Manage Data Styles Setup Task Guidance logo

Manage Data Styles Setup Task Guidance

Organization
oracle
Manage Data Styles Setup Task Guidance

Exports or imports the complete Manage Data Styles setup-task data as an unfiltered Oracle Fusion FSM package. Use only for whole-task FSM export/import; route every import through core.data-loader and refuse record-level maintenance, filtered or template exports, and direct REST or loader execution.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameManage Data Styles Setup Task Guidance
Stars
112
Forks
26
Bundled files
3
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.

  • 3 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 Manage Data Styles Setup Task Guidance 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/pillars/cx/customer-data-management/trading-community-foundation/manage-data-styles
  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/pillars/cx/customer-data-management/trading-community-foundation/manage-data-styles .claude/skills/oracle-manage-data-styles-setup-task-guidance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Data Styles Setup Task Guidance 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 Manage Data Styles Setup Task Guidance 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 Manage Data Styles Setup Task Guidance 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.

Manage Data Styles Setup Task Guidance

Purpose and Boundary

Use this skill only to export or import the complete FSM setup package for Manage Data Styles. Fix the task identity to HZ_MANAGE_DATA_STYLES under Trading Community Foundation.

Do not use this skill for planning, sequencing, standalone validation, template generation, filtered exports, or individual data-style inspection or maintenance. Validation is permitted only as a mandatory preflight for an export or import.

Supported Operations

  1. export: export every setup-data object owned by the fixed task as one unfiltered, non-template FSM package through shared.setup-export.task-level-export.
  2. import: import one complete, unchanged FSM ZIP for the fixed task through the mandatory core.data-loader workflow.

Treat a migration request as these two operations in sequence. Finish and persist the source export before starting a separate target import preflight and approval.

Fixed Task Context

  • Pillar: CX
  • Offering: Customer Data Management
  • Functional area: Trading Community Foundation
  • Functional area code: ORA_HZ_CDM_FOUNDATION
  • Task name: Manage Data Styles
  • Task code: HZ_MANAGE_DATA_STYLES
  • CSV export/import supported: Y
  • Data Loader import mode: FSM

Never accept a task-code, offering, or functional-area override.

Whole-Task FSM Export

  1. Require project_id and the named source Fusion environment. Resolve the live task by the exact fixed task code and confirm CSVExportImportSupportedFlag=Y; fail closed on a mismatch.

  2. Fix template_only=false and export_criteria=[]. Refuse every filter, business-object subset, and template/specification request.

  3. Show the source environment, task name/code, unfiltered whole-task scope, output directory, and one-process impact. Obtain explicit approval immediately before the export POST.

  4. Invoke shared.setup-export.task-level-export exactly once with the fixed context. Use its shared FSM engine for submission, polling, ZIP or ProcessLog retrieval, and packaging; do not implement task-local REST. Run the approved command with unbuffered output:

    bash
    python3 -u shared/setup-export/task-level-export/scripts/export_setup_task_csv.py \
      --data \
      --task-code HZ_MANAGE_DATA_STYLES \
      --offering-name "Customer Data Management" \
      --functional-area-name "Trading Community Foundation" \
      --project-id "{project_id}" \
      --output-dir project/{project_id}/exports/customer-data-management/trading-community-foundation/manage-data-styles/ \
      --token-cache auto \
      --wait-seconds 300 \
      --poll-interval 10

    If the command runner yields a session or cell identifier, wait on that same process until it exits. The -u option exposes the shared exporter's Started ... process: <ProcessId> line without changing shared code. Capture that identifier in the task skill's run record. Empty initial output is not failure, and must not trigger a replacement export.

  5. Persist the ProcessId as soon as it is returned. On success, verify the ZIP, calculate SHA-256, store it in the declared export directory, register it, and update project state.

  6. On timeout, preserve the same ProcessId, last status, and elapsed time. Resume only that process after a later request; never submit a replacement export automatically.

Whole-Task FSM Import Through Data Loader

  1. Require project_id, the named target Fusion environment, and one explicit ZIP path. Reject loose CSV files, multiple archives, and directory scans.

  2. Verify that the file is a readable ZIP containing exactly one member whose basename is ASM_SETUP_CSV_METADATA.xml, and require its single ObjectShortName to equal HZ_MANAGE_DATA_STYLES. Preserve the archive byte for byte and calculate its SHA-256.

  3. Invoke core.data-loader with the fixed task context and dataloader_import_mode=FSM. Let Data Loader own source review, task validation, FSM adapter selection, immutable preview creation, approval, upload/initiation, and user-friendly reporting. Start with:

    bash
    python3 core/data-loader/scripts/upload_and_initiate.py "{setup_data_zip_path}"

    Require the task-routed preview and approval envelope to retain executionRoute=data-loader, sourceType=FSM, the exact task code/name, absolute archive path, archive SHA-256, and wholeTask=true.

  4. Treat the initial import request as validation only. Display the exact target, fixed task code, ZIP path and hash, and impact statement; stop before the first POST. Continue only after the separate user message required by core.data-loader approves that immutable artifact. Then use Data Loader's approved task-routed command with the envelope returned by Task Hook:

    bash
    python3 core/data-loader/scripts/upload_and_initiate.py "{setup_data_zip_path}" \
      --task-routed \
      --task-validation-envelope "{task_validation_envelope_path}" \
      --approve-upload
  5. Do not invoke setupTaskCSVImports, a shared import script, UCM upload, or a product adapter directly from this skill. Any changed target, archive bytes, task context, or execution option invalidates approval and restarts Data Loader preflight.

  6. Persist the accepted execution result and available redacted diagnostics in the declared report path. Check status only when the user asks, and never resubmit an import while its outcome is uncertain.

Error and Retry Contract

Classify failures as input_validation, catalog_or_metadata_mismatch, auth_expired_or_invalid, authorization_or_privilege, transport_or_environment, fusion_api_error, process_timeout, process_failed, artifact_persistence, or approval_blocker.

  • Refresh a cache-managed token once after a read-only 401 or 403. A second authentication response blocks execution.
  • Never retry an export or import POST automatically. Require a new immutable preview and explicit approval before any new submission.
  • On timeout or failure, report the environment, operation, task code, ProcessId or request ID when available, last status, elapsed time, diagnostic artifact, and recommended next action.
  • Never print or persist tokens, secrets, authorization headers, token-cache contents, full Base64 payloads, or sensitive customer data.

Result Contract

For export, report the source environment, fixed task name/code, ProcessId, status, ZIP or log path, enclosure type, and ZIP SHA-256 when available. For import, return the complete core.data-loader Markdown result and report the target environment, fixed task name/code, immutable ZIP path and hash, status, and available process log. Record the skill run and token usage for every durable project activity.

Refusal Rules

  • Refuse individual data-style creation, editing, deletion, or inspection.
  • Refuse filtered, partial, criteria-based, and template/specification exports.
  • Refuse task-code overrides, loose CSV files, edited or repacked ZIPs, and packages that Data Loader cannot confirm as the fixed FSM task.
  • Refuse direct endpoint, script, or adapter execution that bypasses the required approval workflow.

Golden Scenarios

RequestExpected route
Export all data styles from TESTApprove and run one unfiltered FSM task export; persist the ZIP, hash, and process result
Import the complete exported ZIP into UATRun core.data-loader preflight, require its separate immutable-artifact approval, and submit one FSM import
Export only one data styleRefuse partial scope without making a Fusion call
Create or edit a data styleRefuse record-level maintenance without making a Fusion call

Sources

  • knowledge/setup-task-catalog/effective/setupTasksAll.json
  • shared/setup-export/task-level-export/SKILL.md
  • core/data-loader/SKILL.md

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 Manage Data Styles Setup Task Guidance AI skill do?

Exports or imports the complete Manage Data Styles setup-task data as an unfiltered Oracle Fusion FSM package. Use only for whole-task FSM export/import; route every import through core.data-loader and refuse record-level maintenance, filtered or template exports, and direct REST or loader execution.

Why use Manage Data Styles Setup Task Guidance on TypingMind?

Because you install it once and use it with any model. Manage Data Styles Setup Task Guidance 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 Manage Data Styles Setup Task Guidance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/cx/customer-data-management/trading-community-foundation/manage-data-styles. 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 Manage Data Styles Setup Task Guidance?

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 Manage Data Styles Setup Task Guidance?

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

Is the Manage Data Styles Setup Task Guidance 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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