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Graph

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xwtro0tk1t-cloud
graph

Manage code knowledge graphs via code-review-graph (CRG). Build, update, and check status of project code graphs stored in .code-review-graph/graph.db. Use when: (1) user says "build graph", "update graph", "graph status", "/graph", (2) Harness init detects CRG, (3) preparing to use /explore commands. Gracefully degrades if CRG is not installed.

Overview

Publisherxwtro0tk1t-cloud
Repositoryharness
Skill namegraph
Stars
264
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xwtro0tk1t-cloud on GitHub. Read the source before you install it.

Installation

Install the Graph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/xwtro0tk1t-cloud/harness/tree/main/bundled-skills/graph
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xwtro0tk1t-cloud/harness.git /tmp/harness
mkdir -p .claude/skills
cp -r /tmp/harness/bundled-skills/graph .claude/skills/graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Graph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Graph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Graph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Graph — Knowledge Graph Management

Overview

Manage project code knowledge graphs powered by code-review-graph. The graph stores code structure (functions, classes, calls, imports) in .code-review-graph/graph.db (SQLite) and enables architecture understanding, impact analysis, and caller/callee tracing.

Announce at start: "I'm using the graph skill to manage the project's code knowledge graph."

Commands

/graph build — Full Build

Build a complete code graph for the current project:

bash
# Check CRG is available first
python -c "import code_review_graph.tools" 2>/dev/null
if [ $? -ne 0 ]; then
    echo "CRG not installed. Run: pip install code-review-graph"
    exit 1
fi

# Full build
code-review-graph build --repo .

After build, report:

  • Graph location: .code-review-graph/graph.db
  • Node count: SELECT COUNT(*) FROM nodes
  • Edge count: SELECT COUNT(*) FROM edges
  • Community count: use list_communities_func from code_review_graph.tools

/graph update — Incremental Update

Update the graph with recent changes (faster than full build):

bash
code-review-graph update --repo .

/graph status — Graph Status

Check if a graph exists and show stats:

python
import os, sqlite3

db_path = ".code-review-graph/graph.db"
if not os.path.exists(db_path):
    print("No graph found. Run /graph build first.")
else:
    conn = sqlite3.connect(db_path)
    nodes = conn.execute("SELECT COUNT(*) FROM nodes").fetchone()[0]
    edges = conn.execute("SELECT COUNT(*) FROM edges").fetchone()[0]
    mtime = os.path.getmtime(db_path)
    from datetime import datetime
    last_updated = datetime.fromtimestamp(mtime).strftime("%Y-%m-%d %H:%M")
    print(f"Graph: {db_path}")
    print(f"Nodes: {nodes} | Edges: {edges} | Last updated: {last_updated}")
    conn.close()

Also run list_communities_func from code_review_graph.tools to show community count.

/graph view — Interactive Visualization in Browser

Open an interactive graph visualization in the browser:

bash
# Generate HTML + start local server (auto-opens http://localhost:8765)
code-review-graph visualize --repo . --serve

View modes:

bash
# Default: auto-detect best layout
code-review-graph visualize --repo . --serve

# Community/module view: nodes grouped by detected communities
code-review-graph visualize --repo . --mode community --serve

# File-level view: nodes are files, edges are cross-file dependencies
code-review-graph visualize --repo . --mode file --serve

# Full view: every function/class as a node
code-review-graph visualize --repo . --mode full --serve

Export (no server):

bash
# Generate HTML file only → open .code-review-graph/visualization.html manually
code-review-graph visualize --repo .

# Export as SVG image
code-review-graph visualize --repo . --format svg

# Export for Obsidian
code-review-graph visualize --repo . --format obsidian

# Export as GraphML (for yEd/Gephi)
code-review-graph visualize --repo . --format graphml

/graph wiki — Generate Markdown Wiki

bash
code-review-graph wiki --repo .
# Generates markdown docs from community structure

/graph install — Check/Install CRG

bash
python -c "import code_review_graph.tools; print('CRG is available')" 2>/dev/null \
  || echo "CRG not installed. Install with: pip install code-review-graph"

Graceful Degradation

  • CRG not installed → show install command, never crash
  • Detection method: python -c "import code_review_graph.tools" (matches graph_builder.py pattern)
  • Graph file missing → prompt /graph build

Storage

  • Graph: .code-review-graph/graph.db (CRG default, in workspace)
  • Metadata: .code-review-graph/ directory, managed by CRG automatically

Frequently asked questions

What does the Graph AI skill do?

Manage code knowledge graphs via code-review-graph (CRG). Build, update, and check status of project code graphs stored in .code-review-graph/graph.db. Use when: (1) user says "build graph", "update graph", "graph status", "/graph", (2) Harness init detects CRG, (3) preparing to use /explore commands. Gracefully degrades if CRG is not installed.

Why use Graph on TypingMind?

Because you install it once and use it with any model. Graph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xwtro0tk1t-cloud/harness/tree/main/bundled-skills/graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Graph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Graph?

As many as you like. As long as a model supports skills, you can use Graph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Graph AI skill free?

It is published on GitHub by xwtro0tk1t-cloud. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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