Project Workspace Initializer logo

Project Workspace Initializer

Organization
oracle
Project Workspace Initializer

Initializes the local project workspace, state file, artifact folders, and starting artifact index for a Fusion implementation project.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameProject Workspace Initializer
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 Project Workspace Initializer 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/project-mgmt/project-workspace-initializer
  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/project-mgmt/project-workspace-initializer .claude/skills/oracle-project-workspace-initializer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Workspace Initializer 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 Project Workspace Initializer 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 Project Workspace Initializer 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.

Project Workspace Initializer

Purpose

Initializes the local project workspace, state file, artifact folders, and starting artifact index for a Fusion implementation project.

When to Use

Use this skill before guided implementation work starts in a freshly cloned repository or when creating a new implementation project workspace.

Inputs

  • project_name (optional; defaults from the implementation objective)
  • customer
  • implementation_objective
  • pillar
  • workspace_root
  • identified_task_mapping — required JSON file containing identified project tasks.

Outputs

  • project_state
  • artifact_index
  • workspace_folder_structure
  • project_readme

Implementation Notes

Create a project-specific workspace folder at project/{project_id}/ from the tracked templates under project/_template/. When an implementation objective is available, derive a concise default project name and workspace ID from that objective; cap the generated project name at 50 characters. Use draft-impl-project-<date> only when no objective is available at creation time. Never write credentials into the workspace. Initialize project/{project_id}/state/project-state.json, project/{project_id}/artifacts/artifact-index.json, and the mirrored artifact root folders.

Use scripts/initialize_project_workspace.py to create workspaces deterministically from the template:

bash
python3 core/project-mgmt/project-workspace-initializer/scripts/initialize_project_workspace.py \
  --project-name "Order Management Implementation" \
  --customer "Example Customer" \
  --implementation-objective "Implement Oracle Fusion Cloud Order Management" \
  --pillar SCM \
  --offering "Order Management" \
  --identified-task-mapping "path/to/identified-task-mapping.json"

The script creates project/{project_id}/README.md with the local workspace ignore notice, seeds the objective artifact, writes the initializer run record, and updates the project state and artifact index. New-project initialization requires automatic initial Run Book creation by default. The workspace template does not seed runbook_control; after the planning wrapper successfully persists all initial Run Book planning artifacts, it adds runbook_control with status: pending. The workflow must then continue through core.runbook and persist the creation outcome. Use --no-create-runbook only when the user explicitly wants to defer Oracle Run Book creation; generate the same planning artifacts, leave status: pending, and wait for an explicit user request to create the Run Book.

Project Initialization Completion Gate

Do not report project initialization complete until all required artifacts have been created and verified. Report the exact paths for:

  • state/project-state.json
  • artifacts/artifact-index.json
  • artifacts/shared/runbook/current/identified-task-mapping.json
  • artifacts/shared/token-usage/current/token-usage-log.md

A copied project/_template directory is not an initialized project, and an .example.json artifact cannot substitute for a required populated artifact. If required source data is missing, create a required artifact only with explicitly known values and mark unresolved fields as pending; otherwise stop and report the blocker. Run schema/JSON validation and git diff --check before handoff.

Before running the initializer, create or locate one valid JSON task mapping. --identified-task-mapping is mandatory for every initialization. The initializer rejects calls without it, persists the exact file at artifacts/shared/runbook/current/identified-task-mapping.json, and indexes it.

For every task row, identify Offering, Pillar, and Functional Area from the user-provided intent and data using knowledge/setup-task-catalog/effective/offering-functional-area-task-catalog.json. Use the task-and-offering combination; a task name alone is not sufficient. If more than one offering remains plausible, ask the user to provide the current offering for that task. Do not infer a replacement offering or create a separate offering-mapping artifact. Task-context creation records unmatched contexts and candidate offerings against the same catalog but continues with the supplied mapping so the user can correct it later.

Task-Mapping Completeness

When the mapping is based on a user-supplied workbook, document, export, or catalog selection, preserve the complete matching task set eligible for the resolver's ordered setup_task_sequence. Do not condense, group, merge, omit, or infer eligible tasks in order to make the plan shorter. Copy each included task's resolved order into Resolver Sequence; do not use catalog display sequence or mapping-row position as a replacement. Keep pre-release and custom tasks diagnosed as sequence_unresolved out of the mapping's tasks array and report them separately. Each mapped task must retain a stable task identifier, the exact source reference (sheet and row, or equivalent source location), its requested phase, and the applicable FSCR skill path. Validate and report the source-match count before initialization; if the task count is ambiguous, the source cannot be read, or a complete match cannot be established, stop and report the gap rather than creating a partial mapping.

Before calling scripts/create_project_runbook.py, use the mapping produced under the offering task-map builder's sequence gate. Do not add tasks to it or fill missing Resolver Sequence values in the initializer. If that mapping is absent or contains a sequence_unresolved pre-release or custom task, stop and report the mapping gap; do not create a Run Book plan.

The generic artifacts/shared/runbook/current/identified-task-mapping.json location is the initializer's traceability input for the later Run Book workflow. When a user also requests an implementation-facing plan, persist that plan separately under the applicable project implementation artifact path (for example, artifacts/{pillar}/{offering}/) and index both artifacts.

For every initialization, the LLM must call scripts/create_project_runbook.py --project-root project/{project_id} --refresh-artifacts to generate and persist setup-task-plan.json, task-context.json, and runbook-plan.json. This script is planning-only and never creates or updates an Oracle Run Book. When runbook_control is absent, it adds status: pending only after all three artifacts are written. When control already exists, artifact generation and refresh leave the entire project-state file untouched, regardless of the Run Book status. If --no-create-runbook was supplied, stop with status: pending and wait for an explicit user request to create the Run Book from the persisted plan. Otherwise, invoke core.runbook to create the Oracle Run Book automatically and supply the absolute project workspace path to its create-runbook --project-root option. Only that creation command requires pending, unbound state. It persists the active reference and status: active on success, status: failed on a confirmed failure, or status: unknown on a partial or indeterminate creation. Both failure states block downstream Fusion submission and must not be retried automatically.

Multi-Context Runbook Planning

A Run Book may contain tasks from multiple offerings, functional areas, or pillars. Preserve each task's catalog-resolved offering and functional area in the task mapping, setup-task plan, task context, and runbook plan. For a mixed-context plan, set the plan-level offering and functional_area to Multiple Offerings and Multiple Functional Areas; task-level values remain authoritative for task routing and Run Book grouping.

If the identified task mapping changes, regenerate the local planning artifacts with:

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

Refresh can regenerate the persisted setup-task plan, task context, and runbook plan before or after Oracle Run Book creation. It leaves the existing runbook_control state untouched and does not change the live Run Book.

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 Project Workspace Initializer AI skill do?

Initializes the local project workspace, state file, artifact folders, and starting artifact index for a Fusion implementation project.

Why use Project Workspace Initializer on TypingMind?

Because you install it once and use it with any model. Project Workspace Initializer 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 Project Workspace Initializer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/core/project-mgmt/project-workspace-initializer. 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 Project Workspace Initializer?

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 Project Workspace Initializer?

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

Is the Project Workspace Initializer 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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