Specflow

Specflow is an enhanced fork of the specify CLI from GitHub Spec Kit, distributed as a single native binary (no Python prerequisites). It scaffolds the files your AI coding harness consumes — SpecKit slash-commands, spec / plan / tasks templates, a constitution, agents, and a backlog system — directly into an existing project, in one command.

Specflow does not call any LLM and does not orchestrate any agent at runtime. Your AI harness (Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot CLI, Windsurf, OpenCode, Antigravity) is what reads the generated files and acts on them.

This page is the canonical documentation. The same content is available as raw Markdown at /llms.txt for LLM consumption — see llmstxt.org for the convention.

Install

The fastest path on macOS or Linux:

curl -fsSL https://raw.githubusercontent.com/mkrlabs/specflow/main/install.sh | bash

The installer downloads the platform binary, verifies the SHA256 checksum, and places it in /usr/local/bin (auto-elevating via sudo if needed). On non-writable prefixes with no terminal it falls back to ~/.local/bin.

Pin a specific version:

curl -fsSL https://.../install.sh | VERSION=v0.7.1 bash

Custom install dir:

curl -fsSL https://.../install.sh | PREFIX=$HOME/.local/bin bash

Or via Homebrew (macOS / Linux):

brew tap mkrlabs/tap
brew install specflow

Manual download: pick the binary for your OS/arch from GitHub Releases, chmod +x, place it on your $PATH. On macOS clear the quarantine attribute with xattr -d com.apple.quarantine /path/to/specflow.

Quickstart

Create a new project

specflow init my-project
cd my-project

This scaffolds a tree configured for the Claude Code harness by default (.claude/, .specflow/, AGENTS.md, tasks/backlog.md, …). Open the project in your harness and run the /specflow.specify slash-command to start your first feature.

Add Specflow to an existing project

cd my-existing-project
specflow init --here

Specflow merges its .gitignore block into your existing file (non-destructively, fenced with # --- Specflow: gitignore --- markers). Other specflow-managed files use upgrade-aware semantics: if you customize a generated file, specflow upgrade will preserve it unless you pass --force.

Pick a different harness

specflow init my-project --ai cursor
specflow init my-project --ai antigravity
specflow init my-project --ai gemini
# … etc.

Eight harness targets are supported: claude (default), cursor, codex, gemini, windsurf, copilot, opencode, antigravity. Each emits files in the convention that harness expects.

Other commands

specflow check                    # diagnose your environment
specflow check --project          # also diagnose the current specflow project
specflow upgrade                  # update templates to the binary's version
specflow upgrade --dry-run        # preview the upgrade plan
specflow upgrade --force          # apply destructive changes (backs up customizations)
specflow self-update              # upgrade the binary itself
specflow self-update --check      # only report whether an update is available
specflow backlog configure        # wire the backlog to a remote (GitHub Issues + Project V2)
specflow backlog sync             # push backlog mutations to the remote
specflow --version                # print version
specflow --help                   # full usage

Available harnesses

All harnesses share the same source-of-truth content in templates/core/. The per-harness adapters in src/infrastructure/harness/ map that core bundle to each harness's directory layout and frontmatter conventions.

What makes Specflow different from upstream Spec Kit

Specflow is a fork of the official specify CLI with three additions:

1. Auto-chained pipeline

The generated specify skill chains clarify → plan → tasks → analyze → implement → review → merge in a single session. Upstream stops at every step and asks the human to invoke the next one. Specflow only stops twice: when clarification is genuinely required, and once before merging.

2. review phase post-implement

After implement, the generated workflow runs a dedicated review phase that checks structure (architecture boundaries, silent error swallowing, leaked internal IDs, cache layering, test coverage) and the quality gates (format, lint, typecheck, tests). If review flags something, the loop is implement → review → fix → re-review — also automatic.

3. Backlog as product source of truth

The generated project ships with a tasks/backlog.md index plus per-task files under tasks/backlog/NNN-slug.md (typed frontmatter: id, title, category, priority, complexity, status, depends_on, spec, tags, created). A Product Owner agent gates every mutation. specflow backlog sync pushes the backlog to a remote (GitHub Issues + Project V2 in v1; GitLab and Bitbucket planned for later).

Design principles

• Agnostic of the user project's language — Python, TypeScript, Go, PHP, Rust… your project, your stack.
• Agnostic of the LLM — Claude, OpenAI, Gemini, local models, anything your harness supports.
• Agnostic of the AI harness — eight first-class targets today, with the same core content for all.
• Agnostic of the backlog source — local Markdown is the source of truth, with one-way sync to the remote of your choice.
• Single binary — distributed via deno compile for macOS arm64/x64, Linux arm64/x64, and Windows x64. No Python, no pip, no extra runtimes on the user's machine.

Repository

Source, releases, and issue tracker: github.com/mkrlabs/specflow.

The AGENTS.md file at the repo root is the canonical context document for any future Claude Code, Codex, or other agent session contributing to the project itself.