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/qa-extractornpx skills add Jacobinwwey/notemdpro --skill qa-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/qa-extractor)<a href="https://agentmods.dev/skills/jacobinwwey/notemdpro/qa-extractor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/qa-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 | $0.00024 | $0.01068 |
| Opus 5 | $0.00012 | $0.00534 |
| Sonnet 5 | $0.00005 | $0.00214 |
| Haiku 4.5 | $0.00002 | $0.00107 |
Grade A, and why
qa-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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NoteMD Pro - Text Extraction (Q&A)
Overview
This feature extracts specific answers from markdown files based on user-configured questions. It uses LLM to analyze the content and provide precise answers to each question.
When to Use
- Q&A extraction: Get specific answers from long documents
- Data mining: Extract structured information from unstructured text
- Survey analysis: Parse responses to multiple questions
- Research: Pull key findings from papers
Function Call Chain
extractOriginalText (extractOriginalText.ts)
├── read_file(inputPath)
├── settings.extractQuestions (split by newlines)
├── getProviderAndModelForTask('extractOriginalText')
├── IF mergedMode
│ └── callLLM with all questions combined
├── ELSE (individual mode)
│ └── for each question
│ └── callLLM with single question
├── Format results
└── save to output file
├── mkdir_p(outputFolderPath)
└── write_file(outputPath, content)
Key Function (from extractOriginalText.ts)
extractOriginalText
Extracts answers to configured questions from a file.
export async function extractOriginalText(
plugin: NotemdPlugin,
inputPath: string,
reporter: ProgressReporter,
): Promise<void>;
// Outputs: File with Q&A extracted
Settings
Question Configuration
| Setting | Description |
|---|---|
extractQuestions |
Questions to ask (one per line) |
extractOriginalTextMergedMode |
Process all questions in one LLM call |
Provider Settings
| Setting | Description |
|---|---|
extractOriginalTextProvider |
LLM provider for extraction |
extractOriginalTextModel |
Model for extraction |
extractOriginalTextLanguage |
Language for output |
Contextual Context (Macro Settings)
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 · 187 lines · 24 tokens per session scan A 5bf21b7a359b
qa-extractor is a skill published in the GitHub repository Jacobinwwey/notemdpro (2 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 1,068 once invoked, about $0.0001 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.
Other skills, from other repositories
honey-px
Read huge read-only text as PNG pages; big input cut.
lov-any2docx
Convert Markdown documents to professionally styled DOCX (Word) files with python-docx. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, TOC field, watermarks, and page numbers. Supports multiple color themes matching any2pdf (Configurable Academic, Nord, GitHub Light, etc.) and is…
lov-bp-deck
Turn an approved investor BP outline into a clean, professional slide deck with deliberate style selection, real product evidence, charts, branding, PPTX/PDF, and a full-deck preview. Use when the narrative already exists and the user wants PPT production, visual style exploration, slide regeneration, or export.…
lov-document-illustrator
为文档原地插入 AI 配图。读取文档后全局规划插入点,并行生成所有图片, 异步插回原文。支持封面图、自定义比例和三种风格。 Use when: 用户要求为文档/文章/笔记生成配图、插图。 Also trigger when user mentions: 配图、插图、illustration、 generate images、document images、为文章加图。.
lov-fill-form
Fill in Word document form templates (.docx) with user-provided data. Reads a template containing tables with label→value cell pairs, detects all fillable fields, and outputs a completed document. Handles CJK/Latin mixed text with proper font switching. Use this skill when the user wants to fill in a form template…
lov-rich-export
将一份面向用户的内容稳定导出为单文件 HTML、带 assets 的 HTML 文件夹、Markdown、DOCX 与 PDF,并按格式正确处理图片、音频、视频和嵌入式交互内容。用于“富媒体导出”“一键导出”“导出 html/pdf/docx/md”“离线网页”“交付包”“给产品接导出能力”等需求,以及需要为产品建立可复用内容导出管线时。.