fix skill large-corpus path and detect output

detect: add scan_root to return dict so skill can strip absolute prefix
  when computing relative subdirectory breakdown; remove stale --no-semantic
  flag reference from large-corpus warning (flag does not exist)

skill: clarify fast path checks CWD graphify-out/graph.json (project root);
  remove hardcoded --backend gemini from multi-subfolder example — users
  should pass whichever backend key they have; expand large-corpus gate
  instruction to use scan_root for relative paths, filter graphify-out/
  converted sidecars, and handle flat repos with no subdirectories

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

graphify/detect.py

@@ -825,7 +825,7 @@ def detect(root: Path, *, follow_symlinks: bool | None = None, google_workspace:
         warning = (
             f"Large corpus: {total_files} files · ~{total_words:,} words. "
             f"Semantic extraction will be expensive (many Claude tokens). "
-            f"Consider running on a subfolder, or use --no-semantic to run AST-only."
+            f"Consider running on a subfolder."
         )
 
     return {
@@ -836,6 +836,7 @@ def detect(root: Path, *, follow_symlinks: bool | None = None, google_workspace:
         "warning": warning,
         "skipped_sensitive": skipped_sensitive,
         "graphifyignore_patterns": len(ignore_patterns),
+        "scan_root": str(root.resolve()),
     }
 
 

graphify/skill.md

@@ -49,7 +49,7 @@ Drop any folder of code, docs, papers, images, or video into graphify and get a
 
 If the user invoked `/graphify --help` or `/graphify -h` (with no other arguments), print the contents of the `## Usage` section above verbatim and stop. Do not run any commands, do not detect files, do not default the path to `.`. Just print the Usage block and return.
 
-**Fast path — existing graph:** Before doing anything else, check whether `graphify-out/graph.json` already exists in the current directory. If it does AND the user's request is a natural-language question about the codebase (e.g. "How does X work?", "What calls Y?", "Trace the data flow through Z") and NOT an explicit rebuild command (`--update`, `--cluster-only`, or a bare path/URL that implies fresh extraction): **skip Steps 1–5 entirely and jump straight to `## For /graphify query`.** Run `graphify query "<question>"` immediately. Do not run detect. Do not check corpus size. Do not ask the user to narrow. The graph is already built — use it.
+**Fast path — existing graph:** Before doing anything else, check whether `graphify-out/graph.json` exists. The expected location is `graphify-out/graph.json` relative to the **current working directory** (i.e. the project root where you are running commands). If it exists AND the user's request is a natural-language question about the codebase (e.g. "How does X work?", "What calls Y?", "Trace the data flow through Z") and NOT an explicit rebuild command (`--update`, `--cluster-only`, or a bare path/URL that implies fresh extraction): **skip Steps 1–5 entirely and jump straight to `## For /graphify query`.** Run `graphify query "<question>"` immediately. Do not run detect. Do not check corpus size. Do not ask the user to narrow. The graph is already built — use it.
 
 If no path was given, use `.` (current directory). Do not ask the user for a path.
 
@@ -85,9 +85,10 @@ Graphify clones into `~/.graphify/repos/<owner>/<repo>` and reuses existing clon
 The skill pipeline writes all intermediate and final outputs to `graphify-out/` in the current working directory. Running the skill on each subfolder separately will clobber the same output dir. Instead, use the CLI directly for each subfolder — it places `graphify-out/` *inside* the scanned path:
 
 ```bash
-graphify extract ./core/     --backend gemini  # → ./core/graphify-out/graph.json
-graphify extract ./service/  --backend gemini  # → ./service/graphify-out/graph.json
-graphify extract ./platform/ --backend gemini  # → ./platform/graphify-out/graph.json
+graphify extract ./core/     # → ./core/graphify-out/graph.json
+graphify extract ./service/  # → ./service/graphify-out/graph.json
+graphify extract ./platform/ # → ./platform/graphify-out/graph.json
+# Add --backend gemini|kimi|openai|deepseek|claude-cli depending on which API key you have set
 
 # Then merge at the project root:
 graphify merge-graphs \
@@ -169,7 +170,13 @@ Omit any category with 0 files from the summary.
 Then act on it:
 - If `total_files` is 0: stop with "No supported files found in [path]."
 - If `skipped_sensitive` is non-empty: mention file count skipped, not the file names.
-- If `total_words` > 2,000,000 OR `total_files` > 500: show the warning. Then compute the top 5 subdirectories by file count using **relative paths from INPUT_PATH** (strip the absolute scan-root prefix so you show `core/`, `service/`, not `/home/user/project/core/`), show those, then ask which subfolder to run on. Wait for the user's answer before proceeding.
+- If `total_words` > 2,000,000 OR `total_files` > 500: show the warning. Then compute the top 5 first-level subdirectories by file count:
+  - Read `scan_root` from the detect JSON (always an absolute path to the resolved INPUT_PATH).
+  - Concatenate all file lists across all types (`code`, `document`, `paper`, `image`, `video`).
+  - Filter out any path that starts with `scan_root + "/graphify-out/"` to exclude converted sidecars.
+  - For each file, strip the `scan_root` prefix and take the first path component. Files directly in `scan_root` with no subdirectory count as `(root)`.
+  - If all files are in `(root)` with no subdirectories, do not ask to narrow — no subfolders exist. Instead suggest `--no-cluster` to skip the expensive clustering step and proceed.
+  - Otherwise rank by count, show the top 5 with file counts, then ask which subfolder to run on. Wait for the user's answer before proceeding.
 - Otherwise: proceed directly to Step 2.5 if video files were detected, or Step 3 if not.
 
 ### Step 2.5 - Transcribe video / audio files (only if video files detected)