# Sdlc Workspace Init > Initialize a new repository with SDLC workspace files — MCP config, copilot-instructions.md, quality instructions, and prompt files. Use when setting up a new project, bootstrapping SDLC, onboarding a repo, or when Harness detects missing workspace files. Use when this capability is needed. - Skill: `tomevault-io/sdlc-workspace-init` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/sdlc-workspace-init` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/sdlc-workspace-init/raw - Safety review: pending (external: skill-scanner PASS, skillspector FAIL) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/sdlc-workspace-init --- # SDLC Workspace Initialization ## When to use - Setting up a new repository for SDLC-driven development - After installing the SDLC Agent Template plugin into a repo that has no `.github/copilot-instructions.md` - When Harness detects missing workspace files during first-run initialization ## What this skill does Copies workspace-specific files from the skill's `assets/` folder into the target repo's `.github/` and `.vscode/` directories. These files cannot be distributed via the plugin system because they require per-project customization or live outside `.github/plugin/`. > **This skill is the ONLY place that deploys `.vscode/mcp.json`.** > No other agent or skill should deploy mcp.json to avoid duplicate writes. ## Files deployed | Source (skill assets) | Target (workspace) | Customized? | |---|---|---| | `assets/mcp.template.json` | `.vscode/mcp.json` | No — copied as-is (MCP server definitions) | | `assets/copilot-instructions.template.md` | `.github/copilot-instructions.md` | Yes — project name, domain, stack | | `assets/instructions/*.instructions.md` | `.github/instructions/` | No — copied as-is | | `assets/prompts/*.prompt.md` | `.github/prompts/` | No — copied as-is | | *(generated at runtime)* | `.github/reference-catalog.md` | Yes — empty template with 5 fixed sections | ## Procedure ### Step 1: Check workspace state Check what already exists in the workspace: - `.vscode/mcp.json` — MCP server config - `.github/copilot-instructions.md` — project-specific Copilot instructions - `.github/instructions/` — quality instruction files (check if directory has files, not just exists) - `.github/prompts/` — SDLC prompt files (check if directory has files, not just exists) If ALL four are present and populated → Report: _"Workspace already initialized. Skipping."_ and stop. If any are missing or empty → continue and deploy **only the missing pieces**. ### Step 2: Create directory structure **Before writing any files**, create the required directories using the terminal. These directories may not exist in a fresh workspace: ```bash mkdir -p .github/instructions .github/prompts .vscode ``` **This step is mandatory** — do NOT skip it. File writes will fail silently in directories that don't exist. ### Step 3: Deploy MCP server configuration > **This is the ONLY place mcp.json should be deployed.** Do NOT deploy it anywhere else. > **CRITICAL: Use the terminal to write this file — do NOT use create/edit tools.** > LLM file tools sometimes append instead of overwrite, producing invalid JSON. 1. Check if `.vscode/mcp.json` exists in the workspace. 2. **If NOT found** → deploy using the terminal: a. Read [mcp.template.json](./assets/mcp.template.json) to get the content. b. **Delete any partial file** and write fresh using the terminal: ```bash rm -f .vscode/mcp.json ``` Then write the content using a heredoc redirect (which ALWAYS overwrites): ```bash cat > .vscode/mcp.json << 'MCPEOF' MCPEOF ``` c. **Validate the file is valid JSON:** ```bash python3 -c "import json; json.load(open('.vscode/mcp.json')); print('✅ mcp.json is valid JSON')" ``` If validation fails → `rm -f .vscode/mcp.json` and retry from step (b) once. 3. **If found** → validate it anyway: ```bash python3 -c "import json; json.load(open('.vscode/mcp.json')); print('✅ mcp.json is valid JSON')" ``` If valid → Skip. Report: _"Existing `.vscode/mcp.json` found — keeping current config."_ If invalid → `rm -f .vscode/mcp.json` and deploy fresh from step (b). After deploying (or confirming it exists), tell the user: > ✅ `.vscode/mcp.json` — 7 MCP server definitions ready. > Please start all MCP servers: open `.vscode/mcp.json` and click **"Start"** on each. ### Step 4: Gather project information Ask the user for: 1. **Project name** (required) — e.g., "SmartDoc Analyzer" 2. **Business domain** (required) — e.g., "Intelligent document processing" 3. **Tech stack** (required) — e.g., "Python, FastAPI, React, TypeScript" 4. **Primary language(s)** (derived from tech stack) — used to filter instruction files (e.g., `code-quality-py.md` vs `code-quality-ts.md`) ### Step 5: Deploy copilot-instructions.md 1. Read [copilot-instructions.template.md](./assets/copilot-instructions.template.md). 2. Replace these placeholders: - `{{PROJECT_NAME}}` → user's project name - `{{BUSINESS_DOMAIN}}` → user's business domain - `{{TECH_STACK}}` → user's tech stack 3. Write the result to `.github/copilot-instructions.md`. ### Step 6: Create empty reference catalog template Create `.github/reference-catalog.md` with the empty catalog template. This template will be populated by the Analyst agent during Phase 1-2 using the `sdlc-reference-catalog` skill. Write this content to `.github/reference-catalog.md`: ``` # Reference Catalog > This catalog is populated by the Analyst agent during the design phase. > Downstream agents may append new entries but must not modify existing ones. > Each entry includes the source agent that added it. ## Approved Libraries ## Project Templates ## API Patterns ## Code Examples ## Documentation Links ``` > **Why an empty template?** The Analyst populates this during the design phase using live research > from GitHub MCP, Context7, awesome-copilot, and web sources. A static pre-populated catalog > falls out of date; a living catalog stays current with each project's actual tech stack. ### Step 6b: Ask catalog review preference Ask the user: > **Catalog review preference:** Would you like to review the reference catalog before > development begins, or proceed automatically? > > - **review** (default) — Harness will show you the catalog summary after the Analyst > populates it, and you can approve, edit, or proceed. > - **auto** — Harness proceeds automatically after catalog population. Store the answer in `harness-config.yml` as: ```yaml catalog_review: true # "review" → true (default) catalog_review: false # "auto" → false ``` If the user doesn't answer or skips the question, default to `catalog_review: true`. ### Step 7: Deploy instruction files Copy each file from `assets/instructions/` to `.github/instructions/`: - [code-quality-py.instructions.md](./assets/instructions/code-quality-py.instructions.md) - [code-quality-ts.instructions.md](./assets/instructions/code-quality-ts.instructions.md) - [code-quality-tsx.instructions.md](./assets/instructions/code-quality-tsx.instructions.md) - [code-quality-java.instructions.md](./assets/instructions/code-quality-java.instructions.md) - [code-quality-csharp.instructions.md](./assets/instructions/code-quality-csharp.instructions.md) - [code-quality-go.instructions.md](./assets/instructions/code-quality-go.instructions.md) - [code-quality-rust.instructions.md](./assets/instructions/code-quality-rust.instructions.md) - [test-quality.instructions.md](./assets/instructions/test-quality.instructions.md) - [test-quality-ts.instructions.md](./assets/instructions/test-quality-ts.instructions.md) - [test-quality-tsx.instructions.md](./assets/instructions/test-quality-tsx.instructions.md) - [test-quality-java.instructions.md](./assets/instructions/test-quality-java.instructions.md) - [test-quality-csharp.instructions.md](./assets/instructions/test-quality-csharp.instructions.md) - [test-quality-go.instructions.md](./assets/instructions/test-quality-go.instructions.md) - [test-quality-rust.instructions.md](./assets/instructions/test-quality-rust.instructions.md) Only copy instruction files matching the project's language stack: - Python project → copy `code-quality-py` + `test-quality` - TypeScript project → copy `code-quality-ts` + `test-quality-ts` - React project → copy all TypeScript + TSX files - Java project → copy `code-quality-java` + `test-quality-java` - C# project → copy `code-quality-csharp` + `test-quality-csharp` - Go project → copy `code-quality-go` + `test-quality-go` - Rust project → copy `code-quality-rust` + `test-quality-rust` - Full stack → copy all applicable files ### Step 8: Deploy prompt files Copy each file from `assets/prompts/` to `.github/prompts/`: - [requirement-and-design.prompt.md](./assets/prompts/requirement-and-design.prompt.md) - [repo-structure-and-cicd.prompt.md](./assets/prompts/repo-structure-and-cicd.prompt.md) - [deployment.prompt.md](./assets/prompts/deployment.prompt.md) - [implementation-and-tests.prompt.md](./assets/prompts/implementation-and-tests.prompt.md) - [repo-documentation.prompt.md](./assets/prompts/repo-documentation.prompt.md) - [qa-rai-release.prompt.md](./assets/prompts/qa-rai-release.prompt.md) ### Step 9: Report ``` ## ✅ SDLC Workspace Initialized - ✅ `.vscode/mcp.json` — 7 MCP server definitions deployed - ✅ `.github/copilot-instructions.md` — customized for "{{PROJECT_NAME}}" - ✅ `.github/instructions/` — X quality instruction files deployed - ✅ `.github/prompts/` — 6 SDLC prompt files deployed - ✅ `.github/reference-catalog.md` — empty catalog template created (Analyst will populate during Phase 1-2) **Next steps:** 1. **Start MCP servers** — open `.vscode/mcp.json` and click "Start" on each server. All 7 servers are required. 2. Review `.github/copilot-instructions.md` and adjust if needed. 3. Use `@Harness` to start your first SDLC task. 4. Use `/requirement-and-design` to begin Phase 1-2. ``` --- > Source: [Dongbumlee/sdlc-harness](https://github.com/Dongbumlee/sdlc-harness) — distributed by [TomeVault](https://tomevault.io).