Update README and descriptions to mention video/audio and YouTube support

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: safishamsi

README.md

@@ -39,7 +39,7 @@ Same syntax as `.gitignore`. Patterns match against file paths relative to the f
 
 ## How it works
 
-graphify runs in two passes. First, a deterministic AST pass extracts structure from code files (classes, functions, imports, call graphs, docstrings, rationale comments) with no LLM needed. Second, Claude subagents run in parallel over docs, papers, and images to extract concepts, relationships, and design rationale. The results are merged into a NetworkX graph, clustered with Leiden community detection, and exported as interactive HTML, queryable JSON, and a plain-language audit report.
+graphify runs in three passes. First, a deterministic AST pass extracts structure from code files (classes, functions, imports, call graphs, docstrings, rationale comments) with no LLM needed. Second, video and audio files are transcribed locally with faster-whisper using a domain-aware prompt derived from corpus god nodes — transcripts are cached so re-runs are instant. Third, Claude subagents run in parallel over docs, papers, images, and transcripts to extract concepts, relationships, and design rationale. The results are merged into a NetworkX graph, clustered with Leiden community detection, and exported as interactive HTML, queryable JSON, and a plain-language audit report.
 
 **Clustering is graph-topology-based — no embeddings.** Leiden finds communities by edge density. The semantic similarity edges that Claude extracts (`semantically_similar_to`, marked INFERRED) are already in the graph, so they influence community detection directly. The graph structure is the similarity signal — no separate embedding step or vector database needed.
 
@@ -251,6 +251,8 @@ Works with any mix of file types:
 | Office | `.docx .xlsx` | Converted to markdown then extracted via Claude (requires `pip install graphifyy[office]`) |
 | Papers | `.pdf` | Citation mining + concept extraction |
 | Images | `.png .jpg .webp .gif` | Claude vision - screenshots, diagrams, any language |
+| Video / Audio | `.mp4 .mov .mkv .webm .avi .m4v .mp3 .wav .m4a .ogg` | Transcribed locally with faster-whisper, transcript fed into Claude extraction (requires `pip install graphifyy[video]`) |
+| YouTube / URLs | any video URL | Audio downloaded via yt-dlp, then same Whisper pipeline (requires `pip install graphifyy[video]`) |
 
 ## What you get
 
@@ -288,7 +290,7 @@ Token reduction scales with corpus size. 6 files fits in a context window anyway
 
 ## Privacy
 
-graphify sends file contents to your AI coding assistant's underlying model API for semantic extraction of docs, papers, and images — Anthropic (Claude Code), OpenAI (Codex), or whichever provider your platform uses. Code files are processed locally via tree-sitter AST — no file contents leave your machine for code. No telemetry, usage tracking, or analytics of any kind. The only network calls are to your platform's model API during extraction, using your own API key.
+graphify sends file contents to your AI coding assistant's underlying model API for semantic extraction of docs, papers, and images — Anthropic (Claude Code), OpenAI (Codex), or whichever provider your platform uses. Code files are processed locally via tree-sitter AST — no file contents leave your machine for code. Video and audio files are transcribed locally with faster-whisper — audio never leaves your machine. No telemetry, usage tracking, or analytics of any kind. The only network calls are to your platform's model API during extraction, using your own API key.
 
 ## Tech stack
 

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "graphifyy"
 version = "0.3.29"
-description = "AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, OpenClaw, Factory Droid, Trae) - turn any folder of code, docs, papers, or images into a queryable knowledge graph"
+description = "AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, OpenClaw, Factory Droid, Trae) - turn any folder of code, docs, papers, images, or videos into a queryable knowledge graph"
 readme = "README.md"
 license = { file = "LICENSE" }
 keywords = ["claude", "claude-code", "codex", "opencode", "cursor", "knowledge-graph", "rag", "graphrag", "obsidian", "community-detection", "tree-sitter", "leiden", "llm"]