deepread
AI-native OCR platform that turns documents into high-accuracy data in minutes. Using multi-model consensus, DeepRead achieves 95%+ accuracy and flags only uncertain fields for review—reducing manual work from 100% to 5-10%. Zero prompt engineering required.
What this skill does
# DeepRead - Production OCR API
DeepRead is an AI-native OCR platform that turns documents into high-accuracy data in minutes. Using multi-model consensus, DeepRead achieves 95%+ accuracy and flags only uncertain fields for review—reducing manual work from 100% to 5-10%. Zero prompt engineering required.
## What This Skill Does
DeepRead is a production-grade document processing API that gives you high-accuracy structured data output in minutes with human review flagging so manual review is limited to the flagged exceptions
**Core Features:**
- **Text Extraction**: Convert PDFs and images to clean markdown
- **Structured Data**: Extract JSON fields with confidence scores
- **Quality Flags**: Human Review tagging for uncertain fields (`hil_flag`)
- **Multi-Pass Processing**: Multiple validation passes for maximum accuracy
- **Multi-Model Consensus**: Cross-validation between models for reliability
- **Free Tier**: 2,000 pages/month (no credit card required)
## Setup
### 1. Get Your API Key
Sign up and create an API key:
```bash
# Visit the dashboard
https://www.deepread.tech/dashboard
# Or use this direct link
https://www.deepread.tech/dashboard/?utm_source=clawdhub
```
Save your API key:
```bash
export DEEPREAD_API_KEY="sk_live_your_key_here"
```
### 2. Clawdbot Configuration (Optional)
Add to your `clawdbot.config.json5`:
```json5
{
skills: {
entries: {
"deepread": {
enabled: true,
apiKey: "sk_live_your_key_here"
}
}
}
}
```
### 3. Process Your First Document
**Option A: With Webhook (Recommended)**
```bash
# Upload PDF with webhook notification
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F "webhook_url=https://your-app.com/webhooks/deepread"
# Returns immediately
{
"id": "550e8400-e29b-41d4-a716-446655440000",
"status": "queued"
}
# Your webhook receives results when processing completes (2-5 minutes)
```
**Option B: Poll for Results**
```bash
# Upload PDF without webhook
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]"
# Returns immediately
{
"id": "550e8400-e29b-41d4-a716-446655440000",
"status": "queued"
}
# Poll until completed
curl https://api.deepread.tech/v1/jobs/550e8400-e29b-41d4-a716-446655440000 \
-H "X-API-Key: $DEEPREAD_API_KEY"
```
## Usage Examples
### Basic OCR (Text Only)
Extract text as clean markdown:
```bash
# With webhook (recommended)
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F "webhook_url=https://your-app.com/webhook"
# OR poll for completion
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]"
# Then poll
curl https://api.deepread.tech/v1/jobs/JOB_ID \
-H "X-API-Key: $DEEPREAD_API_KEY"
```
**Response when completed:**
```json
{
"id": "550e8400-...",
"status": "completed",
"result": {
"text": "# INVOICE\n\n**Vendor:** Acme Corp\n**Total:** $1,250.00..."
}
}
```
### Structured Data Extraction
Extract specific fields with confidence scoring:
```bash
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F 'schema={
"type": "object",
"properties": {
"vendor": {
"type": "string",
"description": "Vendor company name"
},
"total": {
"type": "number",
"description": "Total invoice amount"
},
"invoice_date": {
"type": "string",
"description": "Invoice date in MM/DD/YYYY format"
}
}
}'
```
**Response includes confidence flags:**
```json
{
"status": "completed",
"result": {
"text": "# INVOICE\n\n**Vendor:** Acme Corp...",
"data": {
"vendor": {
"value": "Acme Corp",
"hil_flag": false,
"found_on_page": 1
},
"total": {
"value": 1250.00,
"hil_flag": false,
"found_on_page": 1
},
"invoice_date": {
"value": "2024-10-??",
"hil_flag": true,
"reason": "Date partially obscured",
"found_on_page": 1
}
},
"metadata": {
"fields_requiring_review": 1,
"total_fields": 3,
"review_percentage": 33.3
}
}
}
```
### Complex Schemas (Nested Data)
Extract arrays and nested objects:
```bash
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F 'schema={
"type": "object",
"properties": {
"vendor": {"type": "string"},
"total": {"type": "number"},
"line_items": {
"type": "array",
"items": {
"type": "object",
"properties": {
"description": {"type": "string"},
"quantity": {"type": "number"},
"price": {"type": "number"}
}
}
}
}
}'
```
### Page-by-Page Breakdown
Get per-page OCR results with quality flags:
```bash
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F "include_pages=true"
```
**Response:**
```json
{
"result": {
"text": "Combined text from all pages...",
"pages": [
{
"page_number": 1,
"text": "# Contract Agreement\n\n...",
"hil_flag": false
},
{
"page_number": 2,
"text": "Terms and C??diti??s...",
"hil_flag": true,
"reason": "Multiple unrecognized characters"
}
],
"metadata": {
"pages_requiring_review": 1,
"total_pages": 2
}
}
}
```
## When to Use This Skill
### ✅ Use DeepRead For:
- **Invoice Processing**: Extract vendor, totals, line items
- **Receipt OCR**: Parse merchant, items, totals
- **Contract Analysis**: Extract parties, dates, terms
- **Form Digitization**: Convert paper forms to structured data
- **Document Workflows**: Any process requiring OCR + data extraction
- **Quality-Critical Apps**: When you need to know which extractions are uncertain
### ❌ Don't Use For:
- **Real-time Processing**: Processing takes 2-5 minutes (async workflow)
- **Batch >2,000 pages/month**: Upgrade to PRO or SCALE tier
## How It Works
### Multi-Pass Pipeline
```
PDF → Convert → Rotate Correction → OCR → Multi-Model Validation → Extract → Done
```
The pipeline automatically handles:
- Document rotation and orientation correction
- Multi-pass validation for accuracy
- Cross-model consensus for reliability
- Field-level confidence scoring
### Quality Review (hil_flag)
AI compares extracted text to the original image and sets `hil_flag`:
- **`hil_flag: false`** = Clear, confident extraction → Auto-process
- **`hil_flag: true`** = Uncertain extraction → Human review required
**AI flags extractions when:**
- Text is handwritten, blurry, or low quality
- Multiple possible interpretations exist
- Characters are partially visible or unclear
- Field not found in document
**This is multimodal AI determination, not rule-based.**
## Advanced Features
### 1. Blueprints (Optimized Schemas)
Create reusable, optimized schemas for specific document types:
```bash
# List your blueprints
curl https://api.deepread.tech/v1/blueprints \
-H "X-API-Key: $DEEPREAD_API_KEY"
# Use blueprint instead of inline schema
curl -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "[email protected]" \
-F "blueprint_id=660e8400-e29b-41d4-a716-446655440001"
```
**Benefits:**
- 20-30% accuracy improvement over baseline schemas
- Reusable across similar documents
- Versioned with rollback support
**How to create blueprints:**
```bash
# Create a blueprint from training data
curl -X POST https://api.deepread.tech/v1/optimize \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "utility_invoice",
"description": "Optimized fRelated in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.