implements an interface
invokes	Flow calls a subflow, or Agentforce action calls an Apex class / Flow

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Reference File Support

graphify-sf indexes non-Salesforce reference files that live alongside your SFDX metadata — design docs, data dictionaries, architecture notes, and more — and cross-references them against SF component names found in the graph.

File type	Extensions	What is extracted	Library required
Document	.md, .mdx, .txt, .rst, .html	H1/H2/H3 headings as sub-nodes; SF name mentions → references edges	(none — base install)
Image	.png, .jpg, .jpeg, .gif, .webp, .svg	Metadata node only	(none — base install)
PDF	.pdf	Full text extracted; SF name mentions → references edges	graphify-sf[docs]
Word doc	.docx	Converted to Markdown sidecar in graphify-sf-out/converted/; then processed as Document	graphify-sf[docs]
Spreadsheet	.xlsx	Converted to Markdown sidecar; structural nodes (workbook → sheet → named table → column)	graphify-sf[docs]

SF component mention detection uses a heuristic regex (PascalCase identifiers and names ending in __c, __r, __mdt, etc.). Mention edges are tagged INFERRED and resolved to real SF node IDs in the cross-reference pass; unresolved mentions are silently dropped.

If the required library is not installed, the file is skipped with a warning — the pipeline never crashes due to a missing optional dependency.

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Configuration

All configuration via environment variables:

Variable	Default	Description
GRAPHIFY_SF_OUT	graphify-sf-out	Default output directory
GRAPHIFY_SF_VIZ_NODE_LIMIT	5000	Max nodes before HTML viz switches to aggregated community view
GRAPHIFY_SF_FORCE	``	Set to 1/true to always overwrite graph.json
GRAPHIFY_SF_REBUILD_MEMORY_LIMIT_MB	``	Cap memory usage for background watch rebuilds (MB)
CLAUDE_CONFIG_DIR	~/.claude	Override Claude Code config directory — only set if your Claude Code config lives at a non-standard path (company-managed dotfiles, CI, or multiple installations)

.graphifysfignore

Place a .graphifysfignore file at your project root to exclude files/directories from scanning (same syntax as .gitignore glob patterns):

# Exclude test data
force-app/test/**
# Exclude archived components
force-app/archived/**
*.bak

---

Optional Extras

# Better community detection (Leiden algorithm, Python < 3.13 only)
pip install graphify-sf[leiden]

# Neo4j direct push support
pip install graphify-sf[neo4j]

# SVG/PNG graph image export
pip install graphify-sf[svg]

# Watchdog for efficient file-system events in watch mode
pip install graphify-sf[watch]

# MCP SDK (reserved for future native MCP SDK adoption)
pip install graphify-sf[mcp]

# LLM semantic extraction — Claude
pip install graphify-sf[claude]

# LLM semantic extraction — Gemini / Kimi / OpenAI / Ollama (all use openai SDK)
pip install graphify-sf[gemini]

# LLM semantic extraction — AWS Bedrock
pip install graphify-sf[bedrock]

# LLM semantic extraction — Claude + all OpenAI-compat backends
pip install graphify-sf[llm]

# Reference file support (PDF, Word, Excel alongside SFDX metadata)
pip install graphify-sf[docs]

# Everything
pip install graphify-sf[all]

---

Requirements

• Python 3.10+
• No Salesforce CLI, no org connection, no API keys
• Core dependencies: networkx, datasketch, rapidfuzz

---

Architecture

graphify_sf/
├── detect.py       File scanner — classifies .cls/.trigger/.flow-meta.xml/... files + doc/paper/image types
├── extract/        Per-type extractors — Apex, Flow, Object, LWC, Aura, Profile, Agentforce, ...
│   └── doc.py      Document extractor — headings, SF mentions, xlsx structure, PDF/image nodes
├── build.py        Graph construction — node/edge deduplication, merge strategies
├── cluster.py      Community detection — Louvain (default) or Leiden (optional)
├── analyze.py      Graph analysis — god nodes, surprising connections, question generation
├── report.py       GRAPH_REPORT.md generation
├── export.py       All export formats — HTML, SVG, Obsidian, GraphML, Cypher, wiki, ...
├── llm.py          LLM semantic extraction — 6 backends, SF-specific prompt, adaptive retry
├── watch.py        File-system watcher — fcntl rebuild lock, resource limits, callflow regen
├── serve.py        MCP stdio server — 8 tools, 6 resources, blank-stdin filter
├── cache.py        Per-file extraction cache — SHA-256 keyed, atomic writes
├── validate.py     Extraction schema validation — catches bad extractors before graph assembly
├── security.py     Input sanitization — sanitize_label, validate_graph_path
└── __main__.py     CLI entry point — all command dispatch

---

Changelog

v0.3.0

Edge extraction improvements

• Record-Triggered Flows now emit triggers edges to the target object with EXTRACTED confidence
• Flows emit invokes edges for subflow references and calls edges for Apex action calls
• Custom Fields emit references (Lookup) and master_detail (MasterDetail) edges to target objects
• ValidationRule formula fields emit INFERRED references edges to the fields they reference
• Agentforce: GenAiPlannerBundle local actions parsed as inline GenAiFunction nodes; conversationDefinitionPlanners XML path supported for BotVersion planner references; PromptTemplate flexTemplateActionCalls create reference edges
• build.py exports _resolve_apex_calls, _derive_object_edges, and _ensure_stub_nodes for use in the extract pipeline

Bug fixes

• Fixed: standard-object edges (Lead, Account, etc.) were incorrectly downgraded from EXTRACTED to INFERRED confidence because _ensure_stub_nodes ran after _resolve_cross_references; ordering is now correct
• Fixed: Apex calls edges had ~96% false-positive rate from local variable method calls; new _looks_like_apex_class() heuristic reduces noise to ~36%

Tests

• 65 new tests → 268 total (up from 203)
• New test_extract_pipeline.py with regression test for the stub-node ordering fix

---

v0.2.0

• Reference file support: .md, .txt, .pdf, .xlsx, .docx, and images indexed alongside SF metadata
• Headings become sub-nodes; SF component mentions create cross-reference edges
• .[docs] extra for PDF/Word/Excel parsing

---

v0.1.0

• Initial release: Apex, Flow, Object, LWC, Aura, Profile, Permission Set, Agentforce metadata extraction
• NetworkX graph with Louvain community detection
• HTML, JSON, Obsidian, GraphML, Cypher, SVG, Mermaid export formats
• Incremental updates via SHA-256 manifest
• MCP stdio server, file watcher, git hooks

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License

MIT