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Apps Common Extensible Flexfields

Organization
oracle
Apps Common Extensible Flexfields

Manages Oracle Fusion Extensible Flexfield setup data through task-level CSV export, approved Data Loader FSM CSV import, and approval-gated REST deployment status workflows.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameApps Common Extensible Flexfields
Stars
112
Forks
26
Bundled files
1
LicenseUPL-1.0
Links
Skill sourceRepository
  • 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 oracle on GitHub. Read the source before you install it.

Installation

Install the Apps Common Extensible Flexfields 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/extensible-flexfields
  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/extensible-flexfields .claude/skills/oracle-apps-common-extensible-flexfields
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apps Common Extensible Flexfields 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 Extensible Flexfields 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 Extensible Flexfields 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 Extensible Flexfields

Purpose

Manage EFF setup-data tasks routed from pillar setup-task skills without owning CSV transport. Read EFF setup-task guidance before acting.

Use shared.setup-export.task-level-export only for export and core.data-loader only for approved FSM CSV import. For EFF metadata deployment or deployment status, use only core.fusion-api.rest-api-client as specified in the EFF reference.

EFF Deployment Workflow

Read the REST Deployment and Status section of EFF setup-task guidance. It is separate from CSV import: an EFF deployment compiles the already-configured flexfield; it does not transport setup data. After the Import EFF Setup Data workflow has completed successfully, enter this workflow for the imported EFF. Do not deploy after an incomplete or failed import.

  1. Preflight the confirmed target environment, application_id, EFF flexfield_code, optional force_deployment value ("true" or "false"), and the caller's required Fusion privilege. When the calling skill omits it, set forceDeployment to the documented default, "false". Preserve URL encoding in the flexfield code (for example, %23); do not infer or substitute identifiers.
  2. For a live deployment, present the exact target environment, POST endpoint, and immutable JSON payload, then obtain explicit approval immediately before invoking the REST client. Submit the request with fusion_rest_client.py, which uses the default Fusion scope for /fscmUI/applcoreApi.
  3. Capture jobId, jobStatus, deploymentStatus, validationStatus, validationReport, deploymentErrorMessage, and the response links. Do not retry a POST without explicit approval.
  4. For status, call the documented getDeploymentState GET endpoint through the REST client using the same resolved identifiers. Poll only when asked, using the bounded interval and timeout in the reference; stop at a terminal response or timeout.
  5. Run a caller-supplied post_deployment_action only after status confirms DEPLOYED. Follow the Task-Specific Post-Deployment Action section of the EFF reference. Do not infer, substitute, or run a post-deployment action for a task that does not explicitly define one.

Preflight and Error Handling

Resolve task identity from the calling pillar skill, verify ExportImportSupportedFlag, and preflight the selected workflow's target, artifact, and approval requirements. Classify an unsupported task as catalog_or_metadata_mismatch and an unapproved import as approval_blocker. Redact credentials, tokens, and sensitive setup data from diagnostics.

For deployment, classify malformed identifiers or force values as input_validation; a 401/403 after the REST client's one refresh attempt as authorization_or_privilege; HTTP failures as fusion_api_error; and a nonterminal result at the polling limit as process_timeout. Return the last safe status and job ID to the calling project workflow; never persist OAuth tokens, secrets, Authorization headers, or sensitive setup data.

For a caller-supplied post-deployment action, classify a missing successful DEPLOYED status as process_failed, an unapproved POST as approval_blocker, and an unsuccessful job result as process_failed. Capture the response and any request identifier in post_deployment_result. Do not retry the POST without a new explicit approval.

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 Extensible Flexfields AI skill do?

Manages Oracle Fusion Extensible Flexfield setup data through task-level CSV export, approved Data Loader FSM CSV import, and approval-gated REST deployment status workflows.

Why use Apps Common Extensible Flexfields on TypingMind?

Because you install it once and use it with any model. Apps Common Extensible Flexfields 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 Extensible Flexfields 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/extensible-flexfields. 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 Extensible Flexfields?

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 Extensible Flexfields?

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

Is the Apps Common Extensible Flexfields 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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