Imported from alibaba/neug (skills/codegraph/SKILL.md). Install upstream with npx skills add alibaba/neug --skill codegraph. Copyright stays with the author.

name: codegraph description: Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a .codegraph index exists in the workspace, or when the user wants to create one.

CodeScope Q&A

CodeScope indexes source code into a two-layer knowledge graph — structure (functions, calls, imports, classes, modules) and evolution (commits, file changes, function modifications) — plus semantic embeddings for every function. Supports Python, JavaScript/TypeScript, C, and Java (including Hadoop-scale repositories with 8K+ files). This combination enables analyses that grep, LSP, or pure vector search cannot do alone. It can also fetch GitHub issues and trace bugs to code, and review open PRs — scoring per-PR risk, detecting cross-PR conflicts, identifying auto-merge candidates, and applying GitHub labels.

When to Use This Skill

• User asks about call chains, callers, callees, or dependencies
• User wants to find dead code, hotspots, or architectural layers
• User asks about code history, who changed what, or why something was modified
• User wants to find semantically similar functions across a codebase
• User wants a full architecture analysis or report
• User asks about module coupling, circular dependencies, or bridge functions
• User wants to index or analyze a Java project (Maven, Gradle, plain Java)
• User wants to analyze GitHub issues or bug reports to find root causes
• User asks "why does this project have so many bugs" or "what code is most buggy"
• User wants to trace a bug report to the most relevant code locations
• User asks about class relationships, ownership, composition, or wants a class diagram / UML
• User wants to understand which classes own or depend on other classes
• User wants to review PRs, assess PR risk, or prioritize PR reviews
• User asks about cross-PR conflicts or which PRs can be merged independently
• User wants to find auto-merge candidates or generate a PR review report
• User asks about the blast radius or impact scope of a PR
• User wants to apply labels to PRs from analysis results
• User wants to explore PR-specific follow-up questions for a given PR
• A .codegraph directory (or similar index) exists in the workspace

Getting Started

Installation

pip install codegraph-ai

Environment Variables (optional)

# Create Python virtural environment
python -m venv .venv

source .venv/bin/activate

# Point to a pre-built database (skip indexing)
export CODESCOPE_DB_DIR="/path/to/.linux_db"

# Offline mode for HuggingFace models
export HF_HUB_OFFLINE="1"

Tip: If the all-MiniLM-L6-v2 model fails to download (network issues, firewall, etc.):

• HuggingFace mirror: export HF_ENDPOINT="https://hf-mirror.com" then retry download
• ModelScope (China-friendly): download from https://www.modelscope.cn/models/sentence-transformers/all-MiniLM-L6-v2 to a local path, then load by path instead of model name:

  pip install modelscope
  modelscope download --model sentence-transformers/all-MiniLM-L6-v2 --local_dir /path/to/all-MiniLM-L6-v2
  # Then use the local path in Python API:
  # cs = CodeScope(db_dir, embedding_model="/path/to/all-MiniLM-L6-v2")

Check Index Status

codegraph status --db $CODESCOPE_DB_DIR

If no index exists, create one:

codegraph init --repo . --lang auto --commits 500

Supported languages: python, c, javascript, typescript, java, or auto (auto-detects from file extensions).

The --commits flag ingests git history (for evolution queries). Without it, only structural analysis is available. Add --backfill-limit 200 to also compute function-level MODIFIES edges (slower but enables change_attribution and co_change).

To add git history to an existing index (without re-indexing structure):

codegraph ingest --repo . --db $CODESCOPE_DB_DIR --commits 500
codegraph ingest --repo . --db $CODESCOPE_DB_DIR --backfill-limit 200   # add MODIFIES edges only

Two Interfaces: CLI vs Python

Use the CLI for status and reports:

codegraph status --db $CODESCOPE_DB_DIR
codegraph analyze --db $CODESCOPE_DB_DIR --output report.md

Use the Python API for queries and custom analyses:

import os
os.environ['HF_HUB_OFFLINE'] = '1'  # required

from codegraph.core import CodeScope
cs = CodeScope(os.environ['CODESCOPE_DB_DIR'])

# Cypher query
rows = list(cs.conn.execute('''
    MATCH (caller:Function)-[:CALLS]->(f:Function {name: "free_irq"})
    RETURN caller.name, caller.file_path LIMIT 10
'''))
for r in rows:
    print(r)

cs.close()  # always close when done

The Python API is more powerful — it gives you raw Cypher access and lets you chain queries.

Core Python API

Raw Queries

These are the building blocks for any custom analysis:

Structural Analysis

Class Dependency Relationships (UML-Style)

CodeScope extracts three UML relationship types from class fields and type annotations during indexing:

# Get all composition relationships (A strongly owns B)
list(cs.conn.execute('MATCH (c1:Class)-[:COMPOSES]->(c2:Class) RETURN c1.name, c2.name'))

# Get all aggregation relationships (A optionally holds B)
list(cs.conn.execute('MATCH (c1:Class)-[:AGGREGATES]->(c2:Class) RETURN c1.name, c2.name'))

# How many objects does a class directly own?
list(cs.conn.execute(
    'MATCH (c:Class {name: "Llama"})-[:COMPOSES]->(t:Class) RETURN t.name'
))

# Full dependency graph for a class (composition + aggregation + inheritance)
list(cs.conn.execute(
    'MATCH (c:Class {name: "GPUModelRunner"})-[r:COMPOSES|AGGREGATES]->(t:Class) '
    'RETURN type(r), t.name'
))

Generating a Mermaid class diagram:

inherits  = list(cs.conn.execute('MATCH (c1:Class)-[:INHERITS]->(c2:Class) RETURN c1.name, c2.name'))
composes  = list(cs.conn.execute('MATCH (c1:Class)-[:COMPOSES]->(c2:Class) RETURN c1.name, c2.name'))
aggregates = list(cs.conn.execute('MATCH (c1:Class)-[:AGGREGATES]->(c2:Class) RETURN c1.name, c2.name'))

print('classDiagram')
for src, tgt in inherits:   print(f'    {tgt} <|-- {src}')   # parent <|-- child
for src, tgt in composes:   print(f'    {src} *-- {tgt}')    # owner *-- owned
for src, tgt in aggregates: print(f'    {src} o-- {tgt}')    # holder o-- optional

Scale reference:

Semantic Search

cross_locate return value — a CrossLocateResult dataclass (not iterable directly):

result = cs.cross_locate("memory allocation error handling", topk=10)

# result.seeds: list[dict] — semantically matched functions
for seed in result.seeds:
    print(f"  [{seed['score']:.3f}] {seed['name']} ({seed['file_path']})")

# result.connections: list[dict] — call-chain links between seeds
for conn in result.connections:
    print(f"  {conn['from']} -> {conn['to']} (distance={conn['distance']}, via={conn['via']})")

# result.clusters: list[list[str]] — connected groups of seed function IDs
for cluster in result.clusters:
    print(f"  Cluster: {cluster}")

Evolution (requires --commits during init)

Report Generation

from codegraph.analyzer import generate_report
report = generate_report(cs)  # full architecture analysis as markdown

Or via CLI:

codegraph analyze --output reports/analysis.md

The report covers: overview stats, subsystem distribution, top modules, architectural layers (with Mermaid diagrams), bridge functions, fan-in/fan-out hotspots, cross-module coupling, evolution hotspots, and dead code density.

Java Support

CodeScope includes a full Java adapter that handles enterprise-scale repositories like Apache Hadoop (~8K files, ~97K functions indexed in ~3.5 minutes).

What Gets Indexed

Indexing a Java Project

codegraph init --repo /path/to/java-project --lang java --commits 500

Or with auto-detection (auto-detects .java files):

codegraph init --repo /path/to/java-project --lang auto

Java-Specific Exclusions

By default, these directories are excluded when indexing Java projects: target/, build/, .gradle/, .idea/, .settings/, bin/, out/, test/, tests/, src/test/.

Java Query Examples

# Find all classes that extend a specific class
list(cs.conn.execute("""
    MATCH (c:Class)-[:INHERITS]->(p:Class {name: 'FileSystem'})
    RETURN c.name, c.file_path
"""))

# Find all methods in a specific class
list(cs.conn.execute("""
    MATCH (c:Class {name: 'DefaultParser'})-[:HAS_METHOD]->(f:Function)
    RETURN f.name, f.signature
"""))

# Find constructor call chains
list(cs.conn.execute("""
    MATCH (f:Function)-[:CALLS]->(init:Function {name: '<init>'})
    WHERE init.class_name = 'Configuration'
    RETURN f.name, f.file_path LIMIT 10
"""))

Bug Root Cause Analysis

CodeScope can fetch GitHub issues and map them to code using the graph + vector infrastructure. This is the core workflow for answering questions like "why does this project have so many bugs?" or "where in the code does this bug come from?"

Prerequisites

• A code graph must already be indexed for the target repository
• gh CLI must be installed and authenticated (gh auth login)

Bug Analysis API

Single Issue Analysis

# Analyze a specific GitHub issue against the indexed code graph
result = cs.analyze_issue("owner", "repo", 1234, topk=10)
print(result.format_report())

This:

1. Fetches the issue from GitHub (or loads from cache)
2. Parses file paths, function names, and stack traces from the issue body
3. Matches extracted paths to File nodes in the graph
4. Uses semantic search (cross_locate) to find related code
5. Traces callers of mentioned functions via impact()
6. Ranks and returns root cause candidates with explanation

Batch Bug Analysis

# Analyze top-k bug issues and get aggregated hotspot data
results = cs.analyze_top_bugs("owner", "repo", k=10, label="bug")
for r in results:
    print(f"#{r.issue.number}: {r.issue.title}")
    for c in r.candidates[:3]:
        print(f"  {c.function_name} ({c.file_path}) score={c.score:.2f}")

CLI Commands

# Fetch and parse a single issue (no graph needed)
codegraph fetch-issue owner repo 1234

# Fetch top-k bugs from a repo
codegraph fetch-bugs owner repo --top 10 --label bug

# Analyze a single bug against the code graph
codegraph analyze-bug owner repo 1234 --db .codegraph --topk 10

# Batch analyze top bugs
codegraph analyze-bugs owner repo --db .codegraph --top 10 --label bug

Lower-Level Components

For custom analysis pipelines, the components can be used individually:

from codegraph.issue_fetcher import fetch_and_parse_issue
from codegraph.bug_locator import (
    resolve_paths_to_files,
    find_semantic_matches,
    trace_callers,
    rank_root_causes,
    analyze_bug,
)

# Fetch and parse (with caching)
issue = fetch_and_parse_issue("owner", "repo", 1234)
print(issue.extracted_paths)   # file paths found in body
print(issue.extracted_funcs)   # function names from stack traces
print(issue.linked_commits)    # merge commit SHAs from linked PRs

# Match paths to graph nodes
path_matches = resolve_paths_to_files(cs, issue.extracted_paths)

# Semantic search using issue description
semantic_matches = find_semantic_matches(cs, f"{issue.title}\n{issue.body}")

# Trace callers of mentioned functions
caller_traces = trace_callers(cs, issue.extracted_funcs, max_hops=2)

# Combine into ranked candidates
candidates = rank_root_causes(path_matches, semantic_matches, caller_traces, issue.extracted_funcs)

Scoring System

Root cause candidates are scored by combining multiple signals:

Issue Cache

Parsed issues are cached at ~/.codegraph/issue_cache/{owner}_{repo}_{number}.json. Cache hits skip the GitHub API call entirely (sub-millisecond). To force a refresh, pass use_cache=False or use --no-cache on CLI.

from codegraph.issue_cache import clear_cache
clear_cache(owner="openclaw", repo="openclaw")  # clear specific repo
clear_cache()  # clear all

Stack Trace Parsing

The parser automatically extracts file paths and function names from stack traces in Python, C/C++, JavaScript/Node.js, Go, and Rust formats. It also extracts func_name() references in backticks and inline code.

PR Review and Analysis

CodeScope can analyze open PRs against the indexed code graph to compute structural risk scores, detect cross-PR conflicts, and generate prioritized review reports.

Prerequisites

• A code graph must already be indexed for the target repository
• gh CLI must be installed and authenticated (gh auth login)
• GITHUB_TOKEN environment variable recommended to avoid rate limiting

Unified Pipeline (CLI)

Two subcommands: prepare (analyze + write to DB) and label (apply GitHub labels + comments).

# Phase 1: Analyze PRs, detect conflicts, write to graph DB (full rebuild)
# Pipeline: cross-PR analysis → single-PR risk scoring → report + labels
codegraph pr-review prepare --db .codegraph

# Filter by author during prepare:
codegraph pr-review prepare --db .codegraph --author someone

# Override auto-detected GitHub repo (owner/repo):
codegraph pr-review prepare --db .codegraph --repo owner/repo

# Skip per-PR risk scoring (conflict-only, faster):
codegraph pr-review prepare --db .codegraph --skip-single-pr

# Phase 2: Apply labels and post conflict comments from graph DB
codegraph pr-review label --db .codegraph

# Label with dry-run (preview without API calls):
codegraph pr-review label --db .codegraph --dry-run

Required arg: --db. Local repo path derived from --db parent. GitHub repo auto-detected from git remote get-url origin (or specified via --repo). Optional: --author, --output, --skip-single-pr (prepare); --dry-run (label).

Python API (for agents / scripts)

For programmatic use within the same Python process, use PRReview — a high-level wrapper that manages CodeScope lifecycle automatically.

from codegraph.pr_api import PRReview

# Full pipeline in 2 lines
with PRReview(db=".codegraph") as pr:
    pr.prepare()                # fetch PRs → graph DB → scoring → report
    pr.label(dry_run=True)      # preview labels without API calls

# Query after prepare (works across sessions once DB has data)
with PRReview(db=".codegraph") as pr:
    # Conflicts
    pr.conflict_prs_of("100")           # → ["101", "102"]

    # Risk
    pr.risk("100")                      # → {"number": "100", "risk_level": "HIGH", ...}

    # Classification
    pr.auto_merge_candidates()          # → [{"number": "200", ...}, ...]
    pr.conflicting_groups()             # → [["100", "101"], ["103"]]

    # All PRs in DB
    pr.all_prs()                        # → [{"number": "100", ...}, ...]

    # Functions changed by a specific PR (added / modified / deleted)
    import json
    cs = pr._open_cs()
    rows = list(cs.conn.execute(
        f"MATCH (pr:PR {{id: {json.dumps('439')}}})-[c:CHANGES]->(f:Function) "
        f"RETURN c.info AS change_type, f.name, f.file_path "
        f"ORDER BY c.info, f.name"
    ))
    for change_type, name, path in rows:
        print(f"  [{change_type}] {name} ({path})")
    # change_type: 'hunk' (modified), 'new' (added), 'deleted', 'related' (newly calls)

All query methods return structured Python objects — no text parsing required. The CLI and Python API share the same underlying implementation (run_prepare / run_label / graph DB), so you can prepare via CLI and query via Python, or vice versa.

For lower-level components (PRScorer, CrossPRAnalyzer, etc.), see:

from codegraph.pr_analysis import GitHubClient, GraphAnalyzer, PRScorer, CrossPRAnalyzer
gh = GitHubClient(repo='owner/repo')
scorer = PRScorer(GraphAnalyzer(cs, repo_dir), repo_dir, gh)
result = scorer.analyze(gh.pr_to_entry(pr), output_dir='/tmp')  # risk_score, risk_level, peak_blast...

cross = CrossPRAnalyzer(cs, repo_dir, gh)
cross.prepare(pr_ids)  # index PR nodes into graph
cross.connected_components()  # {root: [pr_ids]} — detects conflicts
cross.update_pr_labels(assignments)  # persist labels to graph DB

# Load PR results from graph DB (no GitHub API needed)
all_results, components = cross.load_from_graph()

# Build and apply labels from analysis results
from codegraph.pr_labeler import build_label_assignments, apply_labels
assignments = build_label_assignments(all_results, components)
apply_labels(assignments, repo='owner/repo', create_labels=True)

For detailed workflows, Cypher patterns, and CrossPRAnalyzer query dimensions, see pr-analysis.md.

Report Structure (3 sections)

1. Auto-merge Candidates: LOW risk, no interface/config changes, singleton component
2. Independent Review: Non-trivial PRs with no cross-PR conflict
3. Conflicting PR Groups: PRs sharing code/call paths via connected-components (DSU)

Risk levels: CRITICAL (≥12), HIGH (≥7), MEDIUM (≥3), LOW (<3), UNKNOWN (when --skip-single-pr). Key signals: blast_radius (3.0×), no_test_coverage (2.0×), interface_change (2.5×), dead_code (1.5×).

Applying Labels and Conflict Comments

After running codegraph pr-review prepare, run codegraph pr-review label to apply category labels to GitHub PRs and post conflict comments:

# Apply labels and post conflict comments:
codegraph pr-review label --db .codegraph

# Preview without making API calls:
codegraph pr-review label --db .codegraph --dry-run

The label subcommand reads PR labels from the graph DB (pr.label column) — no re-analysis needed. For conflicting PRs (labelled conflicting-group-N), it also posts a comment on the GitHub PR listing shared functions and other conflicting PRs.

Labels are computed during prepare from the analysis results (connected components + risk scores) and persisted to PR nodes in the graph DB (pr.label column, semicolon-delimited).

Label scheme:

Follow-up Exploration

PR-specific follow-up questions are automatically included in codegraph explore when PR nodes exist in the graph DB (i.e., after codegraph pr-review prepare). PR exploration is a question template set integrated into explore. To query a specific PR's details (conflicts, changed functions), use the PRReview Python API.

# After pr-review prepare, explore includes PR questions automatically:
codegraph explore --db .codegraph --top 15

# Interactive exploration (including PR follow-up questions):
codegraph explore --db .codegraph

# Focus on PR-specific questions (use reviewer role):
codegraph explore --db .codegraph --role reviewer

# Filter to only architecture questions (exclude PR patterns):
codegraph explore --db .codegraph --type architecture

# Filter to only risk questions:
codegraph explore --db .codegraph --type risk

# Filter to only PR review questions:
codegraph explore --db .codegraph --type pr-review --role reviewer

The --type filter controls which question categories appear:

• all (default): all categories mixed together
• architecture: structural design questions (fan-in, coupling, cycles)
• risk: risk-focused questions (structural risk + PR risk)
• evolution: git history questions (change attribution, modification patterns)
• hotspot: frequently modified code questions
• pr-review: PR-specific questions (impact, conflicts, test coverage)

When --type pr-review is specified, only PR-related questions are shown.

How to Route Questions

The key decision is: does the user want an exact structural answer, a fuzzy semantic one, or a bug-to-code mapping? Every entry below is tagged with the interface layer(s) that support it: CLI, Python API, Cypher, or MCP.

Function Discovery & Location

Call Graph Navigation (Callers & Callees)

Note: cs.impact() is caller-only and ranks by semantic relevance. For pure structural caller/callee queries without ranking, use the Cypher patterns above.

Class & Object Navigation

Semantic & Similarity Search

Code Quality & Risk Signals

Evolution & Change History

Bug & Issue Analysis

PR Review & Analysis

Architecture & Reports

Indexing & Setup

For novel investigations not covered by pre-built methods, compose raw Cypher queries. See patterns.md for templates. For bug analysis patterns, see bug-analysis.md. For PR analysis patterns, see pr-analysis.md.

Cypher Cheat-Sheet

These patterns cover 90% of structural navigation needs:

-- 1. Function location + metadata
MATCH (f:Function {name: 'func_name'})
WHERE f.is_historical = 0
RETURN f.file_path, f.start_line, f.end_line, f.signature, f.doc_comment
LIMIT 5

-- 2. Direct callers
MATCH (caller:Function)-[:CALLS]->(f:Function {name: 'func_name'})
WHERE f.is_historical = 0
RETURN caller.name, caller.file_path
LIMIT 50

-- 3. Direct callees
MATCH (f:Function {name: 'func_name'})-[:CALLS]->(callee:Function)
WHERE f.is_historical = 0
RETURN callee.name, callee.file_path
LIMIT 50

-- 4. Transitive callers (2 hops)
MATCH (caller:Function)-[:CALLS*1..2]->(f:Function {name: 'func_name'})
WHERE f.is_historical = 0
RETURN DISTINCT caller.name, caller.file_path
LIMIT 50

-- 5. Module membership
MATCH (f:Function {name: 'func_name'})<-[:DEFINES_FUNC]-(file:File)-[:BELONGS_TO]->(m:Module)
RETURN m.path_prefix
LIMIT 1

-- 6. Class methods
MATCH (c:Class {name: 'ClassName'})-[:HAS_METHOD]->(f:Function)
RETURN f.name, f.signature, f.file_path, f.start_line

-- 7. Fan-in / fan-out for a specific function
MATCH (caller:Function)-[:CALLS]->(f:Function {name: 'func_name'})-[:CALLS]->(callee:Function)
WHERE f.is_historical = 0
RETURN count(DISTINCT caller) AS fan_in, count(DISTINCT callee) AS fan_out

-- 8. Find functions by semantic + structural combo
-- Step A: vector search for seed functions
-- Step B: graph query to find which seeds call each other

Important Filters for Cypher

When writing Cypher queries, these filters prevent misleading results:

• f.is_historical = 0 — exclude deleted/renamed functions that are still in the graph as historical records
• f.is_external = 0 (on File nodes) — exclude system headers/library files
• c.version_tag = 'bf' — only backfilled commits have MODIFIES edges; non-backfilled commits only have TOUCHES (file-level) edges
• Always use LIMIT — large codebases can return hundreds of thousands of rows
• Use file_path disambiguation when function names are overloaded across files

When to Use Python API vs. Raw Cypher vs. MCP

Checking Data Availability

Before running evolution queries, check what's available:

# How many commits are indexed?
list(cs.conn.execute("MATCH (c:Commit) RETURN count(c)"))

# How many have MODIFIES edges (backfilled)?
list(cs.conn.execute("MATCH (c:Commit) WHERE c.version_tag = 'bf' RETURN count(c)"))

If no commits exist, evolution methods will return empty results — guide the user to run codegraph ingest first. If commits exist but aren't backfilled, TOUCHES (file-level) queries still work but MODIFIES (function-level) queries won't.

Troubleshooting

The CLI auto-cleans lock issues on startup when possible.

References

• schema.md — Full graph schema: node types, edge types, properties, Cypher syntax notes
• patterns.md — Ready-to-use Cypher query templates and composition strategies
• bug-analysis.md — Bug analysis workflows: single issue, batch analysis, hotspot aggregation, custom pipelines
• pr-analysis.md — PR analysis workflows: per-PR scoring, cross-PR conflict detection, Cypher patterns, CrossPRAnalyzer usage