Agent skill
build-graph
Build or update the code review knowledge graph. Run this first to initialize, or let hooks keep it updated automatically.
Install this agent skill to your Project
npx add-skill https://github.com/tirth8205/code-review-graph/tree/main/skills/build-graph
SKILL.md
Build Graph
Build or incrementally update the persistent code knowledge graph for this repository.
Steps
-
Check graph status by calling the
list_graph_stats_toolMCP tool.- If the graph has never been built (last_updated is null), proceed with a full build.
- If the graph exists, proceed with an incremental update.
-
Build the graph by calling the
build_or_update_graph_toolMCP tool:- For first-time setup:
build_or_update_graph_tool(full_rebuild=True) - For updates:
build_or_update_graph_tool()(incremental by default)
- For first-time setup:
-
Verify by calling
list_graph_stats_toolagain and report the results:- Number of files parsed
- Number of nodes and edges created
- Languages detected
- Any errors encountered
When to Use
- First time setting up the graph for a repository
- After major refactoring or branch switches
- If the graph seems stale or out of sync
- The graph auto-updates via hooks on edit/commit, so manual builds are rarely needed
Notes
- The graph is stored as a SQLite database (
.code-review-graph/graph.db) in the repo root - Binary files, generated files, and patterns in
.code-review-graphignoreare skipped - Supported languages: Python, TypeScript/JavaScript, Vue, Go, Rust, Java, Scala, C#, Ruby, Kotlin, Swift, PHP, Solidity, C/C++
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
review-pr
Review a PR or branch diff using the knowledge graph for full structural context. Outputs a structured review with blast-radius analysis.
review-delta
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
Didn't find tool you were looking for?