Apps Common Trees logo

Apps Common Trees

Organization
oracle
Apps Common Trees

Plan and execute controlled tree and Draft-version creation plus dependency-ordered node actions, labels and parameters for Oracle Fusion Trees.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameApps Common Trees
Stars
112
Forks
26
Bundled files
10
LicenseUPL-1.0
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Apps Common Trees 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/oracle/Fusion-Setup-Configuration-Registry/tree/main/core/fusion-api/apps-common/trees
  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/oracle/Fusion-Setup-Configuration-Registry.git /tmp/Fusion-Setup-Configuration-Registry
mkdir -p .claude/skills
cp -r /tmp/Fusion-Setup-Configuration-Registry/core/fusion-api/apps-common/trees .claude/skills/oracle-apps-common-trees
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apps Common Trees 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 Apps Common Trees 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 Apps Common Trees 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.

Apps Common Trees

Reviewer: Apps Common Trees owner and Central platform REST/security reviewers. Use through the implementation router for implementation requests. Authoring this package does not authorize any Fusion calls.

Purpose

Provide a controlled, project-persisted technical workflow for finding, validating and mutating Oracle Fusion Trees through the shared REST client. The workflow plans exact payloads, applies only established API contracts, and records validation, generated-ID and diagnostic evidence.

Load the applicable references

Mandatory behavior

  1. Resolve project, environment, request and available structure/source evidence. No project is required merely to author this skill. Runtime durable work uses the selected project or the repository's draft-project convention.
  2. Plan offline, then perform only the prerequisite reads declared in the workflow. For a tree create, supplied Data Source Parameters are required and must match an existing same-structure tree or receive a recorded no-tree assumed-correct result. Before any mutation, validate each supplied source member value through the relevant existing domain resolver selected from the tree structure and data-source evidence. Do not use fndTreeNodes to prove a proposed member exists. Missing structure metadata or an applicable resolver blocks dependent work; do not ask for advance privilege confirmation—Fusion enforces access when the request runs.
  3. POST fndTrees creates only the tree, its Draft FndTreeVersionVO and the validated FndTreeDataSourceParamsVO collection. Never include FndTreeNode, including a root. Resolve TreeVersionId from that response, then add every requested root/child through ordered addValueTreeNode calls. Roots use an empty parentTreeNodeId; each child waits for its parent's returned TreeNodeId.
  4. Omit TreeVersionId on creates even if describe marks it mandatory. Capture returned IDs. Default missing create dates independently to the plan's current date and 4712-12-31. Draft creates also default ChangedSinceValidation to Y and LevelCount/NodeCount to 0 under the documented omission rules. Never default PATCH. New versions default to DRAFT.
  5. Validate the proposed final graph and structure-dependent rules. The offline helper is not a substitute for live prerequisite evidence or Oracle audit.
  6. Persist and present exact environment, request, payload hash, defaults and effect before explicit approval immediately preceding mutation. Read the shared REST client instructions and use that client; no alternate adapter.
  7. Include required supplied Data Source Parameters in the fndTrees body only when each has DataSourceId, ParameterId and ParameterValue, and the recorded pre-create comparison has matched or established assumed_correct because no same-structure tree exists. After each successful node action, read back the returned generated TreeNodeId through the endpoint selected by TreeStructureCode. The three HCM Tree Nodes LOV reads are collection queries with q=TreeNodeId={generated_tree_node_id}; fndTreeNodes retains its generic behavior for other structures. Verify its version, parent, source and PK values. A failed read-back records a verification diagnostic without retrying the successful action or stopping known-successful dependencies. Reconcile response/read-back identities and effects; persist partial results. Audit, activation and flattening are separate outcomes and currently manual handoffs, not implied REST success.

Preflight

Before a live call, validate the offline plan, resolved environment, exact identities, structure/source evidence, parameter comparison, source-member checks, target contract, final graph and durable artifact destination. Present the immutable request payload and operation plan, then obtain explicit approval immediately before the first mutation. A missing, ambiguous or unpersistable preflight result blocks the affected operation. 8. To roll forward a Draft version while retaining its graph, create the new version with SourceTreeVersionId. For a reparent request, resolve the copied node and copied destination-parent generated IDs through the structure-selected lookup, then call updateTreeNode with parentTreeNodeId. The HCM LOV result must be scoped to the requested tree/version; a business-object ID can occur in more than one tree/version. Do not use fndTreeNodes for these three HCM structures.

Errors and recovery

Apply workflow failure handling. Classify failures, redact sensitive details, preserve successful IDs, and stop on uncertain mutation outcomes. No automatic destructive rollback or mutation transport retry. The shared client's one cache-auth retry must be included in execution approval; do not add another retry loop.

Offline quick start

From repository root:

bash
python3 core/fusion-api/apps-common/trees/scripts/trees.py plan --input core/fusion-api/apps-common/trees/examples/compound.json --timezone America/New_York --dry-run
python3 -m unittest discover -s core/fusion-api/apps-common/trees/tests -v

The helper has no live mode, credential imports or HTTP implementation. The skill executes approved calls through the existing core client as documented.

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 Apps Common Trees AI skill do?

Plan and execute controlled tree and Draft-version creation plus dependency-ordered node actions, labels and parameters for Oracle Fusion Trees.

Why use Apps Common Trees on TypingMind?

Because you install it once and use it with any model. Apps Common Trees 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 Apps Common Trees in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/core/fusion-api/apps-common/trees. 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 Apps Common Trees?

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 Apps Common Trees?

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

Is the Apps Common Trees AI skill free?

Yes. It is published on GitHub by oracle under the UPL-1.0 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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