init-workspace-context

name	init-workspace-context
description	Classify workspace mode.
license	Apache-2.0
disable-model-invocation	true
user-invocable	false
model	claude-haiku-4-5, gemini-3-flash-preview
tags	["init","workspace","context","detection"]
baseSchema	docs/schemas/skill.md

<init_workspace_context>

Workspace initialization classifier — fast, precise, zero-waste.

<when_to_use_skill> Initialization must behave differently for fresh, existing, or plugin workspaces. Misclassifying the mode overwrites config, skips setup, or duplicates work. First skill in the init flow — runs before all others. Also generates or updates gain.json with SDLC tooling configuration collected from the user. </when_to_use_skill>

<core_concepts>

• All Rosetta prep steps MUST be FULLY completed, load-context skill loaded and fully executed
• Three modes: install (no files per bootstrap_rosetta_files), upgrade (some files per bootstrap_rosetta_files exist), plugin (LLM context already contains "RUNNING AS A PLUGIN")
• gain.json is the single source of truth for SDLC tooling; it wins in conflicts with other files </core_concepts>

1. Check existing LLM context for "RUNNING AS A PLUGIN": If already there → set mode = plugin 2. If not plugin, scan workspace for existing files per `bootstrap_rosetta_files` 3. Any found → mode = upgrade; none → mode = install 4. Scan for multiple sub-repositories with independent documentation roots → set composite flag, treat git repos as modules, requires use of `large-workspace-handling` skill 5. Build file inventory: path and status for each file per `bootstrap_rosetta_files` 6. Generate or update repository root `gain.json` — follow the `gain_json_generation` section below 7. Return: mode (install|upgrade|plugin), plugin_active, composite, existing_files list, gain_json_status (created|updated|skipped|pending_user_input)

<gain_json_generation>

1. Auto-detect fields from workspace. Non-obvious: versions.rosetta — read from Rosetta plugin path in current LLM context, e.g. "2.0.43"; versions is for GAIN suite tools only (rosetta, specflow, etc.), not the project
2. Ask user for anything unresolved in a single batch. All fields optional. Prioritize critical fields first. Leave placeholders for skipped fields
3. If gain.json already exists: read it, ask only about gaps and placeholders; never overwrite confirmed values
4. If running as subagent: output gain_json_draft (template with auto-detected values filled in, all other fields left as template placeholders) to orchestrator; instruct orchestrator to ask user to fill in each placeholder field, and leave any unanswered fields exactly as the placeholder in the template

</gain_json_generation>

gain.json

{
  "description": "[PROJECT_DESCRIPTION - one sentence: what this project/product is and does]",
  "servicesDescription": {
    "[SERVICE_NAME]": "[SERVICE_DESCRIPTION - what this service does]"
  },
  "codingAgents": [
    "[CODING_AGENTS - e.g. cursor, claude-code, codex, copilot]"
  ],
  "sdlc": {
    "issue_tracker": "[ISSUE_TRACKER - e.g. Jira, GitHub Issues, Linear]",
    "issue_tracker_project": "[ISSUE_TRACKER_URL - e.g. https://myorg.atlassian.net/jira/...]",
    "wiki": "[WIKI - e.g. Confluence, Notion, GitHub Wiki]",
    "wiki_project": "[WIKI_URL - e.g. https://myorg.atlassian.net/wiki/...]",
    "test_management": "[TEST_MANAGEMENT - e.g. TestRail, Zephyr; omit if not used]",
    "test_management_project": "[TEST_MANAGEMENT_PROJECT - project key or URL]",
    "scm": "[SCM - e.g. GitHub, GitLab, Bitbucket]"
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
  
   
     
  
   
     
  

</init_workspace_context>