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Pipefy Attachments logo

Pipefy Attachments

Organization
pipefy
pipefy-attachments

Use this skill when the user wants to upload a file to a Pipefy card or table-record attachment field. Covers local and downloaded file sources, plus a presigned-URL handshake for uploading files directly.

Overview

Publisherpipefy
Repositoryai-toolkit
Skill namepipefy-attachments
Stars
52
Forks
24
Bundled files
2
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 Attachments 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-attachments
  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-attachments .claude/skills/pipefy-attachments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipefy Attachments 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 Attachments 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 Attachments 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.

Attachments

MCP clients: read references/mcp.md. CLI users: read references/cli.md. Examples below describe shared operation arguments.

Upload one file at a time to a card or table-record attachment field. The upload goes through Pipefy's presigned URL flow (request URL, S3 PUT, then field update).

When to use

  • The user says "attach this file to card X" or "upload to the documents field on record Y".
  • Pick the source by where the bytes actually are. Use the source rules for your chosen surface.

Do not use this skill for:

  • Reading or listing existing attachments. There is no list/download tool in this skill scope. Cards and table records expose their attachments through the regular card/record fetch tools.
  • Bulk uploads. One file per call; iterate at the agent layer.

Prerequisites

  • A Pipefy organization_id. Find it via get_organization or get_pipe.
  • A target card_id or table_record_id.
  • The attachment field's slug (the human-readable id like document_upload, not the field's uuid). Find it on the card or table record fetch tools.
  • Exactly one file source accessible to the chosen upload surface.

Tools needed

OperationRead-only
upload_attachment_to_cardNo
upload_attachment_to_table_recordNo
create_attachment_presigned_urlNo

Steps

Upload a local file to a card

file_name is inferred from the source basename when omitted, so callers usually only pass the four IDs and the source.

Operation arguments:

text
upload_attachment_to_card(organization_id=42, card_id=1234, field_id=document_upload, file_path=~/report.pdf)

Upload from a URL

text
upload_attachment_to_card(organization_id=42, card_id=1234, field_id=document_upload, file_url=https://example.com/report.pdf)

When the URL has no filename in its path (e.g. .../download?id=1), pass file_name explicitly so Pipefy stores it under a real name.

Upload a local file to a table record

Operation arguments:

text
upload_attachment_to_table_record(organization_id=42, table_record_id=tr-555, field_id=document_upload, file_path=/tmp/export.csv)

Overriding the file name

To store the attachment under a different name than the source basename, pass file_name explicitly. It wins over the inferred basename.

text
upload_attachment_to_card(..., file_url=https://example.com/abc123.pdf, file_name=Invoice-2026.pdf)

Use the presigned handshake

When the one-shot upload cannot reach the bytes or exceeds its 100 MiB cap, use create_attachment_presigned_url and upload from an environment that can read the file:

  1. Call create_attachment_presigned_url with organization_id + file_name (optional content_type / content_length). It returns upload_url (the S3 PUT url), storage_path (the object key), and expires_in_seconds.
  2. From an environment that can reach the upload host, HTTP PUT the file bytes to upload_url within expires_in_seconds (send Content-Type / Content-Length matching what you passed, if any).
  3. Set the attachment field to [storage_path] via update_card_field / set_table_record_field_value. Store storage_path, never a URL — the signed download URL is minted on read.

Because the bytes are handled by the client (step 2), not passed through the tool call, this keeps the model's context clean.

Success criteria

upload_attachment_to_card / upload_attachment_to_table_record (one-shot): the upload result has a download_url (the signed URL Pipefy returns), the inferred or explicit content_type, and the file_size in bytes, and the attachment field on the card or record now lists the file. One call is the whole job.

create_attachment_presigned_url (handshake) is not done at mint: a successful result here only means a target was minted — no field was touched and nothing is attached yet. Done means you completed all three steps: after the client PUTs the bytes to upload_url and you set the field to [storage_path], the attachment field lists the file. Do not treat the mint response (or its premature signed URL) as a finished upload.

Failure modes

Check source accessibility, the 100 MiB one-shot limit, upload expiry and signed headers, and write access to the attachment field. If a signed URL expires, mint a new one; retrying the PUT against the old URL does not refresh it. The presigned handshake requires checking the client PUT and field update separately from minting.

See also

  • pipefy-pipes-and-cards: finding card ids and attachment field slugs.
  • pipefy-database-tables: finding table record ids and field slugs.

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 Attachments AI skill do?

Use this skill when the user wants to upload a file to a Pipefy card or table-record attachment field. Covers local and downloaded file sources, plus a presigned-URL handshake for uploading files directly.

Why use Pipefy Attachments on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pipefy/ai-toolkit/tree/main/plugins/pipefy/skills/pipefy-attachments. 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 Attachments?

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 Attachments?

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

Is the Pipefy Attachments 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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