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 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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jacobinwwey/notemdpro/concept-extractor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/concept-extractor.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00031 | $0.00698 |
| Opus 5.5 | $0.00012 | $0.00279 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 16d ago), licensed MIT. It adds 31 tokens to every session and 698 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
cost-optimization
Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
linkerd-patterns
Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.