Feature/integrate battels api/gh38

mobile_app/.claude/settings.json

@@ -10,13 +10,13 @@
       "Bash(ConvertTo-Json)",
       "Bash(git commit -m ' *)",
       "Bash(Get-ChildItem -Recurse \"c:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\\\\lib\" -File)",
-      "Bash(gh pr create --title 'feat\\(categories\\) ✨ add categories screen with 2-column grid' --body ' *)",
-      "Bash(gh pr create --title 'feat\\(ranking\\) ✨ add ranking screen with global leaderboard' --base development --body ' *)",
+      "Bash(gh pr create --title 'feat\\(categories\\) \u2728 add categories screen with 2-column grid' --body ' *)",
+      "Bash(gh pr create --title 'feat\\(ranking\\) \u2728 add ranking screen with global leaderboard' --base development --body ' *)",
       "PowerShell(Get-ChildItem -Path \"c:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\\\\lib\" -Recurse | Select-Object FullName | ForEach-Object { $_.FullName -replace [regex]::Escape\\(\"c:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\"\\), \"\" })",
-      "Bash(gh pr create --title 'feat\\(profile\\) ✨ add profile screen with stats, heatmap, and category accuracy' --base main --body ' *)",
-      "Bash(gh pr create --title 'feat\\(profile\\) ✨ add profile screen with stats, heatmap, and category accuracy' --base development --body ' *)",
+      "Bash(gh pr create --title 'feat\\(profile\\) \u2728 add profile screen with stats, heatmap, and category accuracy' --base main --body ' *)",
+      "Bash(gh pr create --title 'feat\\(profile\\) \u2728 add profile screen with stats, heatmap, and category accuracy' --base development --body ' *)",
       "Bash(Get-ChildItem -Path \"C:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\\\\lib\\\\features\\\\ranking\\\\\" -Recurse -File | Select-Object FullName)",
-      "Bash(gh pr create --title 'feat\\(quiz\\) ✨ add quiz game with timer, answers, and result screen' --base development --body ' *)",
+      "Bash(gh pr create --title 'feat\\(quiz\\) \u2728 add quiz game with timer, answers, and result screen' --base development --body ' *)",
       "Bash(Get-ChildItem -Path \"c:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\\\\lib\\\\features\\\\auth\" -Recurse -File)",
       "Bash(git mv *)",
       "Bash(Get-ChildItem -Path \"c:\\\\Users\\\\youne\\\\Desktop\\\\repos\\\\culturo\\\\mobile_app\\\\lib\" -Recurse -Directory)",
@@ -41,6 +41,26 @@
           }
         ]
       }
+    ],
+    "PreToolUse": [
+      {
+        "matcher": "Bash",
+        "hooks": [
+          {
+            "type": "command",
+            "command": "CMD=$(python3 -c \"import json,sys; d=json.load(sys.stdin); print(d.get('tool_input',d).get('command',''))\" 2>/dev/null || true); case \"$CMD\" in *grep*|*rg\\ *|*ripgrep*|*find\\ *|*fd\\ *|*ack\\ *|*ag\\ *)   [ -f graphify-out/graph.json ] &&   echo '{\"hookSpecificOutput\":{\"hookEventName\":\"PreToolUse\",\"additionalContext\":\"graphify: knowledge graph at graphify-out/. For focused questions, run `graphify query \\\"<question>\\\"` (scoped subgraph, usually much smaller than GRAPH_REPORT.md) instead of grepping raw files. Read GRAPH_REPORT.md only for broad architecture context.\"}}'   || true ;; esac"
+          }
+        ]
+      },
+      {
+        "matcher": "Read|Glob",
+        "hooks": [
+          {
+            "type": "command",
+            "command": "HIT=$(python3 -c \"import json,sys;d=json.load(sys.stdin);t=d.get('tool_input',d);s=(str(t.get('file_path') or '')+' '+str(t.get('pattern') or '')+' '+str(t.get('path') or '')).lower().replace(chr(92),'/');exts=('.py','.js','.ts','.tsx','.jsx','.go','.rs','.java','.rb','.c','.h','.cpp','.hpp','.cc','.cs','.kt','.swift','.php','.scala','.lua','.sh','.md','.rst','.txt','.mdx');sys.stdout.write('1' if 'graphify-out/' not in s and any(e in s for e in exts) else '')\" 2>/dev/null || true); if [ \"$HIT\" = 1 ] && [ -f graphify-out/graph.json ]; then echo '{\"hookSpecificOutput\":{\"hookEventName\":\"PreToolUse\",\"additionalContext\":\"graphify: knowledge graph at graphify-out/. For codebase questions, run `graphify query \\\"<question>\\\"` (scoped subgraph, usually much smaller than reading files one by one), `graphify explain \\\"<concept>\\\"`, or `graphify path \\\"<A>\\\" \\\"<B>\\\"`, instead of reading source files to answer. Read raw files to modify or debug specific code, or when the graph lacks the detail.\"}}'; fi || true"
+          }
+        ]
+      }
     ]
   }
-}
+}

mobile_app/.claude/skills/graphify/.graphify_version

@@ -0,0 +1 @@
+0.8.31

mobile_app/.claude/skills/graphify/references/add-watch.md

@@ -0,0 +1,56 @@
+# graphify reference: add a URL and watch a folder
+
+Load this when the user ran `/graphify add <url>` or passed `--watch`. Neither is part of the default build.
+
+## For /graphify add
+
+Fetch a URL and add it to the corpus, then update the graph.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys
+from graphify.ingest import ingest
+from pathlib import Path
+
+try:
+    out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR')
+    print(f'Saved to {out}')
+except ValueError as e:
+    print(f'error: {e}', file=sys.stderr)
+    sys.exit(1)
+except RuntimeError as e:
+    print(f'error: {e}', file=sys.stderr)
+    sys.exit(1)
+"
+```
+
+Replace `URL` with the actual URL, `AUTHOR` with the user's name if provided, `CONTRIBUTOR` likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the `--update` pipeline on `./raw` to merge the new file into the existing graph.
+
+Supported URL types (auto-detected):
+- YouTube / any video URL → audio downloaded via yt-dlp, transcribed to `.txt` on next run (requires `pip install 'graphifyy[video]'`)
+- Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
+- arXiv → abstract + metadata saved as `.md`
+- PDF → downloaded as `.pdf`
+- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Any webpage → converted to markdown via html2text
+
+---
+
+## For --watch
+
+Start a background watcher that monitors a folder and auto-updates the graph when files change.
+
+```bash
+python3 -m graphify.watch INPUT_PATH --debounce 3
+```
+
+Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
+
+- **Code files only (.py, .ts, .go, etc.):** re-runs AST extraction + rebuild + cluster immediately, no LLM needed. `graph.json` and `GRAPH_REPORT.md` are updated automatically.
+- **Docs, papers, or images:** writes a `graphify-out/needs_update` flag and prints a notification to run `/graphify --update` (LLM semantic re-extraction required).
+
+Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file.
+
+Press Ctrl+C to stop.
+
+For agentic workflows: run `--watch` in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual `/graphify --update` after those waves.

mobile_app/.claude/skills/graphify/references/exports.md

@@ -0,0 +1,71 @@
+# graphify reference: extra exports and benchmark
+
+Load this when the user passed one of the export flags (`--wiki`, `--neo4j`, `--neo4j-push`, `--svg`, `--graphml`, `--mcp`), or when the corpus is large enough for the token-reduction benchmark. Each step runs only for its own flag.
+
+### Step 6b - Wiki (only if --wiki flag)
+
+**Only run this step if `--wiki` was explicitly given in the original command.**
+
+Run this before Step 9 (cleanup) so `.graphify_labels.json` is still available.
+
+```bash
+graphify export wiki
+```
+
+### Step 7 - Neo4j export (only if --neo4j or --neo4j-push flag)
+
+**If `--neo4j`** - generate a Cypher file for manual import:
+
+```bash
+graphify export neo4j
+```
+
+**If `--neo4j-push <uri>`** - push directly to a running Neo4j instance. Ask the user for credentials if not provided:
+
+```bash
+graphify export neo4j --push bolt://localhost:7687 --user neo4j --password PASSWORD
+```
+
+Default URI is `bolt://localhost:7687`, default user is `neo4j`. Uses MERGE - safe to re-run without creating duplicates.
+
+### Step 7b - SVG export (only if --svg flag)
+
+```bash
+graphify export svg
+```
+
+### Step 7c - GraphML export (only if --graphml flag)
+
+```bash
+graphify export graphml
+```
+
+### Step 7d - MCP server (only if --mcp flag)
+
+```bash
+python3 -m graphify.serve graphify-out/graph.json
+```
+
+This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
+
+To configure in Claude Desktop, add to `claude_desktop_config.json`:
+```json
+{
+  "mcpServers": {
+    "graphify": {
+      "command": "python3",
+      "args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
+    }
+  }
+}
+```
+
+### Step 8 - Token reduction benchmark (only if total_words > 5000)
+
+If `total_words` from `graphify-out/.graphify_detect.json` is greater than 5,000, run:
+
+```bash
+graphify benchmark
+```
+
+Print the output directly in chat. If `total_words <= 5000`, skip silently - the graph value is structural clarity, not token compression, for small corpora.

mobile_app/.claude/skills/graphify/references/extraction-spec.md

@@ -0,0 +1,68 @@
+# graphify reference: extraction subagent prompt
+
+Load this in Step 3 Part B when the corpus has at least one doc, paper, or image chunk. A pure-code corpus skips Part B and never reads this file. Each semantic subagent receives the prompt below verbatim (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE, and CHUNK_PATH).
+
+```
+You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment.
+Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble.
+
+Files (chunk CHUNK_NUM of TOTAL_CHUNKS):
+FILE_LIST
+
+Rules:
+- EXTRACTED: relationship explicit in source (import, call, citation, "see §3.2")
+- INFERRED: reasonable inference (shared data structure, implied dependency)
+- AMBIGUOUS: uncertain - flag for review, do not omit
+
+Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns).
+  Do not re-extract imports - AST already has those.
+Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). `file_type` MUST be one of exactly these six values: `code`, `document`, `paper`, `image`, `rationale`, `concept`. Any other value is invalid and will be rejected.
+Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. `calls` edges MUST stay within one language: a Python function cannot `calls` a JS/TS/Go/Rust/Java symbol and vice versa — cross-language call edges are phantom artifacts, never emit them.
+Image files: use vision to understand what the image IS - do not just OCR.
+  UI screenshot: layout patterns, design decisions, key elements, purpose.
+  Chart: metric, trend/insight, data source.
+  Tweet/post: claim as node, author, concepts mentioned.
+  Diagram: components and connections.
+  Research figure: what it demonstrates, method, result.
+  Handwritten/whiteboard: ideas and arrows, mark uncertain readings AMBIGUOUS.
+
+DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
+  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.
+
+Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples:
+- Two functions that both validate user input but never call each other
+- A class in code and a concept in a paper that describe the same algorithm
+- Two error types that handle the same failure mode differently
+Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.
+
+Hyperedges: if 3 or more nodes clearly participate together in a shared concept, flow, or pattern that is not captured by pairwise edges alone, add a hyperedge to a top-level `hyperedges` array. Examples:
+- All classes that implement a common protocol or interface
+- All functions in an authentication flow (even if they don't all call each other)
+- All concepts from a paper section that form one coherent idea
+Use sparingly — only when the group relationship adds information beyond the pairwise edges. Maximum 3 hyperedges per chunk.
+
+If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
+  contributor onto every node from that file.
+
+confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a default:
+- EXTRACTED edges: confidence_score = 1.0 always
+- INFERRED edges: pick exactly ONE value from this set — never 0.5:
+    0.95  direct structural evidence (shared data structure, named cross-file reference).
+    0.85  strong inference (clear functional alignment, no direct symbol link).
+    0.75  reasonable inference (shared problem domain + similar shape, requires interpretation).
+    0.65  weak inference (thematically related, no shape evidence).
+    0.55  speculative but plausible (surface-level co-occurrence only).
+  Models follow discrete rubrics better than continuous ranges; the bimodal
+  distribution observed in production (>50% at 0.5, >40% at 0.85+) shows the
+  range guidance is being collapsed to a binary. If no value above fits, mark
+  the edge AMBIGUOUS rather than picking 0.4 or below.
+- AMBIGUOUS edges: 0.1-0.3
+
+Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the **immediate** parent directory name + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent is used — not the full path. Examples: `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or the full path (e.g., `src_auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project that had ghost duplicates under the old format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
+
+Generate the extraction JSON matching this schema exactly:
+{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"relative/path"}],"input_tokens":0,"output_tokens":0}
+
+Then write the JSON to disk using the Write tool at this exact absolute path (no relative paths — Write resolves relative paths against an undefined cwd and the file will be silently lost):
+CHUNK_PATH
+```

mobile_app/.claude/skills/graphify/references/github-and-merge.md

@@ -0,0 +1,46 @@
+# graphify reference: GitHub clone and cross-repo merge
+
+Load this when the user passed one or more `https://github.com/...` URLs, or named several local subfolders to merge into one graph.
+
+### Step 0 - Clone GitHub repo(s) (only if a GitHub URL was given)
+
+**Single repo:**
+```bash
+LOCAL_PATH=$(graphify clone <github-url> [--branch <branch>])
+# Use LOCAL_PATH as the target for all subsequent steps
+```
+
+**Multiple repos (cross-repo graph):**
+```bash
+# Clone each repo, run the full pipeline on each, then merge
+graphify clone <url1>   # → ~/.graphify/repos/<owner1>/<repo1>
+graphify clone <url2>   # → ~/.graphify/repos/<owner2>/<repo2>
+# Run /graphify on each local path to produce their graph.json files
+# Then merge:
+graphify merge-graphs \
+  ~/.graphify/repos/<owner1>/<repo1>/graphify-out/graph.json \
+  ~/.graphify/repos/<owner2>/<repo2>/graphify-out/graph.json \
+  --out graphify-out/cross-repo-graph.json
+```
+
+Graphify clones into `~/.graphify/repos/<owner>/<repo>` and reuses existing clones on repeat runs. Each node in the merged graph carries a `repo` attribute so you can filter by origin.
+
+**Multiple local subfolders (monorepo or multi-service layout):**
+
+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/     # → ./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 \
+  ./core/graphify-out/graph.json \
+  ./service/graphify-out/graph.json \
+  ./platform/graphify-out/graph.json \
+  --out graphify-out/graph.json
+```
+
+Once `graphify-out/graph.json` exists, the fast path above takes over: any codebase question runs `graphify query` directly on the merged graph — no re-extraction, no size gate.

mobile_app/.claude/skills/graphify/references/hooks.md

@@ -0,0 +1,33 @@
+# graphify reference: commit hook and native CLAUDE.md integration
+
+Load this when the user asked to install the post-commit hook or wire graphify into a project's CLAUDE.md.
+
+## For git commit hook
+
+Install a post-commit hook that auto-rebuilds the graph after every commit. No background process needed - triggers once per commit, works with any editor.
+
+```bash
+graphify hook install    # install
+graphify hook uninstall  # remove
+graphify hook status     # check
+```
+
+After every `git commit`, the hook detects which code files changed (via `git diff HEAD~1`), re-runs AST extraction on those files, and rebuilds `graph.json` and `GRAPH_REPORT.md`. Doc/image changes are ignored by the hook - run `/graphify --update` manually for those.
+
+If a post-commit hook already exists, graphify appends to it rather than replacing it.
+
+---
+
+## For native CLAUDE.md integration
+
+Run once per project to make graphify always-on in Claude Code sessions:
+
+```bash
+graphify claude install
+```
+
+This writes a `## graphify` section to the local `CLAUDE.md` that instructs Claude to check the graph before answering codebase questions and rebuild it after code changes. No manual `/graphify` needed in future sessions.
+
+```bash
+graphify claude uninstall  # remove the section
+```