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Ontoly Software Graph

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anbeime
ontoly-software-graph

Use Ontoly's deterministic Software Graph and MCP capabilities for architecture review, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source-file search.

Overview

Publisheranbeime
Repositoryskill
Skill nameontoly-software-graph
Stars
6.9K
Forks
645
Bundled files
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  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by anbeime on GitHub. Read the source before you install it.

Installation

Install the Ontoly Software 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.
  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/anbeime/skill.git /tmp/skill
mkdir -p .claude/skills
cp -r /tmp/skill/skills/ontoly-software-graph .claude/skills/ontoly-software-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ontoly Software 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 Ontoly Software 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 Ontoly Software 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.

Ontoly Software Graph

Use this skill when a coding agent needs evidence-backed software understanding from an Ontoly graph before searching repository files directly.

When to Use

  • Explaining a repository architecture
  • Tracing a request, route, controller, service, or dependency path
  • Finding owners of services, modules, routes, configuration, or environment variables
  • Reviewing dependency impact before a refactor
  • Auditing dead code, cycles, unresolved imports, graph quality, or semantic coverage
  • Preparing documentation, onboarding notes, or architecture review from graph evidence

Required Workflow

  1. Check whether an Ontoly graph already exists by looking for .ontoly/, SoftwareGraph.json, diagnostics.json, validation reports, or an Ontoly MCP configuration.

  2. If no graph exists and the user permits local analysis, run:

    bash
    ontoly build .
  3. Inspect graph health before answering: diagnostics, graph hash, semantic coverage, trust or quality score, framework detection, and generation timestamp.

  4. Prefer Ontoly CLI or MCP capabilities for graph questions instead of scanning source files first.

  5. Use repository search only when Ontoly cannot answer, the graph is stale, the graph is incomplete, or the user explicitly asks for source-level verification.

  6. Always cite graph evidence in the answer: node IDs, edge types, file paths, source locations, diagnostics, or framework analyzer output.

  7. State confidence from graph evidence. Do not guess confidence.

Useful Ontoly Capabilities

  • ExplainArchitecture for repository and package topology
  • FindDependencies for dependency trees and direct consumers
  • ImpactAnalysis for refactor blast radius
  • TraceExecution for request, route, and call-flow tracing
  • FindConfigurationUsage for configuration and environment variable usage
  • FrameworkReport for detected framework concepts such as modules, controllers, providers, and routes
  • FindDeadCode for unreachable or unused graph regions

Answer Shape

When answering, include:

  • the direct answer
  • graph evidence
  • confidence
  • diagnostics or caveats
  • fallback source inspection only if needed

Example:

text
AuthController handles authentication.

Evidence:
- node: class:src/auth/auth.controller.ts:AuthController
- route edges: HANDLES POST /login, POST /logout
- dependency edges: USES AuthService, JwtService

Confidence: high, because the graph has controller, route, and dependency edges with source locations.

Fallback Rules

  • If the graph is missing, build it first when allowed.
  • If graph validation fails, report the failure and use source search only to verify the affected area.
  • If multiple nodes match the same name, ask for disambiguation or show the candidates with package/module context.
  • If the requested concept is not in the graph, return NOT_FOUND with the closest graph evidence instead of inventing an answer.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ontoly Software Graph AI skill do?

Use Ontoly's deterministic Software Graph and MCP capabilities for architecture review, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source-file search.

Why use Ontoly Software Graph on TypingMind?

Because you install it once and use it with any model. Ontoly Software 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 Ontoly Software Graph in TypingMind?

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

Which AI models can use Ontoly Software 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 Ontoly Software Graph?

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

Is the Ontoly Software Graph AI skill free?

It is published on GitHub by anbeime. 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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