Keep operator scopes separate from their containing file and ordinary normalized identifiers, including nested bindings.
Co-Authored-By: OpenAI Codex <noreply@openai.com>
(cherry picked from commit dc43c7c646)
graph.json is written "directed": false and carries direction in arc order
(#563), or in _src/_tgt markers on legacy files (#2309). A plain undirected
node_link_graph load re-orders endpoints by node-list position, so every
link whose target precedes its source in the node list came back reversed
(79% of links on graphify's own graph).
query, path/explain, affected, merge-graphs and the MCP server already
restore direction. The remaining reloaders did not:
- export html / neo4j / falkordb (cypher.txt and --push) / obsidian canvas
- graph.html re-rendered by update, watch and the git hooks
- the git merge driver, which wrote the reversal into the committed
graph.json, even when merging three identical copies
Add paths.restore_arc_direction, the inline idiom query and merge-graphs
already use, and apply it in load_node_link_graph and the export loader.
to_cypher, to_canvas and push_to_neo4j/falkordb now read _src/_tgt as
to_html already does. The merge driver writes direction back into arc
order before saving, as to_json does, so the committed file keeps the
post-#563 convention.
global add has the same bug and is fixed separately.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
(cherry picked from commit 5a461c99d7)
style: replace all em dashes with hyphens
fix: explain hidden .graphify/ folder in skill output and README
fix: rename .graphify/ to graphify-out/ so output is visible by default
- Add GitHub Actions CI workflow (Python 3.10 and 3.12)
- Add CI badge to README
- Add ARCHITECTURE.md: pipeline overview, module table, schema, how to
add a language extractor, security summary
- Move eval reports from tests/ to worked/httpx/ and worked/mixed-corpus/
- Fix README: test count 163→212, language table (13 languages via
tree-sitter), extract.py description, worked examples links
benchmark: 8.8x token reduction on nanoGPT + minGPT + micrograd
- Run AST extraction on 29 Python files across 3 Karpathy repos
- 177 nodes, 246 edges, 17 communities (Leiden)
- 8.8x avg token reduction vs naive full-corpus context stuffing
- Notable: micrograd cleanly splits into engine/nn communities;
nanoGPT model vs training loop correctly separated
- Honest: stdlib import noise flagged, config isolates documented
benchmark: 71.5x token reduction on mixed corpus (code+papers+images)
Full run: nanoGPT+minGPT+micrograd + 5 research papers + 4 images
285 nodes, 340 edges, 53 communities
Average BFS query: 1,726 tokens vs 123,488 naive (71.5x)
Code-only (AST) sub-benchmark: 8.8x on 13k-word corpus
xiehuanyi