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Llamaparse

Organization
run-llama
llamaparse

Use this skill when the user asks to parse the content of an unstructured file (PDF, PPTX, DOCX...)

Overview

Publisherrun-llama
Repositoryllamaparse-agent-skills
Skill namellamaparse
Stars
85
Forks
11
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by run-llama on GitHub. Read the source before you install it.

Installation

Install the Llamaparse 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/run-llama/llamaparse-agent-skills/tree/main/skills/llamaparse
  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/run-llama/llamaparse-agent-skills.git /tmp/llamaparse-agent-skills
mkdir -p .claude/skills
cp -r /tmp/llamaparse-agent-skills/skills/llamaparse .claude/skills/llamaparse
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Llamaparse 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 Llamaparse 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 Llamaparse 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.

LlamaParse Skill

Parse unstructured documents (such as PDF, DOCX, PPTX, XLSX) with LlamaParse and extract their contents (text, markdown, images...).

Initial Setup

When this skill is invoked, respond with:

I'm ready to use LlamaParse to parse files. Before we begin, please confirm that:

- `LLAMA_CLOUD_API_KEY` is set as environment variable within the current environment
- `@llamaindex/llama-cloud@latest` is installed and available within the current Node environment

If both of them are set, please provide:

1. One or more files to be parsed
2. Specific parsing options, such as tier, API version, custom prompt, processing options...
3. Any requests you might have regarding the parsed content of the file.

I will produce a Typescript script to run the parsing job and, once you approved its execution, I will report the results back to you based on your request.

Then wait for the user's input.


Step 0 — Install llama-cloud (optional)

If the user does not have the @llamaindex/llama-cloud package installed, add it to the current environment by running:

bash
npm install @llamaindex/llama-cloud@latest

Step 1 — Produce a Typescript Script

Once the user confirms the environment variables are set and provides the necessary details for the parsing job, produce a typescript script.

As a source of truth for the TS script, you can:

  • Refer to the example.ts script, which covers most of the necessary configurations for LlamaParse
  • Refer to the complete LlamaParse Documentation, fetching the https://developers.llamaindex.ai/python/cloud/llamaparse/api-v2-guide/ page.

Scripting Best Practices

Follow these guidelines when generating scripts:

1. Always Use the Top-Level LlamaCloud Client

Use LlamaCloud (the API client) for all parsing operations:

typescript
import LlamaCloud from "@llamaindex/llama-cloud";

// Define a client
const client = new LlamaCloud({
  apiKey: process.env["LLAMA_CLOUD_API_KEY"], // This is the default and can be omitted
});
2. Two-Step Upload → Parse Pattern

Always upload first to get a file ID, then parse using the file ID. Never pass raw file bytes directly to parse().

typescript
import { readFile, writeFile } from "fs/promises";
import { basename } from "path";

// 1. Convert the file path into a File object
const buffer = await readFile(filePath);
const fileName = basename(filePath);
const file = new File([buffer], fileName);
// 2. Upload the file to the cloud
const fileObj = await client.files.create({
  file: file,
  purpose: "parse",
});
// 3. Get the file ID
const fileId = fileObj.id;
// 4. Use the file ID to parse the file
const result = await client.parsing.parse({
  tier: "agentic",
  version: "latest",
  file_id: fileId,
  ...
});

If the user already has a file ID (e.g. from a prior upload), skip the upload step and use it directly.

3. Choose the Right Tier
TierWhen to Use
fastSpeed is the priority; simple documents
cost_effectiveBudget-conscious; straightforward text extraction
agenticComplex layouts, tables, mixed content (default recommendation)
agentic_plusAdvanced analysis, highest accuracy

Default to agentic unless the user specifies otherwise or the document is simple.

4. Always Include the expand Parameter

The expand parameter controls what content is returned. Omitting it returns minimal data. Always specify exactly what you need:

ValueReturns
text_fullPlain text via result.text_full
markdown_fullMarkdown via result.markdown_full
itemsPage-level JSON via result.items.pages
text_content_metadataPer-page text metadata
markdown_content_metadataPer-page markdown metadata
items_content_metadataPer-page items metadata
images_content_metadataImage list with presigned URLs
output_pdf_content_metadataOutput PDF metadata
xlsx_content_metadataExcel-specific metadata

Only request metadata *_content_metadata variants when you need presigned URLs or per-page detail — they increase payload size.

5. Handle None Results Defensively

result.text_full, result.markdown_full, and result.items may be undefined on failure. Always guard against this:

typescript
const text = result.text_full ?? "";
const markdown = result.markdown_full ?? "";
6. Use Structured Options for Advanced Configuration

Group options using the correct nested keys:

typescript
const result = await client.parsing.parse({
  tier: "agentic",
  version: "latest",
  file_id: fileId,
  input_options: {
    presentation: {
      skip_embedded_data: false,
    },
  },
  output_options: {
    images_to_save: ["screenshot"],
    markdown: {
      tables: { output_tables_as_markdown: true },
      annotate_links: true,
    },
  },
  processing_options: {
    specialized_chart_parsing: "agentic",
    ocr_parameters: { languages: ["de", "en"] },
  },
  agentic_options: {
    custom_prompt:
      "Extract text from the provided file and translate it from German to English.",
  },
  expand: [
    "markdown_full",
    "images_content_metadata",
    "markdown_content_metadata",
  ],
});

Use agentic_options.custom_prompt whenever the user wants to guide extraction (translation, summarization, structured extraction, etc.).

7. Downloading Images Requires httpx and Auth

When images_content_metadata is in expand, download images via presigned URLs with Bearer auth:

typescript
if (result.images_content_metadata) {
  for (const image of result.images_content_metadata.images) {
    if (image.presigned_url) {
      const response = await fetch(image.presigned_url, {
        headers: {
          Authorization: `Bearer ${process.env["LLAMA_CLOUD_API_KEY"]}`,
        },
      });
      if (response.ok) {
        const content = await response.bytes();
        await writeFile(image.filename, content);
      }
    }
  }
}
8. Use the Node shebang

Every generated script should include the node shebang:

typescript
#!/usr/bin/env node

Step 2 — Execute the Typescript Script

Once the typescript script has been produced, you should:

  1. Present the script to the user and ask for permissions to run it (depending on the current permissions settings)
  2. Once you obtained permission to run, execute the script
  3. Explore the results based on the user's requests

In order to run typescript scripts, it is highly recommended to use: npx tsx script.ts.

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 Llamaparse AI skill do?

Use this skill when the user asks to parse the content of an unstructured file (PDF, PPTX, DOCX...)

Why use Llamaparse on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/run-llama/llamaparse-agent-skills/tree/main/skills/llamaparse. 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 Llamaparse?

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 Llamaparse?

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

Is the Llamaparse AI skill free?

Yes. It is published on GitHub by run-llama under the MIT 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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