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Canva Bulk Create

Organization
canva-sdks
canva-bulk-create

Bulk-create Canva designs from tabular data using a brand template with autofill fields, producing one design per row. Use when users say "bulk create designs from this CSV", "generate one design per row", "create a design for each product", "batch generate from a template", or "autofill a template from a spreadsheet". Accepts any tabular data source — uploaded files, pasted tables, JSON, or URLs.

Overview

Publishercanva-sdks
Repositorycanva-skills
Skill namecanva-bulk-create
Stars
141
Forks
33
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by canva-sdks on GitHub. Read the source before you install it.

Installation

Install the Canva Bulk Create 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/canva-sdks/canva-skills/tree/main/plugins/canva/skills/bulk-create
  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/canva-sdks/canva-skills.git /tmp/canva-skills
mkdir -p .claude/skills
cp -r /tmp/canva-skills/plugins/canva/skills/bulk-create .claude/skills/canva-bulk-create
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Canva Bulk Create 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 Canva Bulk Create 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 Canva Bulk Create 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.

Canva Bulk Design Creation

Create one Canva design per row of data by autofilling a brand template with data tags.

Workflow

Step 1: Get the Data

Accept data in any form the user provides and extract a list of rows with named columns:

  • Uploaded file: read the file and extract headers and rows
  • Pasted data: parse markdown tables, tab-separated values, or JSON arrays directly from the chat
  • URL: fetch the resource and parse the response as tabular data

If no data has been provided, ask the user to share it in whatever format is convenient for them.

Once parsed, show the user:

  • Column headers found
  • Number of rows (= number of designs that will be created)
  • A preview of the first few rows

Step 2: Select the Brand Template

If the user hasn't specified a template, search for autofill-capable ones:

Canva:search-brand-templates  dataset=non_empty

Show the results and ask the user to pick one. If they already named or described a template, search with that query.

Step 3: Inspect the Template Schema

Canva:get-brand-template-dataset  template_id=<selected_id>

This returns the field names and types (text, image, chart) that the template expects.

Step 4: Map CSV Columns to Template Fields

Present a mapping table to the user:

Template FieldTypeMatched CSV ColumnNotes
product_nametextProduct Nameauto-matched
pricetextPriceauto-matched
hero_imageimage(none)no match — image fields need asset IDs

Matching rules:

  • Do case-insensitive, fuzzy matching between CSV headers and template field names
  • Text fields can be filled directly from CSV string values
  • Image fields require a Canva asset ID — see Image Field Handling below
  • Chart fields require structured data — treat as advanced and ask the user for clarification

Confirm the mapping with the user before proceeding, especially if there are unmapped fields or ambiguous matches.

Image Field Handling

There are two ways a CSV can supply images for image-type template fields:

Pattern A — CSV has a Canva asset ID column (e.g. image_asset_id): Use the asset ID value directly in the autofill-design call:

json
{ "image": { "type": "image", "asset_id": "<value from CSV column>" } }

Pattern B — CSV has an image URL column (e.g. image_url): URLs cannot be passed directly to autofill-design. Upload each URL to Canva first using Canva:upload-asset-from-url, capture the returned asset ID, then use it in the autofill call. Do the upload immediately before creating that row's design so failures stay localised.

Pattern C — No image column in CSV: Ask the user whether to skip the image field (template default image stays) or abort. Skipping is safe — just omit the image key from the data payload entirely.

Step 5: Bulk Create — One Design per Row

Loop through every CSV row and call Canva:autofill-design for each one. Call them sequentially, not all at once — the API may have rate limits and sequential calls are easier to debug.

For each row:

  1. If the row has an image URL column (Pattern B), first call Canva:upload-asset-from-url to get a Canva asset ID.
  2. Build the data payload from the confirmed field mapping:
json
{
  "text_field_name": { "type": "text", "text": "<value from CSV>" },
  "image_field_name": { "type": "image", "asset_id": "<asset ID>" }
}
  1. Call Canva:autofill-design with the template ID, data payload, and a descriptive title using the row number or a meaningful column value (e.g. "Bulk Design - Row 3 - <identifier>").

Track results as you go:

Row 1 / 50: Created — <design_url>
Row 2 / 50: Created — <design_url>
Row 3 / 50: Failed — <error>

Step 6: Report Results

After all rows are processed, summarise:

  • Total rows attempted
  • Successes (with links)
  • Failures (with row number and reason)

Offer to save a summary CSV with columns: row, status, design_url, error.

Notes

  • Autofill requires a Canva Enterprise plan.
  • For large CSVs (50+ rows), warn the user upfront that this will make N API calls and may take a while. Offer to do a test run on the first 3 rows before proceeding with the full batch.
  • If some rows fail, continue with the rest — don't abort the whole batch.
  • Skip rows where all mapped fields are empty and warn the user about them.
  • If no CSV column matches a required template field, ask the user to confirm which column to use or whether to skip that field.
  • Template field names are case-sensitive in the API — use the exact keys from get-brand-template-dataset.
  • There is no "undo bulk create" — warn the user before starting large runs.
  • Designs created this way are full Canva designs the user can further edit in their account.

Frequently asked questions

What does the Canva Bulk Create AI skill do?

Bulk-create Canva designs from tabular data using a brand template with autofill fields, producing one design per row. Use when users say "bulk create designs from this CSV", "generate one design per row", "create a design for each product", "batch generate from a template", or "autofill a template from a spreadsheet". Accepts any tabular data source — uploaded files, pasted tables, JSON, or URLs.

Why use Canva Bulk Create on TypingMind?

Because you install it once and use it with any model. Canva Bulk Create 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 Canva Bulk Create in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/canva-sdks/canva-skills/tree/main/plugins/canva/skills/bulk-create. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Canva Bulk Create?

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 Canva Bulk Create?

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

Is the Canva Bulk Create AI skill free?

Yes. It is published on GitHub by canva-sdks 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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