Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/oaustegard/claude-skills/extracting-keywords)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/extracting-keywords"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/extracting-keywords/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/oaustegard/claude-skills/extracting-keywords"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/extracting-keywords.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 | $0.00075 | $0.03120 |
| Opus 5 | $0.00037 | $0.01560 |
| Sonnet 5 | $0.00015 | $0.00624 |
| Haiku 4.5 | $0.00007 | $0.00312 |
Grade A, and why
extracting-keywords 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 11d 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 — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting Keywords
Extract keywords from text using YAKE (Yet Another Keyword Extractor), an unsupervised statistical keyword extraction algorithm.
Installation
First time only: Install YAKE with optimized dependencies to avoid unnecessary downloads.
cd /home/claude
uv venv yake-venv --system-site-packages
uv pip install yake --python yake-venv/bin/python --no-deps
uv pip install jellyfish segtok regex --python yake-venv/bin/python
This reuses system packages (numpy, networkx) instead of downloading them (~0.08s vs ~5s).
Stopwords Configuration
Built-in YAKE stopwords (34 languages): Use lan="<code>" parameter
- See Parameters section below for all 34 supported language codes
- English (
lan="en") is the default
Custom domain stopwords (bundled in assets/):
AI/ML: stopwords_ai.txt
- English stopwords + 783 AI/ML domain-specific terms (1357 total)
- Filters AI/ML methodology noise (model, training, network, algorithm, parameter)
- Filters ML boilerplate (dataset, baseline, benchmark, experiment, evaluation)
- Filters technical terms (transformer, embedding, attention, optimization, inference)
- Includes full lemmatization (train/trains/trained/training/trainer)
- Use for AI/ML papers, technical reports, machine learning literature
- Performance impact: +4-5% runtime vs English stopwords
Life Sciences: stopwords_ls.txt
- English stopwords + 719 life sciences domain-specific terms (1293 total)
- Filters research methodology noise (study, results, analysis, significant, observed)
- Filters academic boilerplate (paper, manuscript, publication, review, editing)
- Filters statistical terms (correlation, distribution, deviation, variance)
- Filters clinical terms (patient, treatment, diagnosis, symptom, therapy)
- Filters biology/medicine (cell, tissue, protein, gene, organism)
- Includes full lemmatization (analyze/analyzes/analyzed/analyzing/analysis)
- Use for biomedical papers, clinical studies, research articles, scientific literature
- Performance impact: +4-5% runtime vs English stopwords
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 392 lines · 75 tokens per session scan A c6b3166c6e31
extracting-keywords is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 3,120 once invoked, about $0.0004 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 2026-08-30.
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