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Pipefy Process Design

Organization
pipefy
pipefy-process-design

Use this skill ONLY when the user explicitly asks for help designing or architecting a process ("help me design", "what's the best structure", "how do I organize this flow?"). For direct execution requests ("create a pipe for X", "build a reimbursement process", or any message with a detailed spec), skip this skill and execute directly using the domain skills (pipes-and-cards, automations, etc.).

Overview

Publisherpipefy
Repositoryai-toolkit
Skill namepipefy-process-design
Stars
52
Forks
24
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by pipefy on GitHub. Read the source before you install it.

Installation

Install the Pipefy Process Design 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.
    https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-process-design
  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/pipefy/ai-toolkit.git /tmp/ai-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-toolkit/plugins/pipefy/skills/pipefy-process-design .claude/skills/pipefy-process-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipefy Process Design 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 Pipefy Process Design 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 Pipefy Process Design 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.

Pipefy Process Design

Read the MCP reference or CLI reference for the surface you are using. Load only the relevant reference.

This skill activates when the user wants consulting help to design a process — not when they want you to build one. If the user already knows what they want (gave you phases, fields, a spec, or a clear use case), do not use this skill. Execute directly.


When to use vs when to execute

SignalResponse
"Help me design a process for X"Use this skill — consulting mode.
"What's the best structure for Y?"Use this skill — architecture guidance.
"Create a pipe for X"Skip this skill — route via pipefy-building, then the domain skill.
User provides phases, fields, or a specSkip this skill — the spec IS the plan. Route via pipefy-building, then build.

Prerequisites

  • Understand the user's industry and use case before advising structure.
  • Always search for existing pipes in the org before recommending a new one (search_pipes).

Discovery phase

  1. Research existing org structure:

    Operation: search_pipes pipe_name="" (empty search returns all visible pipes) Operation: get_organization organization_id=<id>

  2. Understand the process intent:

    • What triggers a new case? (form submission, email, manual)
    • Who are the actors? (submitter, approver, ops team)
    • What are the key decision points? (approve/reject, escalate, auto-close)
    • What data needs to be tracked? (fields per phase)
    • Are there related processes that should be connected? (use relations)
  3. Identify the right Pipefy components:

    NeedComponent
    Workflow stagesPipe + Phases
    Structured data per stagePhase fields
    Reference/lookup dataDatabase table
    Cross-process linkagePipe relation
    Automatic actionsAutomation rule
    AI-driven processingAI automation or AI agent

Design principles

  • Start with the outcome. What does "done" look like for this process?
  • Name phases for states, not actions. "Under Review" not "Review It".
  • Keep the start form minimal. Only ask for data the requester can provide on day 1.
  • Use required fields sparingly. Every required field is a blocker.
  • Design for the exception. Add a "Blocked" or "On Hold" phase for edge cases.
  • Automate the obvious. If a transition always happens under the same condition, it should be an automation, not a manual step.

Common patterns

Linear approval flow

Submission → Under Review → Approved / Rejected → Done

  • Start form: requester fills details.
  • "Under Review" phase: approver field + due date.
  • Automation: notify approver on card creation.

Multi-stage pipeline

Intake → Triage → In Progress → Testing → Deployed

  • Each phase has role-specific fields.
  • Automations advance cards on condition.
  • SLA fields track time-in-phase.

Hub-and-spoke (related processes)

  • Central "intake" pipe connected via pipe relation to domain-specific pipes.
  • Child cards created automatically via automation when parent moves to "Escalated".

Output format

After the design consultation, produce a concise summary:

Process: <Name>
Phases: [list]
Start form fields: [list with types]
Key automations: [list]
Related processes: [list or "none"]
Impact: [one line: time this design returns to the team against a fully manual run of the same flow; if a phase exists only for human triage, what an automation or an AI agent would change there. Do not start a pipe diagnosis.]
Next step: [execute with pipes-and-cards skill? or more questions?]

Keep Impact to one line. Do not invent volume, hourly cost, or lead time; name every missing number and ask for all of them in one question. For a fuller justification, read pipefy-process-impact.


Success criteria

  • User has clarity on phases, fields, and automation triggers before execution starts.
  • No duplicate pipes created (checked via search_pipes).
  • The design is buildable with available operations (no features promised that don't exist).

See also

  • pipefy-building — for execution / build asks, read the router then the domain skill (do not expand this consulting skill into a build playbook).
  • pipefy-pipes-and-cards — execute the design once finalized.
  • pipefy-automations — add automation rules to the new pipe.
  • pipefy-process-intelligence — analyze an existing process for improvement (distinct from designing new).
  • pipefy-process-impact to justify a material change in full; this skill only emits the Impact line.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pipefy Process Design AI skill do?

Use this skill ONLY when the user explicitly asks for help designing or architecting a process ("help me design", "what's the best structure", "how do I organize this flow?"). For direct execution requests ("create a pipe for X", "build a reimbursement process", or any message with a detailed spec), skip this skill and execute directly using the domain skills (pipes-and-cards, automations, etc.).

Why use Pipefy Process Design on TypingMind?

Because you install it once and use it with any model. Pipefy Process Design 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 Pipefy Process Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-process-design. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pipefy Process Design?

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 Pipefy Process Design?

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

Is the Pipefy Process Design AI skill free?

Yes. It is published on GitHub by pipefy under the Apache-2.0 license. 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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