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 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/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/qa-extractor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/qa-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 | $0.00024 | $0.01068 |
| Opus 5.5 | $0.00010 | $0.00427 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo 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 16d ago), licensed MIT. It adds 24 tokens to every session and 1,068 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.
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