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Ccwf Cli

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breaking-brake
ccwf-cli

Use the `ccwf` CLI (from @cc-wf-studio/cli) to render, validate, preview, export, or run cc-wf-studio workflow JSON files from the terminal. Apply whenever the user mentions viewing, visualizing, checking, executing, or converting a workflow under `.vscode/workflows/` (or any `*workflow*.json`), wants a Mermaid diagram of a workflow, asks to "see" / "preview" / "open" a workflow, or wants to run a workflow as a Claude Code Skill without opening VSCode.

Overview

Publisherbreaking-brake
Repositorycc-wf-studio
Skill nameccwf-cli
Stars
5.4K
Forks
569
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by breaking-brake on GitHub. Read the source before you install it.

Installation

Install the Ccwf Cli 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.
  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/breaking-brake/cc-wf-studio.git /tmp/cc-wf-studio
mkdir -p .claude/skills
cp -r /tmp/cc-wf-studio/packages/cli/skills/ccwf-cli .claude/skills/ccwf-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccwf Cli 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 Ccwf Cli 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 Ccwf Cli 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.

ccwf CLI

ccwf is the command-line entry into cc-wf-studio — a visual AI-agent workflow tool. It treats .vscode/workflows/*.json workflow files as inputs and lets you render them as Markdown, validate the schema, open them in a browser-based viewer, convert them into Agent Skills, or execute them. The same workflow JSON also drives the VSCode extension and an MCP server, so you can pick whichever interface fits the situation.

This Skill teaches Claude how to recognise when ccwf is the right answer and which subcommand to reach for. The subcommands and flags listed here are the source of truth — if behaviour seems off, check ccwf <subcommand> --help first.

Prerequisites

Confirm ccwf is available before running any other subcommand:

bash
ccwf --version

If ccwf is not on PATH:

  • Preferred: install globally — npm install -g @cc-wf-studio/cli
  • Without install: prefix every command with npx @cc-wf-studio/cli (e.g. npx @cc-wf-studio/cli render <file>)
  • In a project: pnpm add -D @cc-wf-studio/cli and run via pnpm exec ccwf

If the user has a VSCode workspace and you can't find a workflow file, search .vscode/workflows/*.json first; that's the canonical location.

Core workflow

The natural flow when the user has a workflow file in hand:

  1. ccwf validate <file> — schema-check it. Exit 0 = clean, exit 1 = errors. Run this first whenever you receive a workflow from elsewhere.
  2. ccwf preview <file> — opens a read-only viewer (Mermaid + per-node Markdown) in the browser. Use this when the user says "show me" / "what's in this workflow?" / "見せて".
  3. ccwf export <file> or ccwf run <file> — materialises the workflow as Claude Code (or another agent's) Skill files. Use run when the user wants the next step to be actually executing the workflow with claude.

If a step fails, stop and surface the exact error to the user before moving on.

Subcommand reference

ccwf render <file>

Print a Markdown bundle (Mermaid flowchart + per-node execution instructions) to stdout. Use when piping to another tool or pasting into a PR / chat message.

bash
ccwf render ./.vscode/workflows/my-workflow.json             # Markdown (default)
ccwf render ./.vscode/workflows/my-workflow.json -f mermaid  # ```mermaid block only

Output is the same content ccwf preview shows in the right pane.

ccwf validate <file>

Schema-check the workflow JSON.

bash
ccwf validate ./.vscode/workflows/my-workflow.json           # exit 0/1, human-readable errors on stderr
ccwf validate ./.vscode/workflows/my-workflow.json --json    # prints { valid, errors[] }

Use this:

  • Before ccwf run / ccwf export if the file is hand-edited or AI-generated
  • In CI / pre-commit hooks
  • When the user asks "is this workflow OK?" / "壊れてない?"

ccwf preview <file>

Open a read-only viewer in the browser. Mermaid flowchart on the left, per-node Markdown on the right. Auto-reloads when the file changes on disk. Auto-shuts down 30s after the last viewer tab closes.

bash
ccwf preview ./my-workflow.json                  # boot, open browser
ccwf preview ./my-workflow.json --port 51234     # pin to a port
ccwf preview ./my-workflow.json --keep-alive     # don't auto-shutdown when the tab closes

Use when the user says any of: "show me", "preview", "what does this workflow do?", "open it in a browser", "見せて", "可視化して".

The printed URL has the shape http://localhost:<port>/<uuid>/. The UUID just keeps two concurrent preview sessions from clobbering each other — it isn't a security boundary, since the server only listens on the loopback interface by default.

ccwf canvas <file> (experimental)

Open the full editable canvas in the browser (same UI as the VSCode extension). Saves write back to the workflow file. Heavier than preview; reach for it only when the user explicitly wants to edit without VSCode.

bash
ccwf canvas ./my-workflow.json

Other VSCode-only features (Slack share, Claude API upload, MCP server management, agent-specific export buttons) return a CANVAS_UNSUPPORTED error in this mode — they require the extension proper.

ccwf export <file> [--agent <name>]

Materialise the workflow as Agent Skill files for a target agent. Pure file write, no execution.

bash
ccwf export ./my-workflow.json                                 # --agent claude-code (default)
ccwf export ./my-workflow.json --agent cursor                  # cursor
ccwf export ./my-workflow.json --agent codex --cwd /tmp/proj   # codex, custom output root
ccwf export ./my-workflow.json --overwrite                     # replace existing files

Output by --agent:

--agentFiles emitted (relative to --cwd or process.cwd())
claude-code (default).claude/agents/<sub-agent>.md (inline SubAgent nodes) + .claude/skills/<workflow>/SKILL.md
antigravity.agent/skills/<workflow>/SKILL.md
codex.codex/skills/<workflow>/SKILL.md
copilot.github/skills/<workflow>/SKILL.md
cursor.cursor/skills/<workflow>/SKILL.md + .cursor/agents/<sub-agent>.md
gemini.gemini/skills/<workflow>/SKILL.md
roo-code (Zoo Code).roo/skills/<workflow>/SKILL.md

Use export (rather than run) when the user wants the files only — e.g. checking generated content into git, inspecting before execution, or generating Skills for multiple agents in batch.

ccwf run <file> [--agent <name>] [--launch]

Same file output as ccwf export, plus a "next step" hint on stdout. --launch additionally spawns the claude binary in the output directory (best-effort, claude-code agent only).

bash
ccwf run ./my-workflow.json --launch          # write + spawn claude
ccwf run ./my-workflow.json --agent cursor    # write only (cursor launch not yet wired)

Use run:

  • When the user wants to execute the workflow in Claude Code right after generating it ("動かして" / "実行して" / "run this workflow")
  • As the one-stop shortcut after validate passes

ccwf mcp --file <file>

Run the cc-wf-studio stdio MCP server in-process against <file>. Equivalent to the standalone ccwf-mcp bin. Use this to point an MCP client (Claude Code, MCP Inspector, …) at a workflow so the agent can read and edit it through MCP tools.

bash
ccwf mcp --file ./.vscode/workflows/my-workflow.json

Typical .mcp.json snippet for Claude Code:

json
{
  "mcpServers": {
    "cc-wf-studio": {
      "type": "stdio",
      "command": "npx",
      "args": ["@cc-wf-studio/cli", "mcp", "--file", ".vscode/workflows/my-workflow.json"]
    }
  }
}

The MCP server exposes 6 tools: get_workflow_schema, get_current_workflow, apply_workflow, update_nodes, list_available_agents, highlight_group_node. Use these when the user wants AI-driven editing of the workflow itself (not just rendering / running it).

ccwf install-skills / ccwf uninstall-skills

Copy this Skill bundle into a discoverable location, or remove it again.

bash
ccwf install-skills                  # ~/.claude/skills/ccwf-cli/ (user-scope)
ccwf install-skills --project        # ./.claude/skills/ccwf-cli/ (project-scope)
ccwf install-skills --overwrite      # replace an existing copy
ccwf install-skills --dry-run        # print paths without writing

ccwf uninstall-skills                # remove from ~/.claude/skills/
ccwf uninstall-skills --project      # remove from ./.claude/skills/
ccwf uninstall-skills --dry-run      # print deletions without writing

Use cases:

  • "Install the ccwf skill" / "teach Claude Code about ccwf" → ccwf install-skills
  • "Update the ccwf skill" / "refresh the install" → ccwf uninstall-skills && ccwf install-skills
  • "Remove the ccwf skill" / "cleanup before uninstalling the CLI" → ccwf uninstall-skills

uninstall-skills is idempotent: running it twice prints "nothing to remove" the second time and exits 0.

Mapping user phrasing to subcommands

Use this as a lookup when the user describes intent in natural language. If the user names a file under .vscode/workflows/ or a *workflow*.json, the subcommand pattern below applies.

User says...Run
"Show me / preview this workflow", "見せて", "可視化して"ccwf preview <file>
"Render this as Markdown", "Mermaid 図にして"ccwf render <file>
"Is this workflow valid?", "壊れてない?", "schema 確認して"ccwf validate <file>
"Export as a Claude Skill / agent file", "skills 化して"ccwf export <file> (default agent)
"Convert for Cursor / Codex / Gemini …"ccwf export <file> --agent <name>
"Run this workflow", "動かして", "実行して"ccwf run <file> --launch
"Edit the canvas without VSCode", "editor を browser で開いて"ccwf canvas <file> (mention experimental)
"Let an MCP client edit this workflow"ccwf mcp --file <file> and configure .mcp.json
"Install the ccwf skill / teach Claude about ccwf"ccwf install-skills [--project]
"Update / refresh the ccwf skill"ccwf uninstall-skills && ccwf install-skills
"Remove the ccwf skill / cleanup"ccwf uninstall-skills [--project]

Tips & gotchas

  • ccwf preview URLs include a per-session UUID so two concurrent preview sessions don't collide. The server itself only binds to the loopback interface (127.0.0.1) by default, so external machines can't reach it; the UUID is a path key, not a credential.
  • Auto-shutdown: preview and canvas shut themselves down 30 seconds after the last viewer tab closes. The countdown only starts once at least one viewer has connected, so a preview that nobody opens stays up. Use --keep-alive for multi-tab or LAN scenarios.
  • ccwf run --launch requires claude on PATH. If it's missing, the command warns and exits cleanly after writing the files — that's not an error condition.
  • ccwf canvas is experimental and missing Slack / Claude API / MCP / external-IDE export. If the user needs any of those, fall back to the VSCode extension.
  • Workflow file location: when the user doesn't specify a path, look first under .vscode/workflows/*.json from the workspace root. If multiple workflows exist, list them and ask.
  • Validation before execution: if the workflow is hand-edited or AI-authored in the same session, run ccwf validate before ccwf run / ccwf export to catch shape errors early.
  • .claude/commands/ vs .claude/skills/: Claude Code folded commands/ into skills/. ccwf export --agent claude-code writes to the new path (.claude/skills/<workflow>/SKILL.md). Existing .claude/commands/<workflow>.md files are left alone — the user may want to delete them manually.

Related interfaces

ccwf is one of three entry points to the same workflow JSON; if the user already has the VSCode extension installed or wants AI-driven editing, suggest the appropriate sibling:

  • cc-wf-studio VSCode extension — visual canvas + Slack share + in-canvas AI editing.
  • @cc-wf-studio/mcp stdio bin (ccwf-mcp or ccwf mcp) — let an external AI client read and edit workflows through MCP tools.

See the monorepo README at https://github.com/breaking-brake/cc-wf-studio for the full picture.

Frequently asked questions

What does the Ccwf Cli AI skill do?

Use the `ccwf` CLI (from @cc-wf-studio/cli) to render, validate, preview, export, or run cc-wf-studio workflow JSON files from the terminal. Apply whenever the user mentions viewing, visualizing, checking, executing, or converting a workflow under `.vscode/workflows/` (or any `*workflow*.json`), wants a Mermaid diagram of a workflow, asks to "see" / "preview" / "open" a workflow, or wants to run a workflow as a Claude Code Skill without opening VSCode.

Why use Ccwf Cli on TypingMind?

Because you install it once and use it with any model. Ccwf Cli 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 Ccwf Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/breaking-brake/cc-wf-studio/tree/main/packages/cli/skills/ccwf-cli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ccwf Cli?

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 Ccwf Cli?

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

Is the Ccwf Cli AI skill free?

It is published on GitHub by breaking-brake. Check the repository for licensing terms. 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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