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Ontoly Software Graph

OrganizationPopular
hashgraph-online
ontoly-software-graph

Use Ontoly Software Graph evidence, CLI reports, MCP, and official Ontoly Agent Skills before searching source files when reviewing architecture, dependencies, request flows, configuration, impact, or security-sensitive ownership.

Overview

Publisherhashgraph-online
Repositoryawesome-codex-plugins
Skill nameontoly-software-graph
Stars
1.3K
Forks
360
Bundled files
Instructions only
LicenseApache-2.0
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 hashgraph-online 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.
    https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/0xsarwagya/ontoly-codex-plugin/skills/ontoly-software-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/hashgraph-online/awesome-codex-plugins.git /tmp/awesome-codex-plugins
mkdir -p .claude/skills
cp -r /tmp/awesome-codex-plugins/plugins/0xsarwagya/ontoly-codex-plugin/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 the user asks Codex to understand a TypeScript repository's architecture, dependency graph, request flow, service ownership, configuration usage, impact radius, package topology, or security-sensitive code paths.

Ontoly is a deterministic Software Graph compiler. It does not answer questions with AI. It builds graph evidence that Codex can query before falling back to source search.

Workflow

  1. Check whether .ontoly/SoftwareGraph.json exists.

  2. If the graph is missing or stale, ask before installing dependencies or writing generated graph files. Then run:

    bash
    pnpm add -D @0xsarwagya/ontoly-cli
    pnpm ontoly build .
  3. Inspect graph quality before answering:

    bash
    pnpm ontoly coverage .
    pnpm ontoly stats .
  4. Prefer Ontoly graph queries before repository-wide source search:

    bash
    pnpm ontoly architecture --json
    pnpm ontoly report dependencies --format markdown
    pnpm ontoly report routes --format markdown
    pnpm ontoly query impact <node-id>
    pnpm ontoly trace <node-id-or-name>
  5. Start Ontoly MCP when the environment supports MCP-backed tools:

    bash
    pnpm ontoly mcp
  6. Use source files only when the graph is missing, stale, low confidence, or insufficient for the question.

Common Questions

  • "Explain this repository."
  • "Which service owns authentication?"
  • "Trace the login flow."
  • "What breaks if I remove this repository/service/function?"
  • "Which packages depend on this module?"
  • "Where is this environment variable read?"
  • "Which routes are protected by auth?"

Evidence Rules

When answering, cite graph evidence:

  • graph hash
  • node ids
  • relationship types
  • source spans when available
  • diagnostics or confidence warnings
  • fallback reason if source files were inspected

Never claim certainty beyond Ontoly evidence. If graph coverage is incomplete, say exactly which part is inferred or unresolved.

Official Ontoly Skills

Ontoly publishes task-specific Agent Skills in its own repository. To inspect or install them with the open skills CLI:

bash
npx skills add 0xsarwagya/ontoly --list
npx skills add 0xsarwagya/ontoly --skill architecture-review
npx skills add 0xsarwagya/ontoly --skill impact-analysis
npx skills add 0xsarwagya/ontoly --skill request-tracing

Use those official skills when the task maps directly to one of their workflows.

Frequently asked questions

What does the Ontoly Software Graph AI skill do?

Use Ontoly Software Graph evidence, CLI reports, MCP, and official Ontoly Agent Skills before searching source files when reviewing architecture, dependencies, request flows, configuration, impact, or security-sensitive ownership.

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/hashgraph-online/awesome-codex-plugins/tree/main/plugins/0xsarwagya/ontoly-codex-plugin/skills/ontoly-software-graph. TypingMind reads its SKILL.md 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?

Yes. It is published on GitHub by hashgraph-online under the Apache-2.0 license. 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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