name: specflow-init
description: Use when setting up SpecFlow in a new or existing project for the FIRST TIME. Triggers when the user says "set up SpecFlow," "initialize the project," or "get started with SpecFlow." Conversational bootstrap that scaffolds directories, installs hooks, generates CI workflows, and recommends next steps. NOT for: re-initializing an already-configured project, adding packs to an existing setup (use specflow-adapter), or routine configuration changes.
Freeform Input Handling
This skill accepts freeform user input alongside the command. Interpret the user's message to determine scope and depth:
- No additional context → run the standard workflow (deterministic core only)
- A question or concern → run the deterministic core, then address the question directly using the results
- A request for depth ("go deep", "be thorough", "all lenses") → run deterministic core + full agent-driven analysis
- A specific focus ("focus on REQ-003", "check compliance only") → narrow scope to the request, still run deterministic core first
Always run the deterministic core regardless of input. It costs zero tokens and provides the foundation for any analysis.
SpecFlow Init
Conversational bootstrap for a SpecFlow project.
Workflow
1. Detect or ask about the AI platform
Check the project root for platform detection markers (from platforms.yaml):
| Marker | Platform Code | Name |
|---|
.claude/ | claude-code | Claude Code |
.cursor/ | cursor | Cursor |
.windsurf/ | windsurf | Windsurf |
.cline/ | cline | Cline |
.gemini/ | gemini | Gemini CLI |
.opencode/ | opencode | OpenCode |
.github/copilot-instructions.md | github-copilot | GitHub Copilot |
.roo/ | roo | Roo Code |
.qwen/ | qwen | QwenCoder |
.kiro/ | kiro | Kiro |
.kilocode/ | kilocode | KiloCoder |
.codex/ | codex | Codex |
.trae/ | trae | Trae |
.junie/ | junie | Junie |
Scan markers in order. If a marker exists, use that platform code. If multiple markers are found, prefer the first match (table order). specflow init also warns when multiple AI-host platform dirs are detected, since skills are only installed to the one you targeted — run uv run specflow refresh --all-platforms afterward to bring the others current.
If no marker is found, ask:
"Which AI coding assistant are you using?"
- Claude Code (Recommended)
- Cursor
- Windsurf
- Cline
- Gemini CLI
- GitHub Copilot
- OpenCode
- Roo Code
- Kiro
- Other
1b. New project, or bringing an existing codebase?
Ask (bounded):
"Is this a NEW project, or are you bringing an EXISTING codebase into SpecFlow?"
- New project (Recommended)
- Existing codebase
If Existing codebase (brownfield): remember a brownfield flag and prefer the adoption preset (see Step 2) — it installs the /specflow-adopt skill, which inventories your code and backfills an as-built baseline instead of authoring requirements from scratch. Greenfield projects do not need it.
Freeform input: if the user's message already implies an existing codebase ("set up SpecFlow for my project", "add SpecFlow to this repo") and the repo has existing source files, set the brownfield flag without asking.
2. Gather project context
Ask the user:
- "What type of project is this?" -- bounded options: Web App, CLI Tool, Library, Firmware/Embedded, Data Pipeline, Mobile, Other
- "Do you want to apply an industry standards preset?" -- bounded options:
iso26262-demo, default, adoption (existing codebase — installs /specflow-adopt), or None (Recommended)
- "Do you want to install optional artifact types (hazard, risk, control)?" -- bounded options: Yes, No (Recommended)
- "Which CI provider do you use?" -- bounded options: GitHub Actions (Recommended), GitLab CI, None
- "Do you have any specific compliance standard packs you want to install?" -- free text, or None (Recommended)
3. Run the init command
uv run specflow init --platform <platform_code>
Append flags as needed:
--domain <project-type> if a project type was chosen (e.g., --domain embedded, --domain api-service, --domain web-app)--domain-tags <comma-separated> for domain qualifiers (e.g., --domain-tags real-time,safety-critical)--preset <preset> if a preset was chosen--with-types hazard,risk,control if optional artifact types were chosen--no-ci if no CI provider was requested
This scaffolds .specflow/, _specflow/, config files, schemas, checklists, and installs skill directories for the target platform. When --domain is provided, it also persists the domain classification and attempts to generate project-level best practices (the "process booklet").
4. Verify SpecFlow instruction injection
The specflow init CLI has already injected the SpecFlow instruction block into the platform's instruction file (via scaffold.py). Verify it landed correctly.
4a. Check the injected block
Verify that the target instruction file contains the SpecFlow sentinel marker:
4b. Determine the target instruction file
The specflow init CLI command has already handled instruction injection automatically (via scaffold.py). The instruction file should already contain the SpecFlow block wrapped in <!-- SpecFlow section ... --> sentinels. Verify it was injected correctly by checking the target file for the sentinel marker.
If the marker is missing (unusual), the target file mapping is:
5. Verify git hook installation
The specflow init command installs a pre-commit hook automatically when .git/ exists. Check the command output for:
+ Installed .git/hooks/pre-commit
If the project has no .git/ directory yet, inform the user they can run uv run specflow hook install after initializing git.
6. Generate CI workflow (if requested)
If a CI provider was specified and the adapters config was generated, the init command may have already created the workflow file. Verify from the output. If not, run:
uv run specflow ci generate
7. Report and recommend next steps
Summarize what was done:
- Directories created (
.specflow/, _specflow/, platform skills directory)
- Configuration files written (
config.yaml, state.yaml, adapters.yaml)
- Domain classification persisted (if
--domain was provided)
- Instruction file updated (target file path)
- Pre-commit hook installed
- CI workflow generated (if applicable)
- Packs applied (if any)
- Docs knowledge surface recognized (root
*.md + docs/); docs: config block written
Then recommend:
If greenfield: "Your project is ready. Run /specflow-discover to start capturing requirements, /specflow-adapter to configure CI or exchange integrations first, or /specflow-doc to cite your spec from existing docs or your README."
If brownfield (adoption preset): "Your project is ready. Run /specflow-adopt to inventory your existing code and backfill an as-built baseline (forward work for new features then resumes through /specflow-discover). Or /specflow-adapter to configure CI."
Upgrade note. After upgrading SpecFlow (uv tool upgrade specflow), remind the user to run uv run specflow refresh — it updates the copied skills, agent-context block, and templates in this repo without a full re-init.
Rules
- When offering the user choices for project type, presets, or CI, provide clear, bounded options.
- The preset option should default to "None" unless the user indicates a regulated industry.
- The CI option should default to "None" unless the user mentions "GitHub" or "GitLab".
- Every choice offered to the user includes "(Recommended)" on the suggested default.
- Platform detection should be automatic when possible. Only ask when no marker is found.
- If the user says "skip" or "move on", accept all defaults and continue without further questions.
- Never overwrite existing instruction file content. Only append the SpecFlow block.
- The
--platform flag must always be passed explicitly to specflow init.
- Instruction injection is idempotent: if
<!-- SpecFlow section is already present, do nothing.
