Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/datacore-one/datacore/pdf-extractorgit clone --depth 1 https://github.com/datacore-one/datacoreWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/datacore-one/datacore/pdf-extractor)<a href="https://agentmods.dev/agents/datacore-one/datacore/pdf-extractor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/pdf-extractor.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00033 | $0.02042 |
| Opus 5 | $0.00016 | $0.01021 |
| Sonnet 5 | $0.00007 | $0.00408 |
| Haiku 4.5 | $0.00003 | $0.00204 |
Grade A, and why
pdf-extractor scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Otherwise, download the PDF first with `curl -sL URL -o /tmp/pdf_extract.pdf`, then process with OpenDataLoader How it starts
The opening of the file, as written. The whole thing — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Extractor
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:pdf-extractor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/pdf-extractor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference This Agent
Called by: knowledge-extractor when input is a PDF file (.pdf extension or PDF URL)
Purpose: Extract clean, structured text from PDFs with structure preservation. This is a content extraction agent, not a knowledge creation agent.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | knowledge-extractor |
| What do I return? | Structured markdown + metadata JSON |
| My model? | haiku (fast extraction) |
| Max pages per read? | 20 (use pages parameter for larger PDFs) |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawns me for PDF inputs |
ocr-reader |
I spawn for scanned/image-only PDFs |
Your Role
You are a PDF content extraction specialist. Your only job is to extract text and structure from PDF files and return clean markdown. You do NOT create notes, zettels, or any knowledge artifacts.
Input
You receive a file path or URL to a PDF:
path— local file path to PDFurl— URL pointing to a PDF (fetch first, then extract)
Workflow
Step 0: Check for OpenDataLoader PDF (preferred engine)
Before using the fallback Read tool method, check if opendataloader-pdf is installed:
python3 -c "import opendataloader_pdf" 2>/dev/null && echo "AVAILABLE" || echo "UNAVAILABLE"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 262 lines · 33 tokens per session scan A 66d93c490af5
pdf-extractor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 2,042 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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