feat(edgeparse): add EdgeParse plugin to marketplace (#152)

Add EdgeParse as a built-in plugin providing Rust-native PDF extraction
that converts PDFs to Markdown/JSON/HTML/text. Includes a skill for
intelligent activation, marketplace registration, and updates
MarkItDown skill to recommend EdgeParse for PDF use cases.

Author: amondnet

.claude-plugin/marketplace.json

@@ -568,6 +568,14 @@
         "ref": "main"
       },
       "homepage": "https://github.com/pleaseai/gemini-plugin-cc/tree/main/plugins/gemini"
+    },
+    {
+      "name": "edgeparse",
+      "description": "Extract structured content from any PDF — headings, tables, paragraphs, lists, bounding boxes — deterministically, without ML dependencies or GPU requirements",
+      "category": "document",
+      "keywords": ["pdf", "extraction", "parsing", "markdown", "rag"],
+      "tags": ["skill", "document"],
+      "source": "./plugins/edgeparse"
     }
   ]
 }

README.md

@@ -282,6 +282,11 @@ ASK (Agent Skills Kit) — AI agent skills for managing library documentation re
 
 **Install:** `/plugin install ask@pleaseai` | **Source:** [pleaseai/ask](https://github.com/pleaseai/ask)
 
+#### EdgeParse
+Extract structured content from any PDF — headings, tables, paragraphs, lists, bounding boxes — deterministically, without ML dependencies or GPU requirements.
+
+**Install:** `/plugin install edgeparse@pleaseai` | **Source:** [plugins/edgeparse](https://github.com/pleaseai/claude-code-plugins/tree/main/plugins/edgeparse)
+
 ## Quick Start
 
 The fastest way to get started — install the marketplace and let the plugin recommender auto-detect what you need:

plugins/edgeparse/.agents/skills/edgeparse/SKILL.md

@@ -0,0 +1,264 @@
+---
+name: edgeparse
+description: Extract structured content from any PDF for AI agents, RAG pipelines, and Copilot Skills. Use this skill whenever the user wants to read, analyze, or reason about a PDF document; needs to feed document content to an LLM; mentions PDF extraction, parsing, or conversion; wants tables, headings, or bounding boxes from a PDF; is building a RAG pipeline; or asks an agent to process a document. Install with: pip install edgeparse
+license: Apache-2.0
+metadata:
+  authors: "EdgeParse Contributors"
+  version: "0.1.0"
+  package: "edgeparse"
+  install_python: "pip install edgeparse"
+  install_node: "npm install edgeparse"
+  source: "raphaelmansuy/edgeparse"
+---
+
+# EdgeParse Skill
+
+Enables AI agents to extract clean, structured content from any PDF — headings, tables, paragraphs, lists, bounding boxes — deterministically, without ML dependencies or GPU requirements.
+
+**Install:** `pip install edgeparse` · **Node.js:** `npm install edgeparse`  
+**Speed:** ~0.023 s/doc (Apple M4 Max, 200-doc benchmark)
+
+---
+
+## When to reach for this skill
+
+Activate when the workflow involves:
+- Reading or analyzing a PDF document on behalf of a user
+- Building a RAG pipeline that ingests PDFs
+- Feeding PDF content to an LLM for summarization, Q&A, or synthesis
+- Extracting tables from financial reports, research papers, or invoices
+- Processing a batch of documents for indexing or search
+- An agent tool that must "open" a PDF and return its contents
+
+---
+
+## Quick start
+
+```python
+import edgeparse
+
+# Convert any PDF to Markdown — best for LLM context windows
+text = edgeparse.convert("report.pdf", format="markdown")
+
+# Convert to JSON with bounding boxes and full structure
+import json
+doc = json.loads(edgeparse.convert("report.pdf", format="json"))
+
+# Plain text (fast, minimal)
+plain = edgeparse.convert("report.pdf", format="text")
+```
+
+The `format` parameter controls output:
+| Value | Best for |
+|-------|----------|
+| `"markdown"` | LLM context — headings, tables, lists in Markdown |
+| `"json"` | Bounding boxes, citations, structured element metadata |
+| `"html"` | Web rendering, semantic HTML5 |
+| `"text"` | Simple full-text search, minimal output |
+
+---
+
+## Core API
+
+### `edgeparse.convert()`
+
+```python
+result: str = edgeparse.convert(
+    input_path,             # str or Path — required
+    format="markdown",      # output format (see table above)
+    pages=None,             # e.g. "1-5" or "1,3,7-10" — specific pages only
+    password=None,          # for password-protected PDFs
+    reading_order="xycut",  # "xycut" (spatial sort, default) or "off"
+    table_method="default", # "default" (ruling-line) or "cluster" (borderless)
+    image_output="off",     # "off", "embedded" (base64), "external" (files)
+)
+```
+
+Returns the extracted content as a **string**. Raises `FileNotFoundError` for missing files and `ValueError` for corrupt PDFs or bad options.
+
+### `edgeparse.convert_file()`
+
+```python
+out_path: str = edgeparse.convert_file(
+    input_path,
+    output_dir="output",    # write output file to this directory
+    format="markdown",
+    pages=None,
+    password=None,
+)
+```
+
+Writes the output file and returns its path.
+
+---
+
+## Common patterns
+
+### Feed a PDF to an LLM
+
+```python
+import edgeparse
+import anthropic
+
+doc = edgeparse.convert("report.pdf", format="markdown")
+
+client = anthropic.Anthropic()
+response = client.messages.create(
+    model="claude-opus-4-5",
+    max_tokens=4096,
+    messages=[{
+        "role": "user",
+        "content": f"Analyze this document and summarize the key findings:\n\n{doc}"
+    }]
+)
+print(response.content[0].text)
+```
+
+### RAG pipeline — chunk with metadata
+
+```python
+import edgeparse, json
+
+raw = edgeparse.convert("paper.pdf", format="json")
+doc = json.loads(raw)
+
+chunks = []
+for el in doc["elements"]:
+    if el["type"] in ("paragraph", "heading", "table"):
+        chunks.append({
+            "text": el["text"],
+            "metadata": {
+                "page":    el["page_number"],
+                "type":    el["type"],
+                "bbox":    el["bounding_box"],   # for citation highlights
+                "order":   el["reading_order"],
+            }
+        })
+
+# Now embed chunks["text"] and store chunks["metadata"] in your vector store
+```
+
+### Batch processing
+
+```python
+import edgeparse
+from pathlib import Path
+
+results = {}
+for pdf in Path("documents/").glob("*.pdf"):
+    try:
+        results[pdf.name] = edgeparse.convert(str(pdf), format="markdown")
+    except Exception as e:
+        results[pdf.name] = f"ERROR: {e}"
+```
+
+### Extract specific pages only
+
+```python
+# Pages 1–5
+text = edgeparse.convert("report.pdf", format="markdown", pages="1-5")
+
+# Non-contiguous pages
+text = edgeparse.convert("report.pdf", format="markdown", pages="1,3,7-10")
+```
+
+### Borderless table extraction
+
+Many financial reports and invoices use tables without ruling lines.
+Use `table_method="cluster"` to handle them:
+
+```python
+text = edgeparse.convert(
+    "earnings.pdf",
+    format="markdown",
+    table_method="cluster"   # spatial clustering for borderless tables
+)
+```
+
+### Password-protected PDF
+
+```python
+text = edgeparse.convert("secure.pdf", format="markdown", password="mypassword")
+```
+
+---
+
+## Node.js usage
+
+```js
+import { convert } from 'edgeparse';
+
+const markdown = convert('report.pdf', { format: 'markdown' });
+const json     = convert('report.pdf', { format: 'json' });
+
+// With options
+const result = convert('report.pdf', {
+    format:       'markdown',
+    pages:        '1-5',
+    readingOrder: 'xycut',
+    tableMethod:  'cluster',
+});
+```
+
+---
+
+## JSON output schema
+
+When `format="json"`, the output is a JSON string with shape:
+
+```json
+{
+  "page_count": 10,
+  "title": "Document Title",
+  "elements": [
+    {
+      "type": "heading",
+      "level": 1,
+      "text": "Introduction",
+      "page_number": 1,
+      "reading_order": 0,
+      "bounding_box": { "x0": 72, "y0": 144, "x1": 540, "y1": 180 }
+    },
+    {
+      "type": "table",
+      "text": "| Col A | Col B |\n|-------|-------|\n| val1  | val2  |",
+      "page_number": 2,
+      "bounding_box": { "x0": 72, "y0": 200, "x1": 540, "y1": 350 }
+    },
+    {
+      "type": "paragraph",
+      "text": "This is body text...",
+      "page_number": 1,
+      "reading_order": 2,
+      "bounding_box": { "x0": 72, "y0": 190, "x1": 540, "y1": 220 }
+    }
+  ]
+}
+```
+
+Element `type` values: `heading`, `paragraph`, `table`, `list`, `list_item`, `figure`, `caption`, `header`, `footer`.
+
+---
+
+## Error handling
+
+```python
+import edgeparse
+
+try:
+    text = edgeparse.convert("report.pdf", format="markdown")
+except FileNotFoundError:
+    # PDF file not found — check the path
+    pass
+except ValueError as e:
+    # Invalid format, corrupt PDF, wrong password, or bad page range
+    print(f"Extraction failed: {e}")
+```
+
+---
+
+## For more detail
+
+Read these reference files when the SKILL.md body isn't enough:
+- `references/api.md` — complete Python + Node.js API with all parameters and types
+- `references/patterns.md` — LangChain, LlamaIndex, MCP tool, CrewAI, and async batch patterns