Pipefy Process Impact logo

Pipefy Process Impact

Organization
pipefy
pipefy-process-impact

Use this skill when the user asks whether an automation or AI agent would pay off, where to start automating a process, how much time a change gives back, or for a short internal justification. Answers from the conversation first; when the user names a pipe, reads what already runs there before pointing at a step. Not for diagnosing a whole pipe (pipefy-process-intelligence), designing one (pipefy-process-design), building (pipefy-building), or what a change breaks elsewhere.

Overview

Publisherpipefy
Repositoryai-toolkit
Skill namepipefy-process-impact
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 Impact 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-impact
  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-impact .claude/skills/pipefy-process-impact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipefy Process Impact 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 Impact 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 Impact 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.

Process impact

Help the user find the one step in their process where an automation or an AI agent returns the most time, and show the arithmetic. One hop, one formula, one question per turn. The next hop comes after they have the first number.

Read the MCP reference or CLI reference only when the user names a pipe, and only for the reads listed there.

When to use

  • "Is it worth automating X?" / "Where do I start?" / "What is the ROI?" / "How much time does this give back?"
  • "Help me justify this internally."
  • The user wants more than the Impact: line that pipefy-process-design or pipefy-process-intelligence already emitted.

Not for designing a process (pipefy-process-design), a full pipe diagnosis or implementation (pipefy-process-intelligence), building the step (pipefy-building, then the domain skill), or finding what a change breaks elsewhere.

How to answer

  1. Pick the hop. From the conversation, name the one manual, repetitive step with the best return: a move someone does by hand, a triage, a copy-paste, a chase. With several candidates, take the one with the most cases per week. Do not walk the whole process.
  2. Check coverage when a pipe is named. Run the reads in the reference: get_pipe, get_automations, get_ai_agents, then get_ai_agent for each active agent. If an active automation or agent already does the hop, say so, size that coverage, and move to the next uncovered hop, restating what the agent or rule already does there. Do not propose a second one. With no pipe in view, say coverage was not checked instead of claiming nothing covers the hop. cards_count is what sits in a phase now, not volume or duration, and get_cards selects no dates; when the answer needs time, use the timed card read under Measuring.
  3. Show the arithmetic, with the user's numbers or the gaps named:
text
hours returned per week ≈ minutes per case × cases per week / 60
cost avoided per week ≈ hours returned per week × hourly cost   (only if the user gave an hourly cost)

Never fill a gap yourself. Lead time and time per phase come from the timed card read, weekly volume from the user or a pipe report export (see Measuring), never from cards_count or an undated sample. 4. Name the cheapest step that removes the hop. A traditional automation when the decision reads from fields ("if value > X, move"). An AI agent or AI automation only when the hop needs reading free text, documents, or a conversation. iPaaS when the work lives in another app. One sentence on which and why. Do not stack rule and AI as "do both now"; mention the AI step as optional when a rule leaves part of the hop manual. 5. End with one thing: the missing numbers in one short question, or the next step (the next hop, a measured export, or building it with the domain skill).

Output

In the user's language, four lines, under 150 words unless they asked for a written case:

text
Where it pays: [hop], [what covers it today, if anything]
Arithmetic: [formula with numbers or named gaps] = [hours per week, or the gap]
Step: [automation | AI agent | iPaaS], [one sentence on why this one]
Next: [one question, or one offer]

For a written case, expand each line into a short paragraph and add an "Assumptions" line. Money only with an hourly cost. Lead time only from the timed card read, an export, or a cycle the user stated.

Rules

  • Report time returned to the team on repetitive hops. Asked how many people the change removes, answer in hours and say that decision is theirs.
  • Do not recommend deleting a pipe, cutting seats or credits, or moving work off Pipefy. A quiet pipe is a process with no trigger or a duplicate: ask which, then cover its hops. Delete only when the user asks for that pipe by name, and then move the work into the pipe that remains.
  • Rule before AI. Existing coverage is value already delivered: size it, do not replace it. Credits an active agent spends are the cost of work it already does, not a line to cut.
  • get_ai_agents takes the pipe uuid from get_pipe, not the numeric id; a numeric id returns a permission error. With the uuid and still a permission error, say agent coverage is unknown and do not call the hop manual on that basis.

Measuring

Lead time, age, and time per phase come from a timed card read: one execute_graphql query on the pipe's cards that selects createdAt, finished_at, started_current_phase_at, current_phase_age, and phases_history (firstTimeIn, lastTimeOut, duration in seconds per phase). The document is in the reference. It is a read, runs with no confirmation, and returns 25 cards per page (up to 50); say how many cards the numbers come from, and page with after when the pipe has more. Lead time is finished_at minus createdAt on done cards; time in a phase is the duration of its phases_history entry.

Weekly volume for a whole pipe comes from the user or from a pipe report export (created_at per card), which is the better path when the pipe has more cards than a few pages. The export works on an existing report (get_pipe_reports); when the pipe has none, create_pipe_report is a write, so ask first. Pass the date columns by name from get_pipe_report_columns; pipefy-reports has the export and polling steps. The export ends in an XLSX link, so use it for volume, not for a number the user wants in the chat.

Tools

OperationRead-onlyPurpose
search_pipesYesPipe id when only the name is known
get_pipeYesPhases, cards_count, and the pipe uuid
get_automationsYesHops an active rule already covers (active and actionEnabled)
get_ai_agentsYesAgents on the pipe; active when disabledAt is null
get_ai_agentYesBehaviors with active: true and the phase each acts on
execute_graphqlYes, for a queryTimed card read: the dates and time per phase that get_cards leaves out
get_pipe_reportsYesExisting reports to export for whole-pipe volume
get_pipe_report_columnsYesColumn name values for the export
export_pipe_reportNoStart the export; changes no card
get_pipe_report_exportYesPoll until the file is ready
create_pipe_reportNoOnly when the pipe has no report and the user agreed

See also

  • pipefy-process-design: new process architecture (emits an Impact line).
  • pipefy-process-intelligence: diagnose and improve a pipe (emits an Impact line).
  • pipefy-building: route the chosen step to a domain skill.
  • pipefy-automations, pipefy-ai-agents, pipefy-ipaas: build the step.
  • pipefy-reports: pipe report exports for lead time and weekly volume.

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 Impact AI skill do?

Use this skill when the user asks whether an automation or AI agent would pay off, where to start automating a process, how much time a change gives back, or for a short internal justification. Answers from the conversation first; when the user names a pipe, reads what already runs there before pointing at a step. Not for diagnosing a whole pipe (pipefy-process-intelligence), designing one (pipefy-process-design), building (pipefy-building), or what a change breaks elsewhere.

Why use Pipefy Process Impact on TypingMind?

Because you install it once and use it with any model. Pipefy Process Impact 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 Impact 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-impact. 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 Impact?

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 Impact?

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

Is the Pipefy Process Impact 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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