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Manage Additional Name Types Setup Task Guidance

Organization
oracle
Manage Additional Name Types Setup Task Guidance

Exports or imports the complete Manage Additional Name Types setup-task data as an unfiltered Oracle Fusion FSM package, gathering project context, authentication, validation, and the immutable preview before one consolidated approval. 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 Additional Name Types Setup Task Guidance
Stars
112
Forks
26
Bundled files
4
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.

  • 4 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 Additional Name Types 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-additional-name-types
  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-additional-name-types .claude/skills/oracle-manage-additional-name-types-setup-task-guidance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Additional Name Types 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 Additional Name Types 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 Additional Name Types 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 Additional Name Types Setup Task Guidance

Purpose and Boundary

Use this skill only to export or import the complete FSM setup package for Manage Additional Name Types. Fix the task identity to HZ_MANAGE_ADDITIONAL_NAME_TYPES under Trading Community Foundation.

Do not use this skill for planning, sequencing, standalone validation, template generation, filtered exports, or individual additional-name-type 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.

One Consolidated Approval

Use exactly one conversational approval point for each export or import operation. Gather and validate every detail first. Never ask the user to approve project creation, local Run Book artifact refresh, task-context creation, authentication, browser sign-in, source review, metadata checks, preview generation, Task Hook validation, or approval-envelope creation. Those are preparation steps.

Complete the preparation bundle before mentioning approval:

  1. Resolve the project, environment, fixed task, operation, source or output path, scope, execution options, and overwrite state from supplied inputs and durable project context. Ask concise factual questions only for values that cannot be resolved safely; do not frame them as approvals.

  2. If no implementation project exists, create the repository-prescribed draft project and inform the user. If the selected project lacks a current local task context, run the project workspace initializer's artifact-only refresh:

    bash
    python3 core/project-mgmt/project-workspace-initializer/scripts/create_project_runbook.py \
      --project-root project/{project_id} \
      --refresh-artifacts

    This command must not create or update a live Oracle Run Book. Do not stop to ask the user to authorize this local, non-mutating prerequisite.

  3. Complete OAuth authentication and target/source identity checks before building the approval summary. Open the configured OAuth URL in the default browser when interactive sign-in is required and wait for the callback. Treat browser sign-in as authentication, not as an execution approval.

  4. Finish catalog, privilege, path, ZIP, task metadata, Data Loader, Task Hook, destination, overwrite, and immutable-preview checks. Persist the exact preview that downstream approval records will reference.

Only after the complete bundle passes, present one compact execution summary containing the environment, operation, fixed task name/code, exact scope, artifact path and SHA-256 when applicable, output path, execution options, and side effect. Ask the user once to reply exactly approve. That one approval authorizes the one immutable operation shown in the summary:

  • for export, one FSM export submission plus polling and result retrieval;
  • for import, one upload and initiation of the validated FSM ZIP.

Pass that approval through the selected shared or core workflow. Do not ask again at a task-local, Task Hook, adapter, upload, or initiation boundary. Downstream approval records are internal representations of the same user approval, not new prompts. Never bypass host security controls; satisfy any platform-required login or tool permission during preparation when possible and do not reinterpret it as another business-operation approval.

If input is missing, validation fails, the environment is ambiguous, or an explicit exception is required, stop with a friendly correction or blocker. Do not label the correction as an approval request. Any change to the approved environment, task, scope, archive bytes, hash, output path, or execution options invalidates the approval and requires a new prepared summary.

An approval is not consumed by a failure that provably occurs before the first live POST, such as a local browser-launch, token-cache access, file-permission, or approval-envelope persistence failure. Correct that preparation failure and resume the exact approved operation without asking again, but only when the environment, task, scope, archive bytes and hash, output path, execution options, and preview fingerprint are unchanged and audit evidence confirms that no live POST was attempted. Permit at most one bounded recovery for that pre-POST failure. If a POST was attempted, accepted, timed out, or has an uncertain outcome, the approval is consumed; never resubmit automatically.

Direct Export Trigger and Resume

Treat export fsm task Manage Additional Name Types and equivalent whole-task export wording as a complete export request. Infer operation=export, the fixed task identity, template_only=false, export_criteria=[], the standard 300-second wait, and the 10-second poll interval. Resolve the project and the single configured Fusion environment from durable context. Ask a factual question only when more than one viable environment or project remains, or an overwrite conflict cannot be resolved safely.

Run every preparation step in the same turn, then persist the redacted ready preview at:

text
project/{project_id}/artifacts/pillars/cx/customer-data-management/trading-community-foundation/manage-additional-name-types/current/export-approval-preview.json

Set status=ready_for_approval and bind the preview fingerprint to the project, source environment, operation, task name/code, whole-task scope, output directory, adapter, template flag, empty export criteria, wait seconds, poll interval, and live lookup artifact. Include no token, secret, authorization header, or token-cache content. Present the same bound values to the user and ask exactly for approve; do not finish the turn with a plan when preparation has succeeded.

When the next user message is exactly approve, load the persisted ready preview, verify its fingerprint and bound values are unchanged, and immediately run the one shared export command in this skill. Do not repeat discovery, ask another business approval, or stop after process submission: capture the ProcessId, poll it, retrieve FileContent or ProcessLog, verify and register the artifact, update project state, append token usage, and report the result. If no valid ready preview exists, complete or repair preflight and present a new summary; never reinterpret an unbound approval as permission for a POST.

Fixed Task Context

  • Pillar: CX
  • Offering: Customer Data Management
  • Offering code: ZCH_CDM
  • Functional area: Trading Community Foundation
  • Functional area code: ORA_HZ_CDM_FOUNDATION
  • Task name: Manage Additional Name Types
  • Task code: HZ_MANAGE_ADDITIONAL_NAME_TYPES
  • 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. Resolve or create project_id, refresh missing local task-context artifacts, and resolve the named source Fusion environment without an approval prompt. Complete authentication, environment identity, privilege, destination-path, and overwrite checks as read-only preflight. Use the existing shared task-name lookup without hierarchy filters, then select the one result whose TaskCode and TaskName exactly match the fixed task identity:

    bash
    python3 shared/setup-export/task-level-export/scripts/find_setup_task_code.py \
      --task-name "Manage Additional Name Types" \
      --token-cache auto \
      --output project/{project_id}/artifacts/pillars/cx/customer-data-management/trading-community-foundation/manage-additional-name-types/current/setup-task-search.json \
      --json \
      --print

    From the returned rows, require exactly one live row with TaskCode=HZ_MANAGE_ADDITIONAL_NAME_TYPES, the exact task name, AvailableFlag=Y, and CSVExportImportSupportedFlag=Y. The documented setupTasks response may omit OfferingName, OfferingCode, FunctionalAreaName, and FunctionalAreaCode; validate that hierarchy from the effective catalog and persisted task context, and do not reject the task merely because those live fields are absent or blank. If Fusion returns a nonblank hierarchy value on the exact row that contradicts the catalog-backed context, or if zero or multiple rows have the exact fixed code and name, fail closed before approval or POST. Ignore non-exact search results; never select the first row merely because it was returned first.

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

  3. Write export-approval-preview.json with status=ready_for_approval, the immutable bound fields and preview fingerprint defined above. Build the single execution summary from that artifact with the source environment, task name/code, unfiltered whole-task scope, output directory, polling options, and one-process impact. Ask exactly once for approve; do not ask for approval during discovery, authentication, validation, or output preparation.

  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_ADDITIONAL_NAME_TYPES \
      --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-additional-name-types/ \
      --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. Resolve or create project_id, the named target Fusion environment, and one explicit ZIP path. Reuse durable project inputs instead of asking the user to restate them. Reject loose CSV files, multiple archives, and directory scans.

  2. Ensure that the project contains a current task-context entry resolving sequence, exact task name/code, and this skill path. If it is missing or stale, run the local --refresh-artifacts command from the consolidated preparation bundle and continue automatically. Never ask for separate Run Book artifact authorization and never create a live Oracle Run Book as an import prerequisite.

  3. Complete OAuth authentication, browser callback when required, target identity, required privilege, route, and source-path checks before creating the approval preview. Confirm that the same cached authentication mechanism is available to the eventual Data Loader command; do not defer first-time login until after approval.

  4. 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_ADDITIONAL_NAME_TYPES. Preserve the archive byte for byte and calculate its SHA-256.

  5. 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, upload/initiation, and user-friendly reporting. Run its non-mutating preflight first:

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

    Complete Task Hook validation and build the task-routed immutable preview before mentioning approval. Require the preview and later approval envelope to retain executionRoute=data-loader, sourceType=FSM, the exact task code/name, absolute archive path, archive SHA-256, and wholeTask=true.

  6. After every preflight check passes, display one user-friendly execution summary with the exact target, fixed task name/code, ZIP path and hash, whole-task scope, validation status, and configuration-changing impact. Ask exactly once for the separate message approve; stop before the first POST. On that message, record Task Hook approval for the displayed preview, verify the archive hash again, create the approval envelope, and immediately use Data Loader's approved task-routed command without another prompt:

    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
  7. Treat Task Hook preview approval, archive verification, approval-envelope persistence, command authorization, upload authorization, and initiation authorization as internal representations of the one user approval. Do not expose them as additional user approval points. If an authentication or local-permission failure occurs before the first POST, apply the bounded pre-POST recovery rule and reuse the same approval only when the immutable preview fingerprint is unchanged.

  8. Do not invoke setupTaskCSVImports, a shared import script, UCM upload, or a product adapter directly from this skill. Do not use an approval prompt to waive a validation problem: resolve it or report a blocker before the single execution summary. Any changed target, archive bytes, task context, or execution option invalidates approval and restarts Data Loader preflight.

  9. 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.
  • Permit one recovery of an unchanged approved operation only when audit evidence proves the failure occurred before the first live POST. This is a continuation of the same approved operation, not a new submission.
  • Never retry an export or import POST automatically. After a POST attempt or uncertain response, 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 additional-name-type 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 Manage Additional Name Types setup data from TESTApprove and run one unfiltered FSM task export; persist the ZIP, hash, and process result
Import the complete exported ZIP into UATFinish Data Loader and Task Hook preflight, show one immutable-operation summary, request one approval, and submit one FSM import
Import when local task context is missingRefresh local artifact-only Run Book context automatically, finish authentication and validation, then ask once for the consolidated import approval
Authentication cache is inaccessible after approval but before any POSTRepair authentication once and continue the unchanged approved operation without another user approval
Export only one additional-name-typeRefuse partial scope without making a Fusion call
Create or edit an additional-name-typeRefuse 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
  • https://docs.oracle.com/en/cloud/saas/applications-common/26b/farca/Find_SetupTask_Code.html

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 Additional Name Types Setup Task Guidance AI skill do?

Exports or imports the complete Manage Additional Name Types setup-task data as an unfiltered Oracle Fusion FSM package, gathering project context, authentication, validation, and the immutable preview before one consolidated approval. 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 Additional Name Types Setup Task Guidance on TypingMind?

Because you install it once and use it with any model. Manage Additional Name Types 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 Additional Name Types 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-additional-name-types. 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 Additional Name Types 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 Additional Name Types Setup Task Guidance?

As many as you like. As long as a model supports skills, you can use Manage Additional Name Types 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 Additional Name Types 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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