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Graph Engineering

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bydtesla1609
graph-engineering

Design, refactor, execute, or audit complex workflows as typed work graphs with explicit roles, artifacts, handoff packets, validation gates, bounded feedback loops, and root-controller arbitration. Use when a multi-stage pipeline needs safe parallelism, restartability, evidence-backed completion, or separation between generic orchestration and domain rules. Do not use merely because domain data is stored as a graph, or for simple one-step tasks.

Overview

Publisherbydtesla1609
Repositorygraph-engineering
Skill namegraph-engineering
Stars
61
Forks
0
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

    Published by bydtesla1609 on GitHub. Read the source before you install it.

Installation

Install the Graph Engineering 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/bydtesla1609/graph-engineering/tree/codex/publish-graph-engineering
  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/bydtesla1609/graph-engineering.git \
  .claude/skills/graph-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Graph Engineering 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 Engineering 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 Engineering 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 Engineering

Model the work itself as a graph. Keep the domain knowledge graph, database graph, or dependency data separate from this orchestration graph.

Read references/graph-contract.md before defining or changing a graph specification. It contains both the stable unconditional v1 contract and the evidence-gated v2 conditional-routing contract. Read references/adapters.md when migrating an existing workflow or introducing a domain adapter.

Evolve the graph without bypassing it

Read references/evolution.md before importing a method from another skill, promoting an incident fix into the generic layer, or changing the graph contract. Treat external systems as evidence sources for graph evolution, not as authorities that can bypass the current validator, domain adapter, or root gate.

  1. Pin the evidence source and state its permitted and excluded access.
  2. Contrast matched contexts and express every candidate as condition, action, recovery, and verification.
  3. Admit the candidate as General, Scoped, or Exclude; place only General rules in the core and route Scoped rules to an adapter or example.
  4. Validate the change set with scripts/validate_graph_change.py <change-set.json> before compilation.
  5. Re-run graph validation, domain audits, compatibility tests, and rollback checks after compilation.

Use examples/skillalchemy-change-set.json as the bridge-format example. The example records the upstream version; it is not permission to execute or install upstream code.

Discover before redesigning

Identify the current entry point, commands, authoritative inputs, generated outputs, mutation boundaries, completion markers, audit reports, retry behavior, and recovery checkpoints. Preserve existing domain invariants until an explicit change is justified.

Draw the current execution graph before proposing a target graph. A node is a unit of work with one accountable role and declared read/write sets. An edge is a typed dependency and a deliberately limited handoff packet, not a vague statement that two steps are related.

Define the graph contract

Give every node a stable ID, kind, role, command or virtual-gate marker, read set, and write set. Give every edge a source, target, packet type, allowed fields, and any independence requirement. Designate exactly one virtual root gate owned by root_controller.

Use a directed acyclic graph for one execution pass. Represent iteration as an explicit feedback edge with a condition and finite max_iterations; never hide an unbounded retry inside a node. Make terminal outcomes typed and auditable, including bounded negative results when the domain permits them.

Use graph-engineering/v1 for fixed dependency graphs. Use graph-engineering/v2 only when an ordinary branch genuinely depends on a declared packet value; express the predicate structurally, declare accepted source statuses, and set an explicit join policy wherever multiple edges enter one node. Validate v2 selected-path evidence with scripts/validate_workflow_graph.py <spec.json> --run <run.json>.

Run scripts/validate_workflow_graph.py <spec.json> after editing any graph. Treat overlapping writes in one potential parallel wave, unreachable nodes, cycles, missing contracts, or fake independence as design errors. Conditional exclusivity does not by itself prove that overlapping writes are safe.

Separate orchestration from domain judgment

The generic layer owns topology, dependency scheduling, typed packets, write-conflict checks, checkpoint identity, failure propagation, and root-gate arbitration. A domain adapter owns commands, evidence standards, semantic validation, terminal states, and external-action permissions.

Do not replace a repository's authoritative importer, validator, or evidence rules with generic graph machinery. Wrap the existing implementation at stable seams and migrate one phase at a time.

Preserve real role boundaries

A role label is not evidence of independence. Use separate agents, processes, reviewers, or independently produced artifacts when the workflow actually requires independent judgment and the task authorizes that execution model. Otherwise run the graph with isolated packets and report that actor-level independence was not established.

Only pass fields declared on the incoming edge. Validators should consume persisted artifacts or bounded packets, not the builder's hidden reasoning. The root controller receives results, resolves conflicts, and decides whether the completion marker may be published; it does not silently rewrite specialist evidence.

Execute and close the loop

Schedule topological waves. Parallelize only nodes in the same wave whose declared writes do not overlap and whose runtime behavior is safe. Stop the graph on a failed hard gate and preserve its last successful checkpoint.

For every run, record the graph ID and version, node outcomes, artifact identities or hashes, failed checks, feedback decisions, and the terminal outcome. Completion means the root gate has verified the domain adapter's acceptance contract, not merely that all commands exited.

When refactoring an existing workflow, prove equivalence with focused tests and before/after hashes for protected products. Keep a rollback path until the new graph runner has passed the same domain audits as the former sequence.

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 Graph Engineering AI skill do?

Design, refactor, execute, or audit complex workflows as typed work graphs with explicit roles, artifacts, handoff packets, validation gates, bounded feedback loops, and root-controller arbitration. Use when a multi-stage pipeline needs safe parallelism, restartability, evidence-backed completion, or separation between generic orchestration and domain rules. Do not use merely because domain data is stored as a graph, or for simple one-step tasks.

Why use Graph Engineering on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bydtesla1609/graph-engineering/tree/codex/publish-graph-engineering. 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 Graph Engineering?

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 Engineering?

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

Is the Graph Engineering AI skill free?

It is published on GitHub by bydtesla1609. 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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