Convert a document to clean markdown suitable for chunking and embedding in a RAG pipeline.
How this skill is triggered — by the user, by Claude, or both
Slash command
/spreadsheet-generation:convert-document-for-rag-ingestionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
AI teams building retrieval-augmented generation pipelines use this recipe to convert source documents into clean markdown that chunks well and produces high-quality embeddings.
AI teams building retrieval-augmented generation pipelines use this recipe to convert source documents into clean markdown that chunks well and produces high-quality embeddings.
Document to Markdown (1 credit per page)
You need an Iteration Layer API key. Get one at platform.iterationlayer.com during the 7-day trial.
For full integration guidance (SDKs, auth, MCP, error handling), see the Iteration Layer Integration Guide.
curl -X POST https://api.iterationlayer.com/document-to-markdown/v1/convert \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"file": {
"type": "url",
"name": "product-manual.pdf",
"url": "https://example.com/docs/product-manual-v3.pdf"
}
}'
import { IterationLayer } from "iterationlayer";
const client = new IterationLayer({ apiKey: "YOUR_API_KEY" });
const result = await client.convertDocumentToMarkdown({
file: {
type: "url",
name: "product-manual.pdf",
url: "https://example.com/docs/product-manual-v3.pdf",
},
});
// Split markdown into chunks at heading boundaries
const chunks = result.markdown.split(/(?=^## )/m);
import re
from iterationlayer import IterationLayer
client = IterationLayer(api_key="YOUR_API_KEY")
result = client.convert_document_to_markdown(
file={
"type": "url",
"name": "product-manual.pdf",
"url": "https://example.com/docs/product-manual-v3.pdf",
}
)
# Split markdown into chunks at heading boundaries
chunks = re.split(r"(?=^## )", result["markdown"], flags=re.MULTILINE)
import (
"strings"
il "github.com/iterationlayer/sdk-go"
)
client := il.NewClient("YOUR_API_KEY")
result, err := client.ConvertDocumentToMarkdown(il.ConvertDocumentToMarkdownRequest{
File: il.FileInput{Type: "url", Name: "product-manual.pdf", Url: "https://example.com/docs/product-manual-v3.pdf"},
})
// Split markdown into chunks at heading boundaries
chunks := strings.Split(result.Markdown, "\n## ")
{
"name": "Convert Document for RAG Ingestion",
"nodes": [
{
"parameters": {
"content": "## Convert Document for RAG Ingestion
AI teams building retrieval-augmented generation pipelines use this recipe to convert source documents into clean markdown that chunks well and produces high-quality embeddings.
**Note:** This workflow uses the Iteration Layer community node (`n8n-nodes-iterationlayer`). Install it via Settings > Community Nodes on self-hosted n8n, or add it directly on n8n Cloud with Verified Community Nodes enabled.",
"height": 280,
"width": 500,
"color": 2
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
200,
40
],
"id": "4dce764c-c876-46e6-b345-073db354eff4",
"name": "Overview"
},
{
"parameters": {
"content": "### Step 1: Convert Document to Markdown
Resource: **Document to Markdown**
Configure the Document to Markdown parameters below, then connect your credentials.",
"height": 160,
"width": 300,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
475,
100
],
"id": "b8aee214-9501-4c49-b56a-5f00a2bb2f41",
"name": "Step 1 Note"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
250,
300
],
"id": "641f3a90-4201-4e73-8faa-526100f62954",
"name": "Manual Trigger"
},
{
"parameters": {
"resource": "documentToMarkdown",
"fileInputMode": "url",
"fileName": "product-manual.pdf",
"fileUrl": "https://example.com/docs/product-manual-v3.pdf"
},
"type": "n8n-nodes-iterationlayer.iterationLayer",
"typeVersion": 1,
"position": [
500,
300
],
"id": "2da87e7d-179a-453c-b7ab-2175662c07b6",
"name": "Convert Document to Markdown",
"credentials": {
"iterationLayerApi": {
"id": "1",
"name": "Iteration Layer API"
}
}
}
],
"connections": {
"Manual Trigger": {
"main": [
[
{
"node": "Convert Document to Markdown",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
Convert the document at [file URL] to markdown for RAG ingestion. Use the convert_document_to_markdown tool with the file URL.
{
"success": true,
"data": {
"name": "product-manual.pdf",
"mime_type": "application/pdf",
"markdown": "# Product Manual v3\n\n## Installation\n\nDownload the latest release from the releases page. Unpack the archive and run the installer.\n\n### System Requirements\n\n| Component | Minimum | Recommended |\n|---|---|---|\n| CPU | 2 cores | 4 cores |\n| RAM | 4 GB | 8 GB |\n| Disk | 2 GB | 10 GB |\n\n## Configuration\n\nThe configuration file is located at `/etc/app/config.yaml`. The following options are available:\n\n- **port** — HTTP server port (default: 8080)\n- **log_level** — Logging verbosity: debug, info, warn, error\n- **max_connections** — Maximum concurrent connections (default: 100)\n\n## API Reference\n\n### Authentication\n\nAll API calls require a Bearer token in the Authorization header.\n\n### Endpoints\n\n| Method | Path | Description |\n|---|---|---|\n| GET | /health | Health check |\n| POST | /process | Submit a processing job |\n| GET | /jobs/:id | Get job status |"
}
}
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
npx claudepluginhub iterationlayer/skills --plugin spreadsheet-generation