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 skills/jacobinwwey/notemdpro/concept-extractornpx skills add Jacobinwwey/notemdpro --skill concept-extractorgit clone --depth 1 https://github.com/Jacobinwwey/notemdproWrote 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/skills/jacobinwwey/notemdpro/concept-extractor)<a href="https://agentmods.dev/skills/jacobinwwey/notemdpro/concept-extractor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/concept-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 | $0.00031 | $0.00698 |
| Opus 5 | $0.00015 | $0.00349 |
| Sonnet 5 | $0.00006 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
concept-extractor scanned grade A with 0 findings 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 4d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NoteMD Pro - Concept Extraction (Agentic Workflow)
Overview
This skill transforms standard Markdown text into an interconnected Knowledge Graph by exhaustively extracting every relevant concept and generating atomic notes with proper backlinks.
⚠️ Critical Rule: Exhaustive Extraction
When executing this skill, DO NOT STOP at 3 to 5 concepts. You must thoroughly scan the entire document and extract every single core knowledge point, technical term, scientific principle, proper noun, and specialized vocabulary word. A comprehensive document (e.g., 1000 words) might yield 15-30 distinct concepts.
Your goal is to be as complete and reasonable as possible, ensuring the end-user's Knowledge Graph is densely populated and highly interconnected.
Step-by-Step Agent Instructions
When the user asks you to extract concepts from a specific Markdown file (or text), follow these steps strictly:
Step 1: Comprehensive Parsing
Read the entire target document thoroughly. Mentally (or in your scratchpad) list every concept following these criteria:
- Scientific & Technical Terms: e.g., "DNA Replication", "Ribosome", "Stellar Nucleosynthesis", "Isotope", "Spectroscopy", "Amino Acid".
- Theories & Principles: e.g., "Statistical Mechanics", "Conservation of Energy".
- Proper Nouns & Tools: e.g., "Tesseract OCR", "Python multiprocessing", "Markdown".
- Abstract Concepts: Limit abstract ideas unless they are central to the domain. Focus on specific noun-phrases.
- Normalization: Always normalize to the singular form (e.g., "Isotope" instead of "Isotopes") and capitalize Title Case (e.g., "DNA Replication").
Step 2: Generate Atomic Concept Notes
For every single concept you identified in Step 1, create a separate atomic note file in the target directory (often a Concepts/ folder or the same folder as the source).
Filename: [Concept Name].md
Content Format:
# [Concept Name]
Brief 1-2 sentence definition or summary of the concept (derive this from your internal knowledge base or the source text context).
## Linked From
- [[Name of the Original Source File]]
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.
- 4d ago First seen · 64 lines · 31 tokens per session scan A 509a11c4e074
concept-extractor is a skill published in the GitHub repository Jacobinwwey/notemdpro (2 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 698 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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