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Yeachan-Heo
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Deterministic orchestration graph runtime - declarative DAG pipelines with journal-based crash recovery

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill namegraph
Stars
39.2K
Forks
3.5K
Bundled files
Instructions only
LicenseMIT
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 Yeachan-Heo 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.
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/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 Skill

Run a deterministic orchestration graph from a declarative JSON descriptor. The runtime consumes the sealed-descriptor and pure-scheduler contracts in src/graph/* and executes through an independent OS process (omc graph run), so crash recovery (kill mid-run, rerun, resume from journal) works for real.

Usage

/oh-my-claudecode:graph <descriptor.json>
/oh-my-claudecode:graph "build then test then ask me before deploy"   (author the descriptor first)

The execution surface is always the CLI subcommand:

omc graph run <descriptor.json> [--runs-root <dir>]

Run it via the Bash tool for non-interactive graphs. Progress lines stream as [run], [node], [ok], [fail], [join], [done].

When To Use

  • Repeatable multi-step pipelines with explicit dependencies (DAG)
  • Work that must survive interruption: kill/restart resumes from journal
  • Auditable runs: OCC journal + projection snapshots under .omc/graph-runs/<run_id>/

When NOT to use: exploratory one-off work (use conversation or /team); anything needing adaptive re-planning mid-run (graphs are deterministic).

Workflow

  1. Descriptor given -> go to step 3.

  2. Pipeline described -> author the descriptor JSON (schema below), write it next to the project (suggest .omc/graphs/<name>.json) and show it to the user before running. run_id must be unique per logical pipeline; rerunning with the same run_id RESUMES, not restarts.

  3. Approval nodes: if the descriptor contains any "kind": "human-approval" node, do NOT run it through the Bash tool (stdin is not interactive there; EOF fails closed to denied). Tell the user to run interactively instead:

    ! omc graph run <file>

    The ! prefix runs it inside this session with live stdin so y/n works.

  4. Run and relay progress. Exit codes (normative): 0 succeeded | 1 terminal failed | 19 another writer owns this run (busy) 20 corrupt/tampered journal (fail-closed) | 21 descriptor drift on resume | 70 runtime crash (unmapped error)

  5. Resume: rerunning the same command after a crash replays committed transitions and continues. Completed nodes never re-execute.

Descriptor Schema (minimal)

{ "descriptor_version": 1, "run_id": "unique-pipeline-id", "revision_id": "rev-1", "goal": "one line", "nodes": [ { "id": "n1", "kind": "command", "title": "...", "timeout_ms": 60000, "max_attempts": 2, "effect_policy": { "policy": "side_effect_free" }, "command": "npm test" }, { "id": "a1", "kind": "agent", "title": "...", "timeout_ms": 300000, "max_attempts": 1, "effect_policy": { "policy": "side_effect_free" }, "instructions": "..." }, { "id": "gate", "kind": "human-approval", "title": "...", "prompt": "Proceed?" } ], "edges": [ { "id": "e1", "kind": "fixed", "from": "n1", "to": "a1" } ], "entry_node_ids": ["n1"], "concurrency_limit": 2, "terminal_verification_node_id": "a1" }

Edge kinds: fixed | conditional | fan_out/join pairs | back_edge (bounded retries via max_traversals). See src/graph/schema.ts for the authoritative Zod schema — and read the Capability Boundary section above for what built-in executors actually execute today.

Capability Boundary & Semantics (read before authoring)

  • Edge support: built-in command/agent executors cover fixed edges and fan_out/join pairs. conditional and back_edge routes are fully supported by the runtime and scheduler contracts but require a custom NodeExecutor that emits route on its results — built-in executors never produce routes, so graphs relying on them fail fast with route_required rather than guessing.
  • Crash-recovery guarantee is at-least-once for command nodes: a crash between an external side effect and its journal append re-executes that node on resume. For idempotent commands, the resolved key is available to the command as GRAPH_IDEMPOTENCY_KEY before it starts and is also recorded for downstream dedupe. Built-in executors reject reconcile; reconciliation requires a custom executor with an actual external reconciliation authority. Exactly-once for external side effects is out of scope for v1.
  • Command trust boundary: command nodes are arbitrary shell lines with process authority in the current working directory. Only run descriptors you wrote or trust. Command children receive an allowlisted environment (PATH, HOME/USERPROFILE, TEMP/TMP, locale/timezone, USER identity, GRAPH_*, and the optional idempotency key), not the host's full secrets. Commands are not filesystem/process sandboxed.
  • Agent authority boundary: built-in agent nodes are explicitly read-only. They run in the current working directory with no additional directories, only Read, Glob, and Grep, permissionMode: dontAsk, session persistence disabled, and a provider-specific environment allowlist. Agent timeouts abort and interrupt the SDK query. Use a custom executor for any agent that needs mutation or external effects. Treat .omc/graph-runs/<run_id>/descriptor.json as executable content.

Frequently asked questions

What does the Graph AI skill do?

Deterministic orchestration graph runtime - declarative DAG pipelines with journal-based crash recovery

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/Yeachan-Heo/oh-my-claudecode/tree/main/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?

Yes. It is published on GitHub by Yeachan-Heo under the MIT 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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